AI development for financial services, where every decision has to be explainable
In lending and financial operations the model is not the deliverable — the defensible decision is. We build so that any output can be traced to its inputs, its version, and its reasoning.
What changes here
Artificial Intelligence in finance is not the same engagement
Explainability as a build requirement
Where a model influences a customer outcome, the factors behind it are captured with the decision. That is what turns an adverse action notice from a research project into a lookup.
Every decision versioned and retained
Model version, prompt, retrieved context, and output stored together under your retention policy, so a question asked eighteen months later has an answer.
Evaluation sets your risk function signs off
The test set is built with compliance and risk in the room, not only engineering — including the cases the model must refuse, which are the ones examiners ask about.
What the regime actually requires
Financial regulators do not ban model-driven decisions; they require that you can explain, evidence, and reproduce them. Building for that from the start is far cheaper than retrofitting it.
- Adverse action reasoning where a model contributes to a credit decision, under ECOA and Regulation B
- Record retention in a form that cannot be silently altered — SEC Rule 17a-4 where it applies
- Model risk documentation: purpose, data, limitations, and monitoring, in the spirit of SR 11-7
- GLBA safeguards over the customer data the model reads, including vendor access
The work itself
Full artificial intelligence pageLLM applications and RAG over your own data
Retrieval-augmented systems on your documents, tickets, and records — chunking, embeddings, a vector store, and citations, so answers can be traced back to a source.
Machine learning models and forecasting
Classification, scoring, and demand or churn forecasting trained on your history, with feature pipelines and retraining scheduled rather than remembered.
Document, voice, and workflow automation
Intake, extraction, classification, and summarization wired into the systems the work already lives in, with a human review step where the cost of being wrong is high.
Evaluation sets and regression testing
A scored test set built with your subject-matter experts and run on every change, so a prompt or model swap has to prove it improved things.
Guardrails, grounding, and audit trails
Input and output filtering, retrieval grounding, refusal paths, and logged prompts and responses — the record you need when someone asks why it said that.
Latency, token cost, and model routing
Caching, batching, and routing cheap work to smaller models, with spend and p95 latency on a dashboard rather than a surprise invoice.
Finance questions we get asked
Something more specific? Send us the situation and we’ll answer it straight.