AI & LLM Integration
Useful AI features, not demos that break in production.
AI features are easy to demo and hard to ship. The gap is everything around the model: grounding answers in your own data, handling bad output, controlling cost, and knowing when the thing is misbehaving. That is the part we do well.
We build retrieval, structured extraction, assistants and automation on top of the major providers, with evaluation and guardrails so the feature stays useful after launch instead of quietly going wrong.
What you get
- Retrieval-augmented answers grounded in your data
- Structured extraction and classification
- Streaming chat and assistant interfaces
- Prompt evaluation, guardrails and fallbacks
- Token cost monitoring and rate controls
Common questions
Will the AI make things up?
Any model can. We reduce it by grounding answers in your data, constraining outputs, and adding checks, then we measure accuracy instead of assuming it.
Which model or provider do you use?
Whichever fits the task and budget. We keep the integration provider-agnostic where possible so you are not locked in if pricing or quality shifts.
How do you keep costs under control?
Caching, smaller models for simple steps, token budgets, and monitoring so a runaway prompt cannot quietly run up a bill.