In banking, asset management, and capital markets, the data the model touches is the most regulated asset you hold. We deploy self-hosted open models — Llama, Mistral, Qwen — inside your compliance boundary so MNPI and customer PII never leave your network, fine-tune them on financial language, and engineer the unit economics so cost stays under control at trading-desk and operations volume.
A metered LLM API asks a bank to send its most sensitive material — deal documents, client positions, surveillance text, draft research — outside the perimeter to a vendor's servers. For material non-public information and regulated customer data, that is a non-starter for compliance, and a contractual data-processing clause does not change where the inference physically runs. Open-weight models remove the question entirely: the model is brought to the data, inside your VPC or data center, and nothing crosses the boundary.
Self-hosting also reverses the economics. Financial workflows are high-volume by nature — every research note summarized, every document reviewed, every client message triaged — and at that scale a per-token API meter compounds into a line item that grows with adoption. A self-hosted open model fixes your cost to capacity you own, lets you fine-tune on the language of your desks and products, and frees you from a single vendor's pricing and deprecation schedule. We help you decide where that trade-off pays off and build the system to act on it.
Open models selected, adapted, and served around the data-control and audit demands of financial services.
Value concentrates wherever data is too sensitive to externalize, volume is too high to meter, or language is too specialized for a general model:
Yes — that is the central reason financial firms move to open weights. We deploy Llama, Mistral, or Qwen inside your VPC or data center, so material non-public information, customer PII, and trade data are processed where they already live and never cross to a third-party API. Inference, logs, and any fine-tuning artifacts stay within your controlled environment and audit perimeter.
At the query volumes financial workflows generate — research summarization, document review, surveillance, client servicing — self-hosting wins on unit economics, and the gap widens as usage grows. We model your token throughput against current API spend before you commit, and design serving with quantization and batching so cost per request stays predictable and well below metered pricing.
Bring your highest-volume task and the sensitive data it runs on. In thirty minutes we will show how a self-hosted open model performs against your current API — on quality, on cost, and on data control — and how we would take it to production. Response inside 24 hours.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: