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AI integration, built into your product

You don’t need another standalone AI demo — you need AI woven into the product you already have. Inner Loop integrates large language models, retrieval, automation and AI-driven features into live products, websites and systems, with the engineering rigour to keep them reliable in production.

Who it’s for

  • Product teams that want AI features inside their existing app, not a separate tool.
  • Companies with documents, data or support load that AI could genuinely lighten.
  • SaaS businesses adding assistants, search, summarisation or automation.
  • Teams who tried an AI prototype but couldn’t get it production-ready.

What integration covers

01

LLM features

Assistants, generation, classification, extraction and summarisation built directly into your product’s flows — with sensible UX, not a bolted-on chatbox.

02

Retrieval & RAG

Grounding AI in your own content and data so answers are accurate and trustworthy, with the plumbing — embeddings, vector search, evals — done properly.

03

Automation & agents

Connecting AI to your systems and tools to automate the repetitive work, with guardrails so it stays safe and predictable.

04

Production hardening

Evaluation, cost control, latency, fallbacks and monitoring — the unglamorous work that turns a clever prototype into something you can ship to users.

What you walk away with

  • AI features live inside your real product, used by real users.
  • Reliable, measured behaviour with costs and latency under control.
  • A clean integration your existing team can maintain and extend.
  • A clear view of what to build next.

Common questions

Can you work with our existing codebase? +

Yes. The whole point is integrating AI into what you already have — your stack, your product, your data — rather than starting from scratch or forcing a separate tool on your users.

Which AI models do you use? +

Whatever fits the job. We work extensively with Claude and other leading models, and choose based on quality, cost, latency and privacy for your specific use case rather than loyalty to one provider.

Our last AI prototype never made it to production. Why? +

Usually because the hard 20% — evaluation, edge cases, cost, latency, reliability — was never finished. That production-hardening work is exactly where we focus, so features actually ship and stay shipped.

How do we start? +

Tell us about your product and where you think AI could help. We’ll scope a focused first integration that delivers visible value quickly.

Ready to ship something real?

Tell us what you’re working on. The first conversation is about your goals and whether there’s a genuine fit — no obligation.

Book a Consultation