From use case to production, not proof-of-concept purgatory
The gap between an impressive AI demo and a tool your team trusts in production is where most AI initiatives stall. We start from the work — diligence memos, portfolio monitoring, research synthesis, document review — and find the places where an LLM or agent removes hours of manual effort or improves decision quality. Then we build it, integrate it with your data and tools, and harden it until it's dependable.
Because we come from quantitative finance and software engineering, we're allergic to AI theater. Every engagement is scoped against a concrete outcome: faster diligence cycles, fewer manual data pulls, research that used to take a day delivered in minutes.
RAG, agents, and custom software built with AI
We build retrieval-augmented generation systems over your internal knowledge — data rooms, research archives, portfolio documents — so your team can ask questions in plain English and get cited, grounded answers. We build agent pipelines that handle multi-step research and operations work. And increasingly, we use AI to generate custom internal software and dashboards in a fraction of the traditional time and cost.
You own everything we build. We favor architectures that keep your data private and your firm independent of any single vendor.
Who it's for
- Investment firms exploring where AI fits their research and operations
- Portfolio companies that need to adopt AI without a large internal team
- Teams stuck in proof-of-concept limbo that need production-grade systems