Our approach
How we actually build it.
The hard part of AI in finance is rarely the model. It is the data underneath it, the evidence that it works, and the workflow it has to live in. The steps below are built around that, in that order.
- 01
Frame the decision
Start from the decision the system is meant to support and the work around it. Before any code, we agree on the one number that will tell us whether it is working.
- 02
Ground the data
Fix the data before the model: point-in-time correctness, reconciliation and lineage, so the system is not confidently wrong on its first day.
- 03
Prototype and evaluate
Build on real data with evaluation from the start. If it cannot beat the current process on the agreed metric, far better to learn that in week three than month six.
- 04
Integrate
Put it where the work happens. Sign-off where mistakes are expensive, monitoring for drift and cost, and an audit trail that survives a hard question.
- 05
Operate and hand over
Leave you able to run it without us: documentation, the eval suite, runbooks, and time spent with the people who will own it.
Principles
What guides every decision.
Demos are easy, quarter-end is hard
Standing up an LLM demo takes an afternoon. The work is the version still correct under audit, at scale, on data it has never seen.
Data before models
The model is a few weeks of work. The data is where a project lives or dies, so that is where we start.
Show the working
In finance, an answer you cannot trace is an answer you cannot use. Every output should carry its sources.
You talk to the builders
You work with the people writing the code, not an account manager relaying it. Small team, direct line.