AI Integrations
Embed LLMs, RAG pipelines, and agent flows into the products you already run — with guardrails, evals, and cost controls baked in.
AI features that survive contact with real users. We focus on observable, evaluable LLM features with clear guardrails — not demos that fall apart in week two.
What does an AI integration project actually involve, and who needs one?
An AI integration means embedding a large language model into a product you already run — not a chatbot demo, but a feature that survives real users hitting it with real, messy questions. This is for teams that already have a working product and a specific, painful task they want AI to take off someone's plate: answering support tickets from your own documentation, extracting structured data from unstructured documents, or automating a research or drafting step that currently eats hours of a skilled person's week.
What we build
We build retrieval-augmented generation (RAG) pipelines over your own data so answers are grounded in your actual documents and pricing, not the model's general training data. We build agent workflows for multi-step tasks that need to call your internal APIs or databases along the way. And critically, we build the guardrails most AI demos skip: input validation, output evaluation against a test set before every deploy, and cost monitoring so a runaway prompt loop doesn't show up as a surprise on your OpenAI or Anthropic bill.
How the engagement works
Discovery here focuses on defining a measurable success bar before we write a line of integration code — what does a 'good' answer look like, and how will we know if the feature is actually helping versus just looking impressive in a demo. We then build an evaluation set from your real queries, ship the first working version behind an internal flag, and only expand access once it's clearing the bar consistently against that eval set.
Typical timeline and cost
A single well-scoped RAG feature (one data source, one clear use case) typically runs RM 35,000–80,000 across six to ten weeks. Multi-step agent workflows with several integrated systems usually land RM 80,000–180,000+ across ten to sixteen weeks. Ongoing model and prompt evaluation is commonly run as a light monthly retainer once the feature is live, since model behaviour drifts and provider models change underneath you.
Why build your AI feature with Tech DevX
We've shipped AI features that are still running in production without becoming the thing that quietly breaks every time a model provider ships an update — because we build the evaluation harness first, not last. You get a feature with a paper trail: what it's allowed to do, how its outputs are checked, and what happens when it's wrong. That's the difference between an AI feature your ops team trusts and one that gets quietly switched off after a bad week.