AI Integration
Put AI inside the software you already run.
We embed LLMs, RAG, and AI features directly into your existing products, apps, and internal tools — through clean API integrations rather than bolt-on widgets.
What it is
We embed LLMs, RAG, and AI features directly into your existing products, apps, and internal tools — through clean API integrations rather than bolt-on widgets.
Best for
Product teams who want real AI capability inside their software without a ground-up rebuild.
What you get
- Working AI features inside your stack
- A retrieval (RAG) knowledge base where relevant
- Integration code with documentation your team can maintain
- Testing suite covering edge cases and failure modes
How it works — visually
How it works
The process
Discovery
We audit your existing stack, identify integration points, and define the AI capability that delivers the highest ROI.
Design
We design the integration architecture — model selection, RAG pipeline if needed, and the API contract.
Build
We build the integration, write tests, and wire it into your product behind a feature flag.
Ship
We hand off working, documented code your team can own and iterate on.
FAQ
Common questions
Which AI models do you integrate with?
We work with all major providers — Anthropic Claude, OpenAI GPT, Google Gemini — and open-source models via Ollama or Hugging Face. We recommend the best fit for your use case.
Do we need to share our data with AI providers?
Not necessarily. We can set up private deployments, use providers with enterprise data agreements, or use models you host yourself.
What is RAG and do I need it?
RAG (Retrieval-Augmented Generation) lets an AI answer questions using your own documents and data rather than just its training. If you want AI that knows your product, docs, or company knowledge — you need RAG.
Ready to get started with AI Integration?
Book a free intro call and we'll scope the engagement, agree on outcomes, and give you a fixed price — before any commitment.