AI-Powered Research & Quantitative Analytics
We build the analysis layer of financial products: features that read fundamentals and technicals and say something useful in plain language, and quantitative models that grade a whole universe on a schedule.
We run both in our own platforms every trading day, which is a different thing from having built one once.
- AI Analysis
- Ranking Models
- Screening
- Guardrails
What We Do
Design and build research and analysis features end to end. Build scoring, ranking and screening models, and the pipelines that keep them current. Define how a model is evaluated, so someone other than its author can tell whether it is working.
And put guardrails around generated output — the difference between a feature you can ship to real users and one you quietly disable.
- Feature Design
- Model Build
- Evaluation
- Output Guardrails
When Teams Bring Us In
The AI feature was impressive in the demo and embarrassing in production. The model works but nobody internally trusts it enough to put it in front of customers. Nobody can answer “is this output good?” without reading every result by hand.
Or you want research automation in your product and would rather not spend a year learning which parts are hard.
- Demo-To-Production
- Trust Gap
- No Evaluation
- Time Pressure
What You Walk Away With
A feature running in production, not a prototype. A way to measure whether it is still working next quarter. Written decisions explaining why it is built the way it is. And a team of yours that can operate it without us.
- Running In Production
- Evaluation In Place
- Decisions Documented
- Your Team Owns It