Llama, Mistral, Qwen, and DeepSeek are now strong enough to run the enterprise — if you can take them to production. We handle model selection, fine-tuning, and self-hosting so the right open model runs inside your environment: breaking single-vendor API lock-in, keeping your data in your network, and putting cost and behavior under your control.
For two years the default answer to "which model?" was whichever hosted API had the best demo. That was the right call when open weights trailed by a generation. It no longer holds. Open models from Meta, Mistral, Alibaba, and DeepSeek have closed most of the quality gap on the tasks enterprises actually run, and they bring four things a metered API cannot: your data never leaves your environment, unit cost falls as volume rises, no single vendor can change pricing or deprecate the model under you, and you can adapt the weights to your domain.
We will not pretend the trade-offs vanish. You take on serving, GPU capacity, and the operational weight of running inference yourself, and the very top of the reasoning curve still belongs to the frontier labs. The job is to be honest about where that line sits for your workload — and for the large class of work that sits below it, open-source is faster, cheaper, more private, and yours. That is the decision we help you make and the system we build to act on it.
Selection, adaptation, and serving — engineered and measured, not a one-line model swap.
Open weights earn their keep wherever data control, volume, or specialization outweigh the convenience of a metered endpoint:
Fixed scope, fixed price, twelve weeks from briefing to live deployment.
There is no single best model — there is the best model for your task, latency budget, and hardware. We benchmark candidate open models (Llama, Mistral, Qwen, DeepSeek and their variants) on your own evaluation set rather than public leaderboards, then weigh quality against serving cost, context length, and license terms to make a defensible choice you can re-run as new models ship.
For a large share of enterprise tasks, yes. The frontier hosted APIs still lead on the hardest open-ended reasoning, but for retrieval-grounded answering, extraction, classification, summarization, and domain-specific work, a well-chosen and fine-tuned open model matches or beats them on your data — while running in your environment at a fraction of the per-token cost. We prove it with a head-to-head eval before you commit.
They solve different problems. RAG injects the facts the model needs at inference time and is the right tool when knowledge changes or must be cited. Fine-tuning changes how the model behaves — format, tone, domain language, and task structure — and is the right tool when you need consistent outputs the base model gets wrong. Most production systems use both, and we help you decide where each belongs before spending GPU hours.
How self-hosted open models map to the realities of each regulated vertical we serve.
Bring your highest-volume task and the 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 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: