AI Development & Integration
AI features, agents, and chatbots built into your product — with a human in the loop at every decision point.
What you get.
LLM integration into existing web and mobile products
Custom AI agents and workflow automation
Conversational chatbots and support assistants
Retrieval-augmented (RAG) search and knowledge tools
Prompt engineering, evaluation harnesses, and guardrails
Human-in-the-loop review and escalation workflows
How it works. Our process for AI Development & Integration.
Discover
Find the highest-leverage use case
Not every workflow needs AI. We start by identifying the tasks where automation genuinely saves time or improves accuracy, and rule out the ones where it would just add risk.
Design
Prompt, guardrail, and escalation design
We design the prompts, retrieval sources, and guardrails together, and define exactly when the system should hand off to a human instead of guessing.
Build
Ship an evaluated, monitored system
We integrate the model into your product, build an evaluation harness to catch regressions before users see them, and instrument logging so you can see what the AI is actually doing in production.
Grow
Tune based on real usage
Once live, we review real conversations and outcomes, refine prompts and retrieval sources, and expand the AI's scope only as confidence in its accuracy grows.
Questions about AI Development & Integration.
Will the AI replace our support team or make decisions on its own?
No — every AI feature we build includes a human-in-the-loop gate for decisions that carry real risk or cost. The AI handles the repetitive first pass; people review, approve, or override before anything final happens.
Can you add AI features to a product you didn't build?
Yes. Most of our AI integration work happens inside existing codebases — we audit your architecture first, then integrate the model and guardrails without a rewrite.
How do you prevent the AI from giving wrong or made-up answers?
We ground responses in your actual data through retrieval (RAG), constrain outputs with guardrails, and build an evaluation suite that tests accuracy before every deployment — not just at launch.