LLM Integration Services for Existing Products

They connect a large language model to software you already run, so the model’s output flows through your product’s logic, data, and interface. The work is mostly engineering around the model — provider selection, an API and orchestration layer, retrieval, structured outputs, evaluation, security, observability, and cost control — rather than training a model.

We add a dedicated AI service between your application and the model providers. Your app calls that service; it handles prompts, retrieval, routing, structured outputs, and fallbacks, then returns validated results. You reuse your existing authentication, data model, and interface, so the integration sits alongside what already works.

Integrate when AI improves a product that already has users; build new only when the product itself is the AI. Integration reaches value faster by reusing your stack, but it places probabilistic behavior into a deterministic system, so the real work is architecture, reliability, and cost.

OpenAI and Anthropic Claude for general reasoning and tool use, Azure OpenAI or Google where your cloud or compliance needs require them, and open-weight models where data residency or cost favors self-hosting. We put providers behind a routing and fallback layer so switching is a configuration change.

We enforce permissions before any data reaches a prompt, isolate tenant data, and minimize or mask PII. We treat external content as untrusted to defend against prompt injection, scope the model’s tool access to the feature’s needs, and require human review for high-impact actions — aligned with the OWASP Top 10 for LLM Applications.

We build an evaluation harness with representative test cases and scoring, and run prompt regression tests so a prompt or model change cannot silently degrade quality. In production we monitor quality signals per feature, so regressions are caught before users feel them.

We route cheap, high-volume tasks to smaller models, cache repeated responses, budget tokens, use retrieval instead of long context, move non-interactive work to background jobs, and set per-feature and per-tenant limits. Cost stays proportional to value as usage grows.

It depends on scope and hardening, but a single well-defined feature often moves from architecture review to a measured prototype in a few weeks, then to a hardened release after that. A short discovery step gives you a realistic, scope-based estimate before any build.

Yes. We offer staff augmentation — senior Python and AI engineers embedded in your team — as well as dedicated-team and fixed-scope delivery. With staff augmentation you direct the work; we supply the capacity and AI-specific production skills.

Python is the default language of the AI ecosystem, with first-class provider SDKs and mature libraries for retrieval, evaluation, and data work. FastAPI adds high-performance async APIs, type validation, and clean service boundaries — a strong fit for the AI service layer that brokers model calls.