LLM integration services · EonTech

Put an LLM inside the product
you already ship.

You do not need a new app to use a language model. We embed LLMs into your existing product, grounded in your private data, orchestrated through your services, and measured in production. Provider-agnostic, so the model is a choice you can change, not a cage.

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  • RAG over your private data
  • Claude, GPT, or open models
  • Cost & privacy controlled

How integration works

From existing product to a feature people trust

We plug language models into what you already run. No rip-and-replace, no second product to maintain. The work follows a clear line.

  1. 01

    Map the use case

    We find where a language model earns its keep in your product, and where it does not. Scope is honest before any code.

  2. 02

    Connect your data

    Retrieval over your private content with vector search and chunking tuned to your documents, so answers are grounded and cited.

  3. 03

    Orchestrate the calls

    Prompt design, tool calling, and multi-step chains wired into your existing services and auth, not a side app.

  4. 04

    Evaluate & observe

    An eval suite plus tracing and logging, so you can see quality, latency, and cost in production and catch regressions.

Model strategy

The right model for each job, not one vendor for all

We integrate behind an abstraction layer, so swapping or mixing models is a configuration change. You keep leverage on price and capability as the field moves.

  • Frontier hosted models

    Claude, GPT, and Gemini for the hardest reasoning and the broadest capability. We integrate them behind an abstraction so you are never locked to one.

  • Open-weight models

    Llama, Mistral, and similar models you can self-host when data residency, cost, or control matters more than raw frontier capability.

  • Model routing

    Send each request to the cheapest model that clears your quality bar. Frontier where it counts, smaller models where it does not.

What we protect

Private data, controlled cost, no lock-in

An LLM feature that leaks data or burns budget is a liability, not an asset. We design the integration so your content stays inside your boundary and every call has a cost ceiling.

Because the model sits behind an abstraction we build and you own, you can change providers without rewriting the product. That is leverage that lasts past this contract.

See generative AI development

Common questions

What product teams ask about adding an LLM

  • Which model should we use?

    It depends on the task. We stay provider-agnostic and integrate behind an abstraction so you can use a frontier model like Claude or GPT where reasoning matters, an open-weight model where control or cost matters, and route between them. You are never locked to one vendor.

  • Can the LLM answer from our private data without leaking it?

    Yes. We build retrieval over your private content with vector search, keep that data inside your boundary, and work to GDPR and ISO 27001 standards. The model sees only the context you allow, and every answer can be traced to a source.

  • How do you keep token costs from spiralling?

    We route each request to the smallest model that meets your quality bar, cache repeated work, and set token budgets per feature. You see cost and latency in production through the observability we wire in, so spend never surprises you.

Add intelligence without the rewrite

Show us the product and the data behind it. We will scope an LLM feature that is grounded, observable, and provider-agnostic from the start.

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