Put language models inside the systems that run your business.
Selection, fine-tuning, and integration of large language models into your applications and workflows — with the routing, caching, and cost controls production demands.
Why this matters now
Calling an API is easy; running LLMs in production isn't. Latency budgets, token costs, model churn, evaluation, and fallback behavior all need engineering that demo projects skip.
Service pillars
Model selection & benchmarking
Frontier vs. open-weight, hosted vs. self-run — benchmarked on your tasks and your cost envelope.
Fine-tuning & adaptation
LoRA/full fine-tunes and domain adaptation when retrieval alone isn't enough.
Production plumbing
Routing, caching, observability, cost dashboards, and graceful degradation.
What changes for your operation
- Vendor independence — Abstraction layers make model swaps a config change.
- Predictable costs — Caching and routing typically cut token spend 40–70%.
- Measurable quality — Eval suites gate every model or prompt change.
Stack we deploy with
- OpenAI / Anthropic / Bedrock / Vertex
- vLLM & self-hosting
- LiteLLM routing
- Langfuse observability
Where this service earns its keep
- Document extraction pipelines
- In-product AI features
- Ticket classification and routing
- Speech-and-text analytics on operations data
Where we deploy it
Questions we hear most
Retrieval first — it's cheaper to maintain and easier to audit. We fine-tune when style, structure, or latency requirements demand it, and often combine both.
Ready to put llm integration to work?
Book a demo, or start with the AI Readiness Assessment — a 30-minute working session that maps your highest-value first deployment.
- AI that sees, predicts, and acts — not just a dashboard.
- Pilot to fleet rollout in weeks, with a go/no-go you can defend.
- Enterprise-grade security, procurement, and support from day one.
Schedule a demo
See Invexal on your own cameras and data.