Open-source LLMs for Cross-Enterprise.
When every function buys its own API, the enterprise loses control of cost, data, and policy. We build a governed, self-hosted model platform — Llama, Mistral, Qwen behind a central gateway — that many functions share, with model routing and central control so each team gets the right model while the enterprise keeps one boundary, one cost view, and no per-vendor lock-in.
Why the enterprise consolidates on one platform
Left to itself, AI adoption fragments. Finance signs one API, support another, engineering a third, and each negotiates its own pricing, ships its own data outside the boundary, and applies its own — or no — policy. The enterprise ends up with redundant spend, scattered data exposure, and no consistent view of how AI is used or governed. A shared, self-hosted platform inverts that: one set of open models, inside your boundary, that every function draws on.
The platform centralizes what should be central and leaves teams what should be local. Data control, access policy, logging, and cost governance live in one place; each function still gets the model and scope its work requires. Because the models are open and self-hosted, you route across them to land the cheapest capable model per request, swap models underneath as better ones ship, and keep every request inside your network — turning AI from a sprawl of vendor contracts into infrastructure the enterprise owns and governs.
Built for the whole enterprise.
A shared open-model platform with routing, governance, and control engineered for many functions at once.
- Central model gateway
- One in-boundary platform
- Many functions, one stack
- Cheapest-capable routing
- Multi-model serving
- Swap models without rework
- Access control & quotas
- Consolidated cost & usage
- Central policy & logging
Where open-source LLMs unlock value across the Enterprise
Value concentrates wherever many functions need models and the enterprise needs one place to control them:
- One gateway for every team — finance, legal, HR, support, and engineering integrate against a single internal endpoint instead of each procuring and securing its own API.
- Consolidated cost & routing — routing each request to the cheapest capable model, on owned capacity, replaces redundant per-department API spend with one governed cost line.
- Uniform data & access policy — data residency, access scope, and logging are applied once at the platform, so every use case inherits the same controls.
- Vendor independence at scale — a self-hosted, multi-model platform means no single provider can reprice or deprecate under the whole enterprise, and models can be swapped centrally.
Common questions.
Can one self-hosted platform serve many functions and use cases?
Yes — that is the point of a shared platform. We deploy a set of open models behind a central gateway so finance, legal, support, engineering, and operations all draw on the same governed, self-hosted capacity. Each team gets the model and access scope its work requires, while the enterprise gets one place to manage data control, cost, and policy instead of a different API contract per department.
How does model routing and central control work?
A routing layer sends each request to the cheapest capable model — a small fast model for simple tasks, a larger one for hard reasoning — with quotas, access control, logging, and policy enforced centrally. Because the models are self-hosted, that gateway also keeps every request inside your boundary, gives you one consolidated cost and usage view, and lets you swap models underneath without changing how any team integrates.
Explore related paths.
Make AI enterprise infrastructure.
Bring the functions already using AI and the contracts behind them. In thirty minutes we will show how one governed, self-hosted platform with routing and central control consolidates that spend, keeps data in-boundary, and removes per-vendor lock-in — and how we would build it. Response inside 24 hours.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: