We help finance and fintech teams get AI into production: the data foundation underneath it, the models and retrieval on top, and the integration into the systems your analysts and ops people already use. Engagements run from a few days of review to building the whole thing.
each layer only as good as the one beneath it
What we do
AI in finance breaks when it sits on messy data or stops at a slide. We build the whole stack, from the data up, and prove each layer works before adding the next.
How the pieces fit together: where retrieval ends and the model begins, what each call costs, how it fails, and how a person checks its work. Designed around the constraints finance imposes, not a generic chatbot template.
Where most finance-AI projects quietly stall. We get your data point-in-time correct, reconciled and queryable, so retrieval returns the right figure and a backtest is not leaking next week into last month.
A model behind an API is not a workflow. We connect the output into the tools your team already works in, with a person in the loop where a wrong answer is costly and hands-off automation where it is not.
Models for forecasting, valuation, risk and reporting, plus the AI that reads the filings and contracts behind them. Outputs show their working: the source line, the assumption, the date it was true.
How we engage
Start with a short review, or hand over the whole build. Either way you work directly with the people doing it.
A few days
A focused look at what you already have, ending in a straight answer: is this worth building, what will it really take, and where will the data hurt you. Worth doing before you commit a quarter to it.
2–6 weeks
A working version on your own data, with an eval set that says how good it actually is. You get something real to judge against your current process, not a scripted demo that breaks on the second question.
Project-based
Design through to running in production: the data layer, the models, the workflow integration and the unglamorous wiring that usually gets cut first and then sinks the project.
Our approach
The same sequence every time: understand the decision, fix the data, prove it on a metric, then put it into the workflow.
See how we workStart 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.
Fix the data before the model: point-in-time correctness, reconciliation and lineage, so the system is not confidently wrong on its first day.
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.
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.
Leave you able to run it without us: documentation, the eval suite, runbooks, and time spent with the people who will own it.
Who we work with
Research and reporting automation, portfolio analytics, document-heavy diligence.
Credit and risk workflows, document processing, compliance support.
Data infrastructure, fraud and ops automation, AI features inside the product.
Underwriting support, claims triage, extraction from unstructured documents.
Market and alt-data pipelines, signal research, trade surveillance.
FP&A and forecasting automation, analyst tooling.
Why Rexto
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.
The model is a few weeks of work. The data is where a project lives or dies, so that is where we start.
In finance, an answer you cannot trace is an answer you cannot use. Every output should carry its sources.
You work with the people writing the code, not an account manager relaying it. Small team, direct line.
A straight conversation about your data, your workflow, and whether AI is the right tool for the job. If it's not, we'll say so.