Every year, nearly 7 billion prescriptions are dispensed in the United States. Of the patients prescribed specialty medicines, nearly 30% never start therapy and 60-70% abandon it within the first year. The drug works, but the patient just never gets to take it. That failure is operational.

Biopharma has built sophisticated commercial organizations to support these patients: field access teams, patient services teams, marketing. But the work is performed across spreadsheets, legacy CRMs built for sales reps, hub portals, and email threads. The patient journey gets fractured across a dozen tools that don’t talk to each other.

Enter Courier Health. Danny Sigurdson spent years leading sales at ActionIQ, where he watched enterprise after enterprise struggle to unify fragmented customer data. He realized biopharma had the same problem with dramatically higher stakes: when a biopharma company loses track of a patient, that patient might lose access to a therapy they need to survive.

Courier Health is the first solution designed entirely around the patient. It ingests the data streams that matter (specialty pharmacy feeds, hub data, claims, EHR signals, field activity), stitches them into a single patient record, surfaces intelligence on at-risk patients, and orchestrates compliant action across teams.

The results: The company quadrupled the number of therapies on its platform in 2025. Customers are achieving an over 15% increase in patient starts, 10-12% increase in 6-month persistency rates, and 5-7x increase in team productivity. At Denali Therapeutics, the platform shifted the focus from rigid process design to patient outcomes. At SetPoint Medical, their VP of Market Access called it “the next generation of life science patient navigation technology.”

Today we’re announcing that Oak HC/FT is leading Courier Health’s $50M Series B. We’re tripling down on our investment, having led their seed round in 2021. We’ve long believed at Work-Bench that the best enterprise software companies reveal a better operating model that was always theoretically possible but practically unreachable without the right infrastructure. Courier Health is doing exactly that. The patient journey was always supposed to be coordinated and patient-centric. It just never had the software to make that real.

Danny and the Courier Health team are building something that quite literally changes and saves lives. We couldn’t be more proud to continue backing them.

Read More Here

Before committing to the startup journey, the most valuable work a founder can do is understanding a market - how it works, who the stakeholders are, and where real problems exist.

Historically, mapping a market, understanding its stakeholders, and identifying real pain could take weeks of manual research. Today, AI can dramatically accelerate the process - if you know how to use them properly.

Join us for a Work-Bench Masterclass with our Venture Partner Diego Oppenheimer, where he’ll walk through a practical framework for using AI to research markets and evaluate startup ideas. Diego will also share the exact AI prompts that he uses for market research.

In this session, Diego will cover:

• How to choose the right industry “haystack” to explore

• How to use AI to map a market and identify stakeholders

• How to uncover real problems worth solving

• How to test early solution hypotheses before writing code

This session is designed for:

• Operators exploring startup ideas

• Builders researching potential companies

• Founders validating a new market

• Anyone curious about structured startup discovery

  ---

🗓️ Wednesday, March 18th
⏰ 1:30-2:30PM ET
💻 On Zoom

Register

A Bit About Diego Oppenheimer

Diego previously founded Algorithmia, an AI infrastructure company acquired by DataRobot.

He is now a Venture Partner at Work-Bench and an Executive Fellow at Stanford GSB, where he assists in teaching AI for Research-Driven Inspiration, a course focused on systematic startup discovery.

Developers spin up Claude Code instances like they’re opening new browser tabs. Marketing teams write everything in ChatGPT first. If you don’t know Cursor, you’re behind. And these are just a handful of examples across the enterprise.

The data backs it up: 57% of companies have AI agents in production, according to G2’s 2025 survey. Another 22% are piloting them, while the rest are still in the experimental phases before that.

But it’s still not that simple: 65% of leaders cite agentic system complexity as the top barrier (for two consecutive quarters), according to KPMG’s Q4 2025 AI Pulse Survey. Gartner thinks 40% of agentic AI projects will get canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.

While enterprises have no shortage of ambition, they lack the right infrastructure.

We’ve been calling this the agent runtime: the infrastructure layer being built to safely execute, constrain, observe, and improve agent work at scale. Here’s how we’re thinking about it.

Why Now

If agents have been technically possible for a while, what changed in 2025 to make the infrastructure gap undeniable?

In short, agents graduated from demos to daily drivers:

• Claude Code went from research preview to general availability.

• Cursor documented agents operating autonomously for close to a week, writing over a million lines of code across 1,000 files, to create a web browser from scratch.

• OpenAI’s Codex sustained 24+ hour autonomous sessions.

• Gartner predicts 40% of enterprise applications will embed task-specific AI agents by the end of 2026, which is up from less than 5% in 2025.

Our use of agents has increased dramatically without proportional adaptations in the infrastructure needed to support it.

Then there’s the cost. A mid-sized e-commerce firm building an agentic supply chain optimizer, for example, saw infrastructure costs jump from $5K/month in prototyping to $50K/month in staging (a 10x increase), due to unoptimized RAG queries fetching far more context than needed. Budget constraints aren’t intuitive to an LLM; they need to be enforced architecturally.

And on top of all that, security incidents make the risks concrete. CyberArk Labs demonstrated how a financial services company’s procurement agent was weaponized through a malicious prompt embedded in a shipping address field, where the agent used its invoicing tool access to compromise sensitive data. Palo Alto Networks’ 2026 predictions put it bluntly: with “a single well-crafted prompt injection or by exploiting a ‘tool misuse’ vulnerability,” adversaries now “have an autonomous insider at their command.”

The fundamental issue is that agents are nondeterministic. Both the input and output space for an LLM are infinite, and the same input doesn’t necessarily guarantee an identical output. They execute untrusted code determined at runtime, so there’s no build-deploy-test cycle. Attribution is blurry when agents complete tasks across systems with multiple tools.

And they can fail invisibly: An agent that confidently returns a wrong answer, loses context mid-conversation, or takes an action misaligned with user intent doesn’t throw errors because from the infrastructure perspective, everything worked.

Early adoption comes with risk. Agents demand a new runtime to make that risk manageable.

The 4 Pillars of the Agent Runtime Stack

• Execute. The systems (sandboxes, skills) that enable agents to take safely action.

• Constrain. The guardrails (permissions, identity) that keep agents operating within defined boundaries.

• Observe. The visibility and monitoring infrastructure that tracks what agents are doing.

• Improve. The feedback loops and learning mechanisms that make agents better over time.

Continue reading on our blog.

We are thrilled to announce that Work-Bench led Flock’s $6M Seed round. The round includes participation from January Ventures, Red Bike Capital, Outlander VC, and strategic angels.

The round comes as retail brands face a fundamental shift: ecommerce success now depends on delivering personalized, high-quality visual storytelling at scale, something traditional photoshoots and first-generation AI tools were never designed to support.

Traditional photo shoots are slow, expensive, and fundamentally limit how much content brands can produce. They were built for seasonal campaigns, not continuous content generation across massive catalogs.

At the same time, generic AI image tools have failed to earn enterprise trust. While they can generate images quickly, they lack product accuracy, brand safety, and the workflow integration creative teams require. For large brands, “almost right” isn’t good enough.

That’s where Flock comes in.

Flock is building the AI-native content engine for retail, replacing traditional photo shoots with brand-accurate imagery and video at scale, while fitting directly into how creative and merchandising teams already work.

Acting as a creative team’s co-pilot, Flock allows brands to generate tailored, brand-accurate product imagery for every customer, channel, and context, without expanding headcount or budgets. Using reinforcement learning and brand-specific AI models, Flock enables one-to-one ecommerce experiences that were previously impossible, while freeing creative teams to focus on the tasks that move the needle.

Why Now

Retail content is hitting a structural breaking point.

Product catalogs are expanding. Channels are fragmenting. Personalization expectations are rising. Yet content production capacity remains constrained by cost, timelines, and headcount.

Retailers are under margin pressure and being asked to do more with less. They need higher content velocity without sacrificing brand integrity or increasing spend.

First-generation AI image tools educated the market on what’s possible - but also revealed their limits. Generic outputs, brand inconsistency, and lack of workflow integration left enterprise retailers searching for vertical, production-grade solutions.

Creative teams are willing to adopt AI when it doesn’t ask them to compromise quality, control, or process.

This convergence of pressure, maturity, and readiness is what makes this moment different.

The Product

From photo shoots to content infrastructure.

Flock is not an AI image studio or a standalone creative tool. It is a full-stack content system designed to replace the parts of retail content workflows that no longer scale.

At the core of the platform is Flock’s brand DNA dataset.

Every creative approval feeds into more than 200 brand-specific parameters that encode a company’s visual language, style rules, and constraints. Approved assets are pushed directly back into existing DAM and ecommerce systems.

Unlike static AI tools, Flock uses reinforcement learning to learn from creative team feedback, approval patterns, and real customer conversion data. Over time, this creates a compounding advantage where output quality, brand alignment, and performance improve with every asset generated.

This allows teams to:

• Generate brand-accurate ecommerce, editorial, and video content at scale

• Maintain control over every element lighting, pose, background, fabric texture, and detail down to every stitch line and zipper

• Reduce production timelines from months to days without changing creative workflows

• Produce inclusive, realistic imagery across skin tones, body types, ages, and textures

By turning content creation into a repeatable production pipeline, Flock enables a shift from one-off assets to continuous content infrastructure.

Traction

From pilots to real production impact.

Flock is already working with billion-dollar fashion and beauty brands with rapid expansion across product catalogs and channels.

What stood out to us is how quickly the platform moves from experimentation to impact.

Customers report up to 90% cost savings compared to traditional photo shoots, alongside 30%+ conversion lift driven by better coverage, faster iteration, and more relevant content.

Because Flock plugs directly into existing ecommerce and DAM workflows, teams can adopt it without re-architecting how they work. That combination of immediate ROI and deep workflow fit is what gives Flock a path to becoming durable infrastructure rather than an optional tool.

The Team

Built by insiders solving their own problem.

CEO Vita Mallela spent years leading merchandising and creative operations at Bloomingdale’s and Jet.com/Walmart, managing multi-billion-dollar P&Ls and experiencing firsthand how slow and expensive traditional photo shoots can be.

She’s building the platform she wished she had.

CTO Malavika Reddy brings over a decade of AI and computer vision experience from Microsoft and Brightcom, where she built and scaled production machine learning systems.

Together, they bring a rare combination of operator empathy and technical rigor. They understand exactly what creative and merchandising teams need because they’ve lived the problem themselves.

Why Work-Bench Is Excited

This is the kind of investment we get excited about: a team solving a mission-critical problem they’ve lived firsthand that sits directly on the path to revenue and brand trust.

Across diligence, the feedback from large retailers was remarkably consistent. A multi-brand apparel company told us the results versus their prior solution were “night and day.” A global luxury retailer that evaluated nearly a dozen vendors said Flock was “the only platform we tested where the images actually looked real.”

After testing dozens of pilots, and in side-by-side evaluations, teams were especially struck by how consistently Flock handled the details that broke other tools. Competing solutions struggled with prints, adding buttons that didn’t exist or losing pockets entirely. Flock was the only platform customers tested that reliably produced correct creases, folds, and shadows, handled complex patterns and prints, and maintained model consistency without creating exact duplicates. In one case, the output quality was so realistic that a marketing team initially didn’t believe the images were AI-generated.

That quality is already translating into real production impact. At one enterprise retailer, teams generated “5,000-6,000 images in a single month” to launch a new shopping experience, and saw a “roughly 3 percentage point lift in conversion since launch.” Another large brand told us they are now replacing meaningful portions of traditional photography rather than treating AI as a side experiment.

Execution matters just as much as output. Multiple customers emphasized that Flock works within existing creative and merchandising workflows and comes with hands-on support when edge cases arise. As one team put it, “it’s not just a tool, we need someone who understands our workflows and can troubleshoot in real time.”

With a natural path from imagery into video, personalization, and broader commerce workflows, we believe Flock is becoming foundational infrastructure for modern retail.

What’s Next

If you’re excited about building AI-native infrastructure for modern retail, now is the time to join. Reach out to the Flock team to learn more about open roles and how you can be part of what they’re building.

We’re excited to support Flock as they grow the team behind the future of retail content.

Welcome to the Work-Bench family, Flock! You can check out their press coverage in Vogue Business and PR Newswire.

This raise comes less than a year after Goodfire’s Series A and will fuel frontier research, the next generation of the core product, and expanded partnerships across AI agents and the life sciences.

Our investment in Goodfire at its heart is rooted in community & long-term conviction. We’ve known Eric Ho, co-founder and CEO, for more than eight years, first backing him at his prior company, RippleMatch, where he was a co-founder and CTO. It was there that Eric met Daniel Balsam, now his co-founder at Goodfire. In addition to Eric and Daniel, Goodfire rounded out the founding team by adding Tom McGrath a brilliant research scientist and cofounder of Google DeepMind’s Interpretability team.

Everything that the Goodfire team has built in such a short time is remarkable. In the earliest days, Eric and Danny from our team were whiteboarding ideas, exploring opportunities, and debating what the future of interpretability, AI infrastructure, and agents could look like. Watching those early conversations evolve into what Goodfire is today has been incredibly rewarding.

Less than two years later, Goodfire has assembled a world-class team drawn from frontier labs and top research universities, and partnered with industry leaders including Arc Institute, Mayo Clinic, and Microsoft. Along the way, they’ve developed novel techniques to decompose model internals, cut LLM hallucinations in half using interpretability-informed training, and even identified a new class of Alzheimer’s biomarkers by reverse-engineering a biological model.

This round is a major milestone, but still just one step on a much longer journey. We’re incredibly proud to be early partners and can’t wait to see what the team builds next.

You can read more about Goodfire’s Series B on their blog, as well as in the accompanying press coverage from Bloomberg News.

Building the AI System of Record for In-House Legal Teams

We are thrilled to announce that we led Chamelio’s $5.7M Seed round, following their pre-seed led by Emerge, bringing their total funding raised to $10M.

In-house legal teams sit at the center of modern businesses. They’re asked to accelerate revenue, manage risk, and scale operations while operating across thousands of contracts, policies, and workflows that were never designed to be analyzed, connected, or automated.

Still, most legal teams rely on a fragmented tech stack that includes legacy CLMs that behave like storage systems, generic AI tools that lack context, and manual workflows stitched together with spreadsheets and email.

That’s where Chamelio comes in.

Chamelio is building the AI context layer for in-house legal teams with a platform that transforms contracts, policies, and negotiation history into a living intelligence layer to power end-to-end legal workflows.

Why Now

Legal teams are drowning in data but starved for intelligence.

For the last decade, contract lifecycle management (CLM) has been treated as the core system for legal teams, but in reality, most CLMs are just glorified document repositories. They’re expensive storage layers wrapped in workflow theater.

They store contracts, they don’t understand them, and they certainly don’t learn from them.

At the same time, the rise of generative AI has produced a wave of “legal copilots” that can draft clauses and summarize contracts, but they are fundamentally stateless. They’re disconnected from a company’s actual legal history, negotiation patterns, and risk posture.

The result is a paradox:

• CLMs have data but no intelligence.

• AI tools have intelligence but no data.

• Legal teams are stuck stitching together brittle workflows in between.

That model is breaking.

Modern in-house legal teams are under unprecedented pressure. Contract volume is exploding. Deal cycles are accelerating. The business expects legal not just to reduce risk, but to enable growth, without adding headcount.

For the first time, AI is powerful enough to change this. But only if it’s embedded inside a system that understands a company’s legal memory: what it has signed, how it negotiates, where it bends, and where it doesn’t.

That’s the shift Chamelio is betting on.

Not another CLM. Not another chatbot. But a new category: an AI-native legal operating system that turns contracts from static documents into a living intelligence layer.

We believe this is the moment the CLM category begins to unbundle - and the legal OS category begins to emerge.

The Product

From document storage to legal intelligence.

Chamelio addresses the full spectrum of legal work from intake and drafting to negotiation, signing, and post-signature operations, all while keeping legal firmly in control.

At its core, Chamelio unifies three core capabilities that have historically lived in separate tools:

1) Best-in-class contract redlining.
Chamelio integrates directly into Microsoft Word, enabling legal teams to run playbook-based reviews on complex agreements, refine standards independently, and scale negotiation without adding headcount.

2) Low-risk contract automation.
For simpler agreements like NDAs and vendor contracts, Chamelio’s AI agent drafts addenda, clauses, and revisions faster than generic LLMs while maintaining guardrails that legal teams trust.

3) Stateful legal intelligence.
Beyond redlining, Chamelio allows teams to query their entire contract repository and generate structured outputs: issue trackers, stakeholder summaries, diligence reports, and risk analyses across thousands of documents.

On top of these capabilities, Chamelio automates workflows: routing approvals, flagging renewals, generating agreements, and integrating with the systems legal teams already use - Slack, Teams, Salesforce, and document repositories.

The result is a shift from static document management to a living legal operating system.

Traction

From pilot to essential infrastructure.

Chamelio’s market pull was one of the strongest signals we’ve seen in legal tech.

Today, Chamelio supports ~100 in-house legal teams, including Wiz, Socure, Fiverr, Global-e, Cellebrite, Lightricks, and more.

One example that stood out:

• A company we spoke with ran a 6-month bake-off against 4-5 competitors and selected Chamelio.

• Since adopting the platform, their legal team has driven a ~15% increase in completed contracts with flat turnaround times and no additional headcount.

• The team estimates $10K+ in monthly savings, with a clear path to enterprise-scale ACVs as integrations deepen.

We heard similar stories from other customers: massive repository analyses completed in minutes, due diligence automated across thousands of contracts, and insights that previously required weeks of manual work.

The Team

Deep domain expertise meets AI-native execution.

Chamelio was founded by operators who have lived the realities of legal and enterprise workflows. Alex, Gil, and Gal are not just pedigreed, but hungry. The team ships quickly, internalizes customer feedback, and iterates relentlessly.

One moment that stuck with us: when the team flew in for their final IC pitch, they passed on dinner with us - not because they were tired, but because they were hosting a prospect dinner with 12 legal leaders, including two existing customers, that evening.

A week later, they were back in New York, carving out time for dinner with us between days spent working the floor at a legal tech conference (with Danny rescuing their booth after a last-minute monitor failure).

It was a reminder that great enterprise companies aren’t built in pitch decks. They’re built in rooms with customers.

Why Work-Bench Is Excited

Chamelio is a system of action that we believe defines the next generation of enterprise software:

• Mission-critical workflows: Legal sits on the critical path of revenue, risk, and compliance.

• Broken legacy systems: CLMs were built for document storage, not AI-native workflows.

• A platform wedge: By becoming the context layer for legal knowledge, Chamelio has a path to expand across procurement, diligence, analytics, and cross-functional workflows.

• Real customer ROI: When legal teams can increase throughput without adding headcount, the value proposition becomes undeniable.

We believe Chamelio has the potential to become the definitive context layer for in-house legal operations replacing fragmented tooling with a unified, AI-native platform built for how modern legal teams actually work.

What’s Next

If you’re a GC or legal ops leader still stitching together spreadsheets, legacy CLMs, and generic LLMs to keep up with contract volume, it’s time to see what an AI-native legal platform feels like. Reach out to the Chamelio team to see how they can help!

Welcome to the Work-Bench family, Chamelio 🦎

Congratulations to the entire Chamelio team! You can check out their press coverage in Law360.

Huge congratulations to Robert Ross (aka “Bobby Tables”), Dylan Nielsen, and the entire FireHydrant team on joining Freshworks!

When we invest at seed, we’re not just underwriting an outcome. We’re underwriting people, conviction, and the willingness to endure the full startup sinusoidal journey. FireHydrant was exactly that, from day one.

I still remember first meeting Robert through Namely’s VP Eng in late 2018. One of those introductions where, within minutes, you can tell you’re talking to someone who deeply understands the problem, not just intellectually, but viscerally. The scars from years as an SRE were already there. The obsession was obvious. We quickly pounced to lead FireHydrant’s Seed round in Dec 2018.

As first time technical founders, they made many common mistakes you’d expect. But we were there each step of the way to coach, guide, and support.

We still laugh about the first board meeting 7 years ago. The deck was rough around the edges, to put it kindly, but the thinking underneath it was razor sharp. The product vision, the customer pain, the clarity around why incident response was fundamentally broken. The ingredients were all there. The potential was undeniable.

A tradition started - unintentionally at first, but very deliberately over time. For every single FireHydrant board meeting over the past seven years, I showed up wearing a FireHydrant sweatshirt or t-shirt. No matter the agenda. No matter the market cycle. No matter how hard the conversation was going to be. It was a small thing, but it symbolized something bigger: belief, alignment, and being all-in alongside the team.

Not long after, Robert gave a dev lunch talk for the Work-Bench community. That was a turning point. You could feel the room light up with founders, operators, and engineers all leaning in. Soon after he co-headlined a NY Enterprise Tech Meetup alongside Alexis Lê-Quôc of Datadog. He didn’t just deliver a product pitch, it was a manifesto. People walked out buzzing, convinced this was a company that had to exist.

From there, it was a blur of milestones and moments that brought me closer to the entire team: speaking at SKOs, sharing the stage at Firehose events, karaoke nights, meals squeezed between meetings, holiday parties, conference hangs, and so much more.

One of my favorite memories over the years was seeing Death Cab for Cutie and The Postal Service with Robert. We both share a fondness for emo music and jumped at the opportunity to see this special 20th anniversary show together at MSG after Robert spoke at our Annual LP meeting. It’s a perfect snapshot of how this relationship always blurred the line between work and life in the best way possible.

Of course, it wasn’t all highs. Scaling a company during ZIRP and the fallout afterwards, there came the whiplash that so many teams endured: growth-at-all-costs…and then the market literally turning upside down a week later. Hard decisions followed. A RIF that no founder ever wants to make. Deeply human moments, handled with care and integrity after lots of planning and deliberation. During those moments you can tell a founder’s true colors, and Robert consistently communicated with transparency and empathy with the board and his employees.

There was the true bet-the-company moment: going all in on Alerting. A bold call. A scary one given the company’s limited engineering capacity. I’ll never forget the late night Zooms and the lunch conversation right before Robert pulled the trigger. He knew it was the correct call and was proven right when competitors started copying the feature and customers started flocking to FireHydrant in droves. That’s how you know you’ve struck a nerve. And then, in the midst of more turbulence, the conviction to buy a competitor, Blameless. Calm, deliberate leadership when it mattered most.

Along the way, we’ve had the privilege of spending real time with so many incredible people:

• Trow, the company’s VP Sales who deftly handled the role of player-coach, closing strategic deals himself while scaling and leveling up his org. I’ll fondly remember our end of quarter recaps over drinks.

• Danielle, building with clarity and strength as CTO while enhancing the company’s enterprise offerings.

• Colin, a FireHydrant OG who was a sales machine during his tenure and always gave me the real talk on deals during our coffee chats in Chicago.

• And Scott, who joined in the earliest days when everything was still fragile and helped secure our first large customers.

These are the moments seed investors actually sign up for.

When we say “we have your back,” we mean it. We’re not here just for the up-and-to-the-right slides. We sign up for the uncertainty, the doubt, the hard calls, and the quiet wins. FireHydrant was the full journey - and then some.

Robert, Dylan, and team: we couldn’t be prouder. You built something real, something durable, something that mattered deeply to customers - and you did it with heart.

We’re so pumped for this next chapter at Freshworks and have no doubt you’re going to make a massive impact.

Cheers to what you built and to everything that’s still ahead. 🍻

We’re thrilled to announce our latest investment as we co-led AIR Platforms’ $6.1M Seed round with Lerer Hippeau. This comes after we previously led their Pre-Seed raise.

We’re excited to partner with AIR Platforms, backing their mission to build the first truly AI-native credit intelligence system - real-time, transparent, and built for the complexity of modern credit markets.

Financial Institutions are making credit decisions in an environment where the volume of available data has grown more than 10x in the last decade, yet most systems haven’t materially evolved. AIR is closing that gap. Their credit intelligence layer increases model transparency, accelerates analysis, and can reduce manual review time by 30–50%.

The market needs infrastructure that enables participants to make faster, more accurate, and more explainable decisions. We expect AIR to become core to how modern institutions assess and price risk.

Why Credit Risk Intelligence Needs a Reckoning - and Why Now

• Credit markets have exploded in size, structure, and complexity. Private credit alone is now a multitrillion-dollar asset class. As public and private credit blur, institutions face a flood of new borrowers, structures, and risk vectors - traditional models weren’t built for this.

• Information overload with no standardized framework for analysis. Firms now juggle financial statements, market data, news, covenants, sector-trends and more - often manually, often inconsistently. That forces slow underwriting cycles, human error, and lack of comparability across portfolios.

• Legacy credit-rating methods are slow, opaque, and static. Ratings from traditional agencies come with lag, limited coverage (especially in private credit), and black-box methodologies. In fast-moving markets, that lack of transparency and agility can be disastrous.

• AI + modern data infrastructure changed the math. AIR uses machine-learning and generative-AI to ingest huge volumes of structured and unstructured data - financials, market signals, news, events - and deliver forward-looking, explainable, real-time credit risk scores. That allows firms to act on signal, not lag.

In short: the convergence of a huge, under-served market + data explosion + outdated workflows + AI breakthroughs makes this the moment for AIR.

The Product - Next-Gen Credit Intelligence

AIR isn’t “just another rating agency.” It offers a full-stack, AI-powered credit-intelligence platform that:

• Produces real-time, independent credit ratings and credit risk scores for private and public entities. Scores reflect underlying financials, sector context, event-driven signals, and are updated as new data arrives.

• Delivers transparency: drivers, not black boxes. For each entity, AIR surfaces the core risk factors (leverage, profitability, cash flow, sector-specific inputs), with their relative weights - so analysis is intuitive and auditable.

• Generates automated credit memos, diligence packages, and scenario analyses. Users can quickly produce standardized, comparable credit packages - dramatically reducing manual labor, turnaround time, and error risk.

• Provides early-warning signals and continuous monitoring. As new financials, events, or market shifts occur, AIR updates scores - giving firms foresight, not hindsight.

• Scales across any credit universe. From publicly-traded corporates to private firms and structured credit vehicles - AIR supports broad coverage, making it “one standard across credit, leadership, and stakeholders.”

In practice: AIR turns what used to take weeks of manual modeling and subjective grading into seconds - with more rigor, consistency, and comparability across portfolios.

The Team - The Right Combination of Credit + AI + Operational Rigor

AIR was founded by a group of seasoned credit-risk and AI veterans who have each lived the pain of legacy workflows. Together, they combine domain expertise, AI-first DNA, and an uncompromising mission to bring clarity and speed to credit risk, something legacy systems have failed to deliver for decades. They most recently worked together at DataRobot before joining forces to launch AIR.

CEO Glenn Carvajal brings former experience at Moody’s, where he co-led Moody’s Analytics Research, Data & Analytics Credit Products business across the Americas, CTO Arbi Abeshi brings a deep ML/engineering background building financial infrastructure at firms like Goldman Sachs and Morgan Stanley, and Chief AI Officer Joe Burdis is a credit-modeling veteran with a track record in AI/ML + structured finance from SAS and Deloitte. The trio worked together at DataRobot and has since reunited to build the next generation of AI-powered credit infrastructure redefining how institutions detect and manage risk.

There’s nothing better than when a deal comes together and our NYC enterprise tentacles work their magic. Glenn came to us via our friend Lindsay (who previously worked at a WB portfolio company) from their DataRobot days. We then called our Fund II founder Diego Oppenheimer - who’d worked with Joe and Arbi post-Algorithmia acquisition - for the inside scoop. And in true “small world” fashion, our portfolio company FireHydrant’s VP of Sales had teamed with Glenn back when Brian ran US East at DataRobot.

This is the NYC enterprise community at its best: fast signals from people we trust, all pointing to the same conclusion - this is a team we want to be in business with.

Why Work-Bench Is All-In

AIR sits squarely at the intersection of what we believe matters most right now to rethink enterprise workflows:

• Mission-critical workflow. Credit risk decisions sit at the heart of institutional risk, fund allocations, and regulatory compliance - changes here ripple across entire balance sheets.

• Manual, redundant, costly processes. Until now, underwriting and monitoring have relied on human labor, static reports, and slow review cycles. That’s inefficient, opaque, and dangerous - especially in credit markets.

• AI + data = step-function improvement. AIR shows that when you apply modern ML + data infrastructure, you don’t just get slight gains - you transform the workflow.

• Real demand and early validation. With early customers and design partners across CLOs, credit funds, institutional investors, banks, and Fortune 500 companies, AIR is already proving its product-market fit.

We believe AIR has the potential to become the standard credit-intelligence layer for institutions, replacing legacy ratings and manual workflows with an AI-first, audit-ready, scalable infrastructure.

If you’re an investor, credit manager, or risk team relying on spreadsheet-based diligence or opaque rating agencies - the future is here.

Welcome to the Work-Bench family, AIR. Looking forward to building the future of credit intelligence together. ⚡️

Congratulations to the entire AIR team! You can check out their press coverage in Banking Dive.

We are thrilled to announce our latest investment as we lead Built AI’s $6M Seed round, with participation from Lerer Hippeau and Timber Grove Ventures.

Commercial real estate moves hundreds of millions of dollars per day. And yet, one of the world’s largest and most consequential asset classes still runs on brittle spreadsheets, outdated modeling tools, and PDFs buried in inboxes.

That’s where Built AI enters. Their thesis is clear: real estate, one of the largest and most critical asset classes in the world, deserves modern tooling. Not just spreadsheets dressed up in a UI, but a purpose-built, AI-powered engine that lets investment teams underwrite smarter, faster, and more consistently.

In a world where “missed deals” is often just a function of bandwidth or speed, Built AI wants to make opportunity cost explicit, and reclaim time for what really matters: strategic deal analysis and key drivers that occur outside of spreadsheets.

Why Now?

Legacy players - Argus, Yardi, and a constellation of point solutions - weren’t built for the complexity, speed, or data fragmentation of today’s market.

Prior attempts to displace Argus failed because they ran into the same wall: data extraction wasn’t accurate enough. GenAI changed that. High-quality extraction from PDFs, spreadsheets, rent rolls, and deal docs is now finally fast, reliable, automatable, and commercially viable. This unlocks a completely new workflow: instant financial models generated directly from raw deal materials.

Combine that with leaner teams, rising transaction complexity, and industry-wide pressure for auditability, and the timing is perfect for an AI-native operating system for real-estate investing - exactly what Built AI is building.

The Product

Built AI is an AI-powered financial modeling and decision-support platform that analyzes investment opportunities in minutes instead of weeks - with transparent assumptions, line-by-line auditability, and a user-friendly interface that’s quick to learn.

Their wedge is simple but powerful: Turn fragmented, unstructured documents and deal materials into instant, accurate underwriting - and then unify everything a firm knows about a property, portfolio, or market into one end-to-end platform.

• Automated screening & underwriting at speed. Built AI can generate a full cash-flow model - including complex leasing scenarios and hold-period assumptions - in minutes, based on the user’s inputs and documents uploaded. That means fewer late nights in Argus and Excel, and fewer oversights from manual entry.

• Instant portfolio aggregation and analysis: C-suite need to make decisions at the portfolio level and Built AI automatically rolls up existing portfolios and any potential acquisitions. That means instant deep-dive analysis and faster decisions.

• Collaboration & shareability. Built for teamwork: analyses can be shared with partners, sponsors, or brokers in a few clicks - removing email chains, lost attachments, and miscommunication.

One major broker recently cut a two-week underwriting exercise that previously required multiple analysts down to a few minutes using Built AI.

In short: Built AI doesn’t just optimize underwriting - it raises the floor of intelligence across a firm’s entire investment stack.

The Team

Built AI is built by real estate people for real estate people.

It was co-founded by investment banking and real estate private equity veteran Natan Lempert, who previously held roles at Goldman Sachs, Citi and Benson Elliott, and Firoz Noordeen, who previously launched and scaled fintech ventures as Director at Natwest Ventures.

In our view, the future of software requires three essential attributes: domain expertise, AI skills, and ingenuity. These founders have all three and seek to fundamentally reimagine the real estate investment process.

By automating away manual tasks, unlocking organizations’ historical data, and overlaying market data, real estate investment professionals can rapidly screen deals, run various scenarios, and spend time laser focused on key diligence areas outside of their computer screen to transact with more speed and confidence than ever before.

Traction

The quality of the team and product paired with the market demand has led to strong early traction and proof points.

Built AI’s customers include US publicly traded developer Howard Hughes and global real estate firm CBRE, FTSE 100-listed REIT LondonMetric, and Knight Frank Investment Management, among many others.

Howard Hughes’ CFO remarked that “Built AI is helping us reimagine asset management by automating the analysis of rent rolls, cashflows, and business plans - which will allow us to do in minutes what it currently takes days. The ability to run multiple scenarios at both the property and portfolio level in real time is invaluable. We’re excited to be a design partner as they continue to expand and enhance their platform.”

We’re proud to back Built AI as they turn real-estate investing into something modern, agile, and scalable. If you’re a real estate investment professional looking to improve your deal analyses, book a demo with Built AI today ⚡️

PS - This investment has to be my wildest sourcing story ever. It involved a mohel, a Miami connection, and a consulting skill that got me through the 2008 financial crisis. Watch this ~2 minute clip to hear the whole story.

Congratulations to the entire Built AI team! You can check out their press coverage in the Commercial Observer.

🤖 Reinforcement Learning’s Last Mile Problem

We are thrilled to announce our latest investment as we lead Runhouse’s $5M Seed round, with participation from Hetz Ventures and Fathom Capital.

As AI labs increasingly rely on synthetic data to improve their models for high-value enterprise tasks, leading Fortune 500s are recognizing the immense value of their proprietary data for training powerful, domain-adapted AI agents (“post-training”). However, while reinforcement learning (RL) methods for agentic training have matured, the biggest barrier to mobilizing data is lagging ML infrastructure. Many organizations still rely on outdated tools like AWS SageMaker and Kubeflow, which are insufficient for the speed, customization, and fault tolerance required by modern Machine Learning and Reinforcement Learning workloads.

Runhouse is bringing AI post-training out of the lab and into production for enterprises and AI-native teams. Their flagship product, Kubetorch, allows ML teams to build and scale magically better models with proprietary data to power the next generation of AI use cases.

💥 Why Now

Enterprises are reaching a turning point in how they build and adopt AI. Frontier labs like OpenAI still dominate large-scale pretraining, creating foundation models with hundreds of thousands of GPUs. However, real differentiation is shifting to post-training, where models are adapted with proprietary data and simulation environments to excel at high-value agentic tasks.

DeepSeek released its groundbreaking R1 model in January 2025, with novel reinforcement learning (RL) training methods. Subsequently, the accuracy of AI code agents more than doubled. Now, post-training is being applied to power the next wave of innovation across domains like law, finance, healthcare, life sciences, and mechanical engineering, where even the largest models still struggle with production-level accuracy.

However, unlike coding models or general-purpose language tasks, the data and workflows in specialized enterprise domains are entirely proprietary. Frontier AI labs try to bridge this gap by buying synthetic datasets from providers like Scale AI or Mercor, but this approach is costly and limited. Meanwhile, enterprises already sit on the data needed to bring agentic AI to production quality. Investment banks, for example, have decades of ground-truth financial models, portfolios, and back-office tasks to train “Cursor for Finance.” As RL methods become simpler and more reliable, the best path forward is training first-party models, not praying for an AI lab miracle.

🌦️ The Problem

Even as reinforcement learning shows enormous promise, a critical barrier remains: there is no developer platform robust enough to support the post-training methods ML and data science teams want to use. For instance, RL is a heterogeneous workload, combining distributed training, inference, and evaluation, something most existing ML platforms, whether cloud-based like SageMaker or Vertex AI, or open-source like Kubeflow, are not designed to handle.

Teams also need a rapid research feedback loop. Today’s ML infrastructure slows this process to a crawl. Every small code change can require a 30-minute wait to test, thanks to slow CI pipelines and long worker restarts. Tools like ML Notebooks accelerate iteration, but cannot handle scale or workload heterogeneity, and create a gap between research and production. No single system currently enables both interactive experimentation and scalable, reproducible execution.

As a result, even as RL research methods become battle-tested and new libraries simplify implementation, infrastructure remains a stubborn bottleneck. A proof of concept might emerge in a day, but it can take an entire quarter for that same training to reach production and prove itself at scale.

🏃‍♀️🏠 The Product

Runhouse solves this bottleneck with Kubetorch, a powerful distributed framework for Kubernetes, purpose-built for modern enterprise ML and reinforcement learning. Kubetorch makes building and running complex distributed workloads on cloud hardware effortless. Regular Python code runs with instant iteration, perfect reproducibility, and unlimited scale.

Built for enterprise security, Kubetorch can be deployed in just 15 minutes entirely within VPC, whether on an on-prem cluster, cloud GPUs, or even multiple different Kubernetes clusters. For ML researchers, complex Kubernetes interactions are fully abstracted behind intuitive Pythonic APIs. For platform teams, Kubetorch’s Kubernetes-native architecture and familiar primitives make it plug-and-play with existing observability and management stacks.

Kubetorch is already powering production deployments at Fortune 500 companies, frontier AI labs, and AI-native startups. These teams are leveraging both proprietary data and valuable user interaction data from live agentic AI deployments, creating a continuous learning loop that drives ever-more performant first-party models.

🏆 The Team

Runhouse was founded by Donny Greenberg and Josh Lewittes, two engineers who have built their careers tackling the practical challenges of machine learning systems.

Donny led product for PyTorch at Meta, where he supported hundreds of engineers building on the framework. Before that, he worked at IBM as a tech lead in quantum computing research and at Google as a product manager in large-scale ad infrastructure. Across these roles, he saw a common problem: breakthrough models and platforms were exciting, but engineers struggled when iteration slowed down at the infrastructure layer.

Josh’s path has been rooted in applied machine learning. He built fraud detection models at SecuredTouch, developed backend ML systems at KPMG, and most recently worked as a machine learning engineer at Gloat. In each of these roles he ran into the same bottleneck from a practitioner’s perspective: ideas and data were plentiful, but getting models into production was slow and cumbersome.

Together, Donny and Josh bring the platform and infrastructure expertise alongside hands-on ML engineering experience needed to solve reinforcement learning’s last mile. Runhouse reflects their conviction that enterprise teams should be able to iterate on RL models with the same ease as running code locally.

💃🏻 Congratulations to the Runhouse Team

We are proud to back Donny, Josh, and the Runhouse team as they tackle one of the most important infrastructure challenges for reinforcement learning. By removing the friction between code and compute, they are making it possible for enterprises to move from experimentation to production at the pace that modern software demands.

Runhouse is currently in open beta, and you can sign up for access at run.house. Read more about their launch on the Runhouse blog.

We’re excited to announce that Work-Bench led Paratus’ $2.5M pre-seed round alongside Bling Capital, Teamworthy Ventures, and a group of defense and startup angels.

From our very first conversation with the Paratus team, we were struck by the scale of their vision and how they wanted to fundamentally rethink military workflows and help usher in a new era of generating defense readiness. The team’s ability to navigate the complexities of defense procurement while approaching a historically overlooked category with fresh, innovative thinking was nothing short of inspiring.

Military operations are high-stakes and highly coordinated, but the systems behind them? Not so much.

Today, over 3 million U.S. service members are tasked with meeting compliance requirements across areas like weapons training and medical screening. Yet, much of this still depends on outdated, disconnected systems. The result? Inefficient processes, excessive admin work, and critical gaps in visibility. All of which compound and ultimately limit a soldier's chances of being deployed on missions.

Paratus is rethinking “readiness” for military personnel that can’t afford to be unprepared. Its AI-native platform streamlines and automates critical readiness workflows from firearms qualification and gas mask checks to immunizations, mental health screenings, and beyond. Paratus’ leadership dashboard provides commanders with granular insights about which troops are ready to be deployed and cuts admin time by 90%. It’s a bold vision for how mission-critical teams should operate, with the same kind of intuitive, modern tools many of us take for granted.

The team brings early GTM hustle and a deep empathy for the friction these units face, and they’re already earning trust from some of the military’s highest tempo units. Enterprise sales is hard. Selling into defense is even harder - and why we’ve been both impressed and energized to partner with Luke Sommer, Roberto Medrano, and Stuart Baker as they redefine work management for frontline service members.

Congratulations to the entire team!

Paratus is growing their team in SF and actively hiring founding engineers. If you know someone who’s passionate about this shared mission, send them over to Paratus.

🚀 Building and selling in 2025 looks different.

🗽 If you're a technical founder or operator in NYC, don't miss our July NY Enterprise Tech Meetup for a candid conversation with the founders of Graphite and Superblocks. We will discuss how to build, adapt, and find your footing in a market that’s evolving as quickly as AI is reshaping it.

🤖 We’ll dig into what it takes to build a company when user expectations are shifting, iteration cycles are compressing, and the lines between product and go-to-market are increasingly blurred. Whether your company started before or after ChatGPT, you’re now operating in a different world.

✍🏽 We’ll talk about how to stay close to real customer problems, and how to evolve your product and positioning when the category itself is still being formed. Founders need to stay close to both product and customer. In a market moving this quickly, that’s the only way to build the right thing.

🧨 Merrill and Brad will share hard-earned lessons from building through uncertainty, iterating quickly, and staying focused on the fundamentals as the market shifts around them.

🔥 Fireside chat with:

• Merrill Lutsky, Founder & CEO of Graphite

• Brad Menezes, Founder & CEO of Superblocks

🧠 Graphite recently raised a $52M Series B and launched Diamond, its AI-powered code review agent. Diamond delivers instant, codebase-aware feedback on every pull request, helping developers catch bugs, logic errors, and style issues in seconds. The platform also supports customizable rules, stacked PRs, and self-healing CI. Engineering teams at companies like Shopify, Snowflake, and Figma use Graphite to ship high-quality code faster.

🛠️ Superblocks recently introduced Clark, an AI agent that helps developers build production-grade internal apps from natural language prompts. Clark combines AI-generated code, visual editing, and full control with React, while enforcing enterprise standards like audit logging, SSO, RBAC, and compliance. With around $60M in funding, Superblocks is already supporting teams at companies like Instacart to accelerate internal tooling development.

✨ If you're a technical founder or early operator navigating this new terrain, this conversation will leave you with real, hard-earned insight into what it means to build right now.

The NY Enterprise Tech Meetup (NYETM) is the only in-person forum exclusively dedicated to enterprise software technology. Now over 10 years old, with 10,000+ members and 100+ past events, the meetup brings together attendees across NYC's “suits and hoodies” community of enterprise startup founders and operators, Fortune 500 executives, VCs and more.

​​This NYETM is brought to you by:

​​​Work-Bench: We are an early stage enterprise software VC fund based in New York City. We lead $3M - $6M Seed rounds in SaaS and infrastructure startups, focus on supporting companies’ 0 to 1 go-to-market journeys, and have built a dynamic enterprise tech community here in NYC. We’re investing out of our $160M Fund IV, and notable portfolio companies include Cockroach Labs, Socure, Spring Health, and Dialpad.

​​Wells Fargo’s Technology Banking Group has delivered specialized insights and recommendations to technology companies for more than 25 years, serving as a service provider, buyer, and investor. The team provides scalable support to clients in all stages of the business lifecycle – early, growth, and maturity – and combines industry experience with the strength and resources of one of the largest U.S. commercial banks. Primary subsector focus includes Software, Fintech, E-commerce, Semiconductor, Business & Technology Services, and Sustainable Tech.

We're thrilled to announce that CloudQuery has raised a $16M funding round led by Partech.

CloudQuery is tackling the growing complexity of enterprise cloud visibility by centralizing cloud assets, security metadata, and cost data into a single, queryable platform. As cloud estates become increasingly sprawling and fragmented, existing tools remain siloed. There's one for security, another for cost, yet another for asset inventory. This makes it nearly impossible to get a unified view across domains.

CloudQuery takes a developer-first approach to cloud governance, pulling data from 60+ sources including AWS, GCP, Azure, Okta, and Wiz into a centralized data warehouse that's accessible through intuitive SQL queries. Built on high-performance technologies like ClickHouse and Apache Arrow, their platform continuously syncs configuration, security, and cost metadata to ensure teams work with the most accurate, up-to-date information.

Over the past year, they saw 4x ARR growth, 4x customer expansion (including 4 Fortune 500 institutions), doubled the team size, and acquired Tailwarden to bolster their platform. The company is serving some of the biggest companies including enterprises like Fastly, Zendesk, Reddit, Palo Alto Networks, Ridgeline, and more.

As founder Yevgeny Pats notes: "When you put developers first, with accessible data, flexible APIs and native language like SQL, you empower them to move faster, catch issues earlier and build more securely."

We're proud to have backed CloudQuery from day 1 and excited to continue to support them as they scale their mission to bring transparency and control to the complex world of multi-cloud infrastructure! 🚀

Read More

Check out Kelley and Yevgeny enjoying borscht 😂

Join us for a special edition of the NY Enterprise Tech Meetup focused on AI-powered hypergrowth in healthcare with the founders of Adonis and Tennr, two of the most exciting healthcare startups here in NYC.

🎢 We’ll go deep into their founding stories: how they broke into one of the most complex industries, secured early customers, and scaled through messy realities and major inflection points.

This is a rare chance to learn firsthand from founders who’ve navigated it all: product-market fit, go-to-market motion, fundraising, and building with AI. If you’re building in healthcare or thinking about it here in NYC, you won’t want to miss this!

​​​🔥 Our fireside chat with:
- Akash Magoon, Co-Founder & CEO at Adonis
- Trey Holterman, Co-Founder & CEO at Tennr

​Adonis is a revenue intelligence and automation platform helping healthcare providers recover lost revenue. With over $50M raised, the company processes over 100 million claims annually, partnering with 20,000+ providers across various specialties.

​Tennr automates healthcare documentation, streamlining patient referrals and reducing administrative burdens. The company has raised over $60M in funding, including a recent Series B led by Lightspeed Ventures, and is rapidly expanding its impact across the U.S. healthcare system.

The NY Enterprise Tech Meetup (NYETM) is the only in-person forum exclusively dedicated to enterprise software technology. Now over 10 years old, with 10,000+ members and 100+ past events, the meetup brings together attendees across NYC's “suits and hoodies” community of enterprise startup founders and operators, Fortune 500 executives, VCs and more.

This NYETM is brought to you by:

​Work-Bench: We are an early stage enterprise software VC fund based in New York City. We lead $3M - $6M Seed rounds in SaaS and infrastructure startups, focus on supporting companies’ 0 to 1 go-to-market journeys, and have built a dynamic enterprise tech community here in NYC. We’re investing out of our $160M Fund IV, and notable portfolio companies include Cockroach Labs, Socure, Spring Health, and Dialpad.

Opal Ventures: We are thesis-driven investors backing health tech startups working to transform healthcare in the US at pre-seed.

Brex: We transform finance teams from reactive no-sayers to proactive growth drivers. With Brex, companies spend with confidence by empowering employees to make smarter financial decisions from anywhere. Brex provides corporate cards, banking, and global payments, plus intuitive software for travel and expenses, that make it easy to plan and track all company spend in one place, in real time. Tens of thousands of companies from startups to global enterprises — including DoorDash, Flexport, and Compass — use Brex to proactively control spend, reduce costs, and increase efficiency on a global scale.

At Work-Bench, we’ve long believed that enterprise AI must do more than dazzle in demos, it has to deliver in production. Nowhere is this truer than in Financial Services, where complexity, compliance, and legacy systems make meaningful automation a high-stakes challenge.

That’s why we’re thrilled to announce that we led a $6M Seed investment in Artian AI, a company building reliable autonomous AI agents for business-critical processes, at true enterprise scale.

🚀 The Team

The Artian AI founding team has some of the deepest founder-market fit we’ve seen. With experience spanning Wall Street and Silicon Valley, they’ve lived both the complexity of financial systems and the cutting edge of AI innovation.

The founders are:

• Prashant Reddy: The former Head of AI & Data at JPMorgan, with prior roles at Google and Morgan Stanley, and a PhD from CMU in ML

• Keith Andrew: A former Head of FX / Market Making at firms like Morgan Stanley, KCG Holdings, and Galaxy Digital

The founders helped bring automation to Wall Street, pioneering algorithmic trading systems and autonomous market makers across banks, building arguably the first real-world agent-based systems, decades before it was a buzzword.

During diligence an exec from a top global bank told us that the trading infrastructure that the founders built in the early ‘00s at Morgan Stanley is still best-in-class. That’s the kind of impact we’re talking about.

They speak the language of enterprise buyers and developers alike, building trust through shared experience and real empathy for their users’ day-to-day pain.

🧩 The Problem

Financial institutions are under relentless pressure to streamline operations, reduce costs, and drive innovation. Yet the realities they face like highly complex environments, rapidly evolving market dynamics, and a stringent regulatory landscape make traditional automation approaches not just insufficient, but often counterproductive.

While modern AI agents offer the promise of intelligent automation, they frequently fall short where it matters most:

• Real-World Variability: Most AI solutions are brittle, struggling to adapt to the messy, unpredictable scenarios that define real-world financial operations.

• Lack of Domain Expertise: Generic AI agents fail to grasp the deep, domain-specific intricacies that are fundamental to understanding financial data, processes, and risk governance requirements.

• Workflow Disconnects: Without seamless integration into established enterprise systems and processes, AI agents become isolated, limiting their ability to deliver true end-to-end value.

• Data Management: Typical architectures for AI agents do not provide enough capabilities to govern data confidentiality levels, access entitlements, and localization requirements.

In Financial Services, building agents isn’t just a technical challenge, but a cross-functional effort that requires tight alignment across dev, data, and business teams. If done right, it delivers vastly improved agility, efficiency, and customer experience, all while ensuring security and compliance are never compromised.

🛠️ The Product

Enter Artian, with a new kind of AI automation platform built for this exact challenge.

The Artian Intelligence Platform delivers:

• Ready-to-use, finance-specific AI agents, capable of tackling high-reliability workflows across front, middle, and back office

• A natural language agent builder experience that empowers both traditional engineers and business technologists

• A multi-agent orchestration system, designed for scale, human oversight, and governance in complex enterprise environments

• The Artian Agent Exchange, enabling agent collaboration across use cases—and eventually, across institutions

This isn’t AI for toy use cases. Artian is AI for production—designed to integrate with legacy systems, operate within regulatory constraints, and scale across the enterprise.

We’re proud to back Artian AI as they help financial institutions unlock the full power of agentic AI: securely, scalably, and with domain expertise at the core.

If you're a large financial institution thinking about how to bring agentic automation capabilities to your organization, definitely reach out to the Artian AI team.

Lunch with the Artian founders at our Work-Bench go-to lunch spot Edo Sushi, at Union Square.

We’re proud to announce Work-Bench Fund IV, a $160M fund dedicated to supporting Seed-stage founders building the next generation of enterprise software.

While round names and investment amounts may have shifted, our dedication to supporting founders in their earliest days hasn’t. With Fund IV we will continue leading Seed rounds with checks ranging from $2M - 4M.

Twelve years ago, we set out to build the early-stage enterprise VC firm we wished existed in New York City - one that works side-by-side with founders from day one. With Fund IV, we’re quadrupling down on that mission and excited for even more.

🚀 From Day One to Category Defining

At Work-Bench, we’ve always believed that backing visionary enterprise founders early - really early - requires deep conviction in both the people and the problem they’re tackling.

• That’s why, when Socure imagined a future where identity verification could be instant, secure, and powered by machine learning, we backed their bold vision to eliminate identity fraud - before they had a single enterprise customer.

• When Spring Health set out to redefine mental health care in the workplace, adoption for enterprise mental health solutions was still nascent. However, we saw the power and potential for a personalized, data-driven approach to well-being.

• And when Cockroach Labs introduced an ambitious new take on the relational database - cloud-native, globally scalable, and resilient by design - we recognized the tectonic shift they were driving in infrastructure and supported them at the very start of their journey.

We’re proud to have invested with conviction in these companies’ earliest days. These weren’t just investments - they were relationships built on shared ambition, mutual trust, and a belief in changing the enterprise landscape from the ground up.

Across our portfolio, you’ll find stories like these - gritty, early, and full of conviction.

🎯 What We Invest In

Despite a decade of market shifts and evolving buyer behavior, our thesis has remained steadfast. We’re not generalists. We’re proud enterprise nerds who go deep where others stay broad.

We focus exclusively on leading Seed rounds in enterprise software startups with checks between $2M - 4M. We look for exceptional founders and companies solving real, painful problems in:

• AI and Machine Learning

• Cybersecurity

• Infrastructure and Developer Tools

• Vertical and Horizontal Enterprise Applications

We take a concentrated approach to portfolio construction. Every investment reflects deep conviction, and we work closely with each company after we invest.

⚡ What Sets Us Apart

We’re GTM Seed Specialists

Go-to-market is everything at the Seed stage. Whether it’s leveling up your discovery and demo calls, guiding your first QBR, or navigating your initial SLAs - we’re there with you. But our support doesn’t stop at GTM - we’re helping you close candidates for those first critical hires, connecting you with founders and operators for relevant tactics, and when the time is right, we’re guiding you step by step on your Series A raise.

We Form Our Own Opinions

We don’t chase hype cycles or FOMO rounds. Our investments are grounded in thematic theses shaped by deep research and hundreds of conversations with enterprise buyers. We invest where we have conviction, not where the crowd is headed.

We Back Founders With a Unique POV

The best founders don’t chase trends - they see the world differently and build where others won’t. Your deep domain expertise and firsthand experience with the pain points you’re solving is what gives us the conviction to invest.

We Believe Community Is a Force Multiplier

Enterprise software isn’t built in a vacuum. That’s why we’ve built the center for gravity for NYC’s enterprise ecosystem: bringing together founders (your peers), operators (your future hires), and corporate executives (your future customers) under one roof. Rising tides lift all boats, and our community is designed to help you scale faster.

We’re Patient and Aligned with Founders

The journey from Seed to IPO is never linear. We’re not optimizing for quick markups - we’re optimizing for lasting impact.

🌟 Let’s Build the Future of Enterprise Software Together

With Fund IV, we’re more energized than ever to support founders with bold ideas and deep conviction. If you’re building something ambitious in enterprise software - we want to hear from you.

We’re hungrier than ever to help you make a lasting impact.

Thank you to our founders, our LPs, and all of our friends in the broader NYC enterprise tech ecosystem for all of the support.

LFG 🚀

Read More in Fortune

We're thrilled to announce Work-Bench's participation in Goodfire's $50M Series A, following our Seed investment in the company last year. This new round of financing is led by Menlo Ventures alongside Lightspeed Venture Partners, Anthropic, B Capital, Wing, South Park Commons, Metaplanet, and Halcyon Ventures.

Our investment in Goodfire is truly a case study in community. We've been fortunate to know Eric Ho, co-founder and CEO, for over seven years — having first invested in his previous company, RippleMatch, where he was a co-founder and CTO. It was there that he met Daniel Balsam, now his co-founder at Goodfire.

We couldn’t be more excited for Goodfire’s massive vision to build tools to understand and intentionally design the next frontier of safe and powerful AI systems.

In conversations with enterprise customers and researchers alike, one theme kept resurfacing: traditional machine learning models remain black boxes. Even with open-source weights, it's nearly impossible to understand their granular inputs—only the outputs (inference) are visible. To assess performance, engineers often rely on trial and error.

As AI enters the mainstream, this opacity becomes more critical. In transformer-based LLMs, a single neuron can trigger multiple responses, leading to hallucinations, bias, and unreliable outputs. This pain compounds as LLMs become core to various use cases.

The Goodfire team has built Ember, a model-agnostic interpretability platform which decodes the neurons inside of an AI model to give direct, programmable access to its internal thoughts, and they are working with industry partners, like Arc Institute, to innovate in the application of interpretability research to scientific discovery and real world problems. By understanding how AI systems work internally, we can guide them toward safer, more reliable behavior.

They have assembled a team of the world’s leading researchers who have helped found the field of mechanistic interpretability at companies like DeepMind and OpenAI, pioneering advancements like Sparse Autoencoders (SAEs) for feature discovery, auto-interpretability frameworks, and revealing the hidden knowledge in AI models.

Mechanistic interpretability aims to demystify neural networks by breaking them down into understandable components. By analyzing each part and how they interact, Goodfire is pioneering a new era of model transparency—enabling humans to engage with models at the neuron level and guide them toward safer, more reliable outcomes.

Read more here

At Work-Bench, we invest in seed-stage enterprise startups and have made early bets on next-gen infrastructure companies such as Goodfire, Runhouse, AutoKitteh, Side-Eye and others.

As part of our research efforts, we actively build out thematic areas where we believe massive shifts are underway—and where new infrastructure companies will emerge.

A shift that we expect to have monumental implications for the entire infrastructure stack is the rise of AI agents—autonomous systems that plan, act, and adapt in real time. As these agents move from proof-of-concept experiments into mission-critical enterprise workflows, they’re pushing the limits of today’s infrastructure stack in ways that mark a shift as foundational as, if not more disruptive than, the cloud-native transformation.

We just published a two-part deep dive on how this shift is unfolding, authored by our very own Priyanka Somrah, and former Work-Bench founder and AI/ML expert Diego Oppenheimer.

🤖 How AI Agents Are Reshaping Infrastructure

AI agents aren’t just running inference—they’re reasoning over long time horizons, managing persistent memory, coordinating with other agents, and making autonomous decisions. That means their compute, memory, and observability requirements look fundamentally different from traditional ML or app workloads.

In our research, we explore two key questions:

📉 Part I: Where Today’s Infrastructure Falls Short

We unpack three core mismatches between AI agents and existing enterprise infrastructure:

• Economic inefficiency: Agent workloads are bursty, persistent, and stateful—breaking the assumptions behind today’s resource allocation and autoscaling models.

• Technical limitations: There are no first-class abstractions for long-term memory, inter-agent collaboration, or multi-step planning.

• Operational gaps: Existing monitoring and debugging tools weren’t designed for dynamic, evolving systems like agents—and it shows.

👉 Read Part I

🏗 Part II: What an Agent-Native Stack Could Look Like

We’re still early—but it’s becoming clear that AI agents will need new infrastructure patterns. In this piece, we explore where things might be headed as teams begin to build systems designed specifically for agentic workloads. A few layers are starting to take shape:

• Memory + Context Stores: Going beyond vector databases to support persistent, evolving memory over time.

• Coordination + Orchestration: Enabling agents to work together, manage workflows, and handle lifecycle events.

• Execution Runtimes: Built for long-running, asynchronous, and loosely connected agent behavior.

These are still early signals, and there’s a lot of experimentation underway—but we’re starting to see the foundations of a new stack take shape. Think of this as a first glimpse into what the future of agent-native infrastructure could look like.

👉 Read Part II

🧬 Why This Matters

We believe AI agents will be foundational to the next era of enterprise software and that deserves thinking through the infrastructure beneath them.

Just as the cloud-native movement gave rise to giants in DevOps, observability, and orchestration, the agent-native era will likely demand a new wave of tooling.

💬 Chat with the Work-Bench Team

If you're a founder building in this space, an enterprise buyer experimenting with agentic workflows, or an SME thinking through how the stack will evolve - let’s talk.

📩 Reach out at priyanka@work-bench.com and diego.oppenheimer@gmail.com

Over the past 10+ years here at Work-Bench, we’ve had the privilege of backing some of the most innovative companies in developer tooling and cloud-native infrastructure — from Cockroach Labs, FireHydrant, AuthZed, and CloudQuery, to CoreOS (acq. by Red Hat), Semmle (acq. by GitHub), and Backtrace (acq. by Sauce Labs)

We have seen many types of business models succeed in this category, ranging from pure open source and open core to product-led growth and traditional enterprise sales motions. With the rise of AI coding assistants and increased product development velocity, we view GTM for technical products as more critical than ever in order to differentiate and scale as businesses. Now more than ever, it’s critical for founders to effectively position, package and deliver your solution to solve real customer problems.

In order to support technical founders and the broader enterprise ecosystem, we’re thrilled to officially launch “Your Technical GTM Blueprint,” a series dedicated to sharing these insights and diving deep into the strategies, frameworks, and best practices of Technical GTM.

From product positioning and messaging to technical storytelling and more, Priyanka Somrah will explore how technical founders selling complex, engineering-focused products can effectively translate deep technical value into compelling market traction.

See our three posts below:

💰 Your Technical GTM Blueprint: Navigating the Transition from Open to Closed Source with Graphite

✍️ Takeaways:

1. Open source successfully established developer trust and accelerated early adoption when Graphite was a lean startup.

2. Recognizing when open source wasn't delivering community benefits allowed Graphite to pivot strategically toward faster innovation cycles.

3. Embracing closed source empowered Graphite to build robust enterprise relationships and develop a more effective go-to-market strategy.

🐝 Your Technical GTM Blueprint: BYOC Strategies Featuring Cockroach Labs, Okta, Earthly, and ParadeDB

✍️ Takeaways:

1. BYOC requirements emerge earlier than expected in a company’s journey, often at Seed stage rather than later in their lifecycle

2. Successful BYOC architectures separate control planes from data planes, giving customers security while vendors maintain serviceability

3. Beyond technical considerations, BYOC fundamentally reshapes business models and pricing strategies

🚀 Your Technical GTM Blueprint: Proven Open Source Monetization Strategies with InfluxDB

✍️ Takeaways:

1. Open source drives developer adoption, but doesn’t automatically translate to revenue

2. Cloud providers can monetize your open source project more effectively than you can

3. Clear separation between open and closed source components is crucial from day one

Subscribe (if you haven't already) to stay updated so you don't miss future pieces that decode the GTM strategies behind technical products that win in crowded markets.

📧 Call for Startups!

Are you a pre-seed technical founder figuring out your initial GTM approach or a pre-founder thinking through launching a technical product? I'd love to swap notes on early-stage strategies that work. Whether you're still validating product-market fit, landing your first design partners, or setting up your initial sales motion, reach out at priyanka@work-bench.com. Have experiences from your 0-to-1 journey worth sharing? I'm always looking for authentic founder stories to feature in upcoming articles.

Our NYC Pre-Seed GTM Summit last week brought together 80+ early-stage SaaS and Infra founders for a half-day session designed to arm founders with the proven sales and marketing playbooks they need to land their first 10 enterprise customers and beyond 🚀 🗽

Big thank you to our speakers:

• Dan Lorenc, SWE turned CEO/Founder of Chainguard

• Kiran Narsu, Sales Advisor at Work-Bench

• Ryan Narod, VP Corporate/Integrated Marketing at Rippling

See a recap of the most powerful tactics here and if you’re interested in getting our 28-page Tactical Workbook we walked through during the session, drop your info here.

See some of the incredible feedback we heard from founders below ⬇️

As a reminder, Work-Bench is an enterprise software Seed VC firm based in NYC. We lead $3M - $6M Seed rounds in SaaS and infrastructure startups, focus on supporting companies’ 0 to 1 go-to-market journeys, and have built a dynamic enterprise tech community here in NYC.

We love connecting with founders and helping operators think through their next move. If fundraising is on your horizon, if you want to jam on GTM, or if you’d like to workshop an idea, we’d love to hear from you - reach out to any one of us on the team 👋

Thank you to our sponsors Rho and Mastercard.