applied ai @pacecom, fmr investor @insightpartners
New York, NY
Joined January 2023
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Come join our Applied AI team :)
After deploying into the world's largest insurers, we've realized that FDEs are essential but need to be complemented with a great product. FDEs are invaluable: they build customer trust, deeply understand a customer's operations, and build reliable agents to automate complex insurance workflows. That said, there are too many workflows to automate, and gatekeeping technical expertise inhibits adoption.
Tempo is an AI FDE, purpose-built for insurance. With Tempo, operators can draw on everything the Pace platform offers and build a working agent in minutes. Tempo is also built to self-improve. We've spent a lot of time & eng effort on nailing the build, test, and improve loop. Anyone who has worked with a 100+ page SOP knows that there are a number of documented edge cases — and many that are undocumented. With Tempo, operators have the ability to build end-to-end agents that can be used in production.
So, what does this mean for FDEs? FDEs help uncover new patterns as they understand and automate the most difficult workflows. These new patterns can then be generalized as product primitives to make Tempo more powerful.
More on this soon. If you are interested in deploying AI in the most sophisticated enterprises in the world, or working on fun technical challenges like self-improving agents and continual learning, reach out to learn more.
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One of the most impressive unlocks with the latest versions of GPT 5.5 and Sonnet 4.6 has been massive performance gains on computer use.
Our CUA agents are now running long-duration tasks at >99% accuracy in production.
As you can see from the demo, Pace’s CUA harness has been purpose built for the harsh UIs of the insurance world, and is durable across environments like traditional browsers to RDPs to even green screen terminals.
This is what the last mile of automation actually looks like.
Most insurance work still lives in systems that were never built to talk to anything else: policy admin platforms, claims systems, the mainframe you reach through a green screen. Still, these are the platforms that hold the data the mission-critical work depends on.
Computer use removes that limit. If a person can operate a system, a @pacecom agent can too: it sees the screen, reasons about what's on it, and acts. We've pointed it at the trickiest UIs out there - cluttered policy platforms, 40-year-old mainframes, systems behind two-factor logins - and because it reasons over screens instead of following hardcoded clicks, it doesn't break when a field moves.
This is the work nobody could automate, till now. See how we build production-grade multi-modal agents here
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Really excited to share a deep dive into our product and what we’ve been working on here at Pace!
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Adithya Sanjay retweeted
Today, Pace has raised a $46 million Series B, co-led by @ThriveCapital and @Sequoia, with participation from @emergencecap and @pruvencapital, to help our customers insure more of the world’s risk.
@pacecom agents have completed more than 250,000 critical insurance operations, growing 3x every quarter. The world’s leading insurers, like Prudential, WTW and Convex, trust Pace to scale back-office operations.
60% of the world's losses last year went uninsured. Closing this $9 trillion protection gap starts with AI-native operations.
Really incredible and thorough work by @SourishJasti and team - must read for anyone interested in robotics
1/ General-purpose robotics is the rare technological frontier where the US / China started at roughly the same time and there's no clear winner yet.
To better understand the landscape, @zoeytang_1007, @intelchentwo, @vishnuman0 and I spent the last ~8 weeks creating a deep dive on humanoid robotics hardware and flew to China to see the supply chain firsthand.
Here's everything we've created + our takeaways about the components, humanoid comparisons, supply chains, and geopolitics👇
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Adithya Sanjay retweeted
1/ The future of general-purpose robotics will be decided by one major question: which flavor of data scales reasoning? Every major lab represents a different bet.
Over the past 3 months, @adam_patni, @vriishin, and I read the core research papers, spoke with staff at the major labs, and mapped the talent pool. This has completely changed how we think about general-purpose robotics.
Our paper builds intuition, step-by step, across the 2025 frontier: from architectures → evals → data → industry dynamics. Each layer reveals a different bottleneck, but they all converge on one truth—data decides everything.
Our takeaways + process below👇
If you want access to our graph (sound on), comment or DM me
“But the next frontier isn’t just digital — it’s a physical world where intelligent robots become a ‘read/write API to physical reality.’”
If you’re building in embodied AI, @SourishJasti and I would love to hear from you!
AI agents are redefining productivity. Physical agents will redefine labor.
In our latest POV, Managing Director Lonne Jaffe, Adithya Sanjay, and Sourish Jasti unpack how robotics is moving from the lab to the real world — delivering ROI across logistics, security, agtech, and more.
insightpartners.com/ideas/th…
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Adithya Sanjay retweeted
What is the best content you’ve come across on foundation models / LLMs?
(podcast episodes, blog posts, research papers, etc.)