AI Development for Financial Services

Risk models, alpha research tools and document intelligence for banks and asset managers, built to pass model validation, audit and stress testing.

In finance, a model has three audiences: the business that wants the signal, the risk function that has to trust it, and the auditor who will ask how it worked two years from now. Most AI projects are built for the first audience. Our Finance AI practice builds for all three, which is why the models we ship survive compliance, audit and stress.

What we build

• Risk and compliance models with explicit assumptions, documented limitations and monitoring for drift.
• Alpha research tooling: pipelines for feature generation, backtesting and evaluation that keep research reproducible and free of look-ahead leakage.
• Document intelligence: extraction, classification and summarization over contracts, filings and internal documents, running on models deployed inside your perimeter.
• On-prem LLM stacks for analysts and operations teams, with retrieval grounded in your own corpus and logging suitable for review.
• Audit-ready ML infrastructure: versioned data, reproducible training, model registries and inference logs.

Engineering for governance

Traceability from training data to inference

We version the data, features, code and weights behind every model, and log each prediction with the version that produced it. When a reviewer asks why the system made a decision, the answer is reconstructable.

Inside your perimeter

We deploy on your infrastructure with no third-party model APIs. Sensitive documents, positions and client data stay where your security policies already govern them.

Latency and cost that fit the workflow

Many financial workloads are latency-sensitive or high-volume. We apply the same inference optimization work behind our DeepSeek R1 inference world record on NVIDIA Blackwell (low-precision inference and model-specific kernels) so on-prem models are fast and affordable enough to use in production. More on that in LLM Engineering.

How an engagement runs

We work with your business, risk and technology teams through four phases.

1. Scoping. We agree on the decision the model supports, the controls it must satisfy and the metrics that define success, with business, risk and technology stakeholders in the room.
2. Research. We build a baseline, test it out of sample and under stress, and iterate on what actually moves the metric.
3. Production. We integrate with your data platforms and access controls, add monitoring and logging, and prepare documentation for model risk review.
4. Handover. Your team receives the code, models, runbooks and validation evidence, and we pair with them until they can maintain and extend the system.

Who we have done this for

Our team has built on-prem inference stacks, risk and compliance modeling and audit-ready ML systems inside a tier-1 banking perimeter at JP Morgan, and has worked with Avian. We bring that operator experience to each engagement, rather than learning the constraints of regulated finance on your time.

For a sector view of the problems we solve for banks and asset managers, see Banking & Finance. To discuss a specific use case, contact us.