AI Integrations
Ship AI features into the stack you already run: production-grade, eval-tested, and yours to own.
Book a Ship Audit →You've already shipped the demo; the hard part is making it survive production. Integration is where most AI features stall: the auth, data access, latency, and cost that looked fine in a notebook fall over under real traffic. The pilot never becomes a feature your users can rely on. We wire models, retrieval, and agents into the stack you already run, instrument every call path, and hand back a feature that holds up in production, plus a pattern your own team can extend without us.
- LLM features wired into your existing app and data
- Retrieval / vector search over your own content
- Agents that call your internal APIs
- Streaming UX for chat and long-running tasks
- Auth, SSO, and multi-tenancy
- Rate-limit, retry, and fallback handling across providers
Everything below transfers to you at handover. No retainer, no lock-in.
- Integrated feature running in your environment
- Infrastructure-as-code for the whole path
- An eval suite and monitoring dashboards
- A runbook and handover documentation
- A clean handover: you own it all
You own every line.
Explicit handover terms, not fine print — this is what transfers to you when we're done.
- All source code transfers to you at handover.
- Infrastructure-as-code for the whole system transfers to you.
- The eval suite and tests transfer to you.
- Credentials and accounts we provision transfer to you.
- Documentation and runbooks transfer to you.
- No retainer required to keep it running.
- No kill-switch, no vendor lock-in — operate and extend it without us.
- AI features live in production, not stuck in a proof of concept
- Predictable latency and cost under real traffic, with evals catching regressions before your users do
- No lock-in to one model provider: swap or A/B models without a rewrite
- A codebase and pattern your team owns and extends without us. You keep every line
Questions, answered.
The questions we get asked most, answered plainly: no hedging, no marketing copy.
01 Can you build on our existing stack?
Yes, that's the point of an integration engagement. We work inside your current codebase, infrastructure, and data stores rather than standing up a parallel system you'll have to reconcile later.
02 Which model providers do you support?
We're model-agnostic by design: OpenAI, Anthropic, open-weight models on your own infrastructure, or a mix. The integration layer we build lets you swap or A/B providers without rewriting the feature.
03 How do you keep latency and cost predictable?
We instrument every call path with observability from day one, set explicit budgets for latency and token spend, and build caching, streaming, and fallback behavior so a slow or expensive call degrades gracefully instead of breaking the feature.
04 Do we own the integration afterward?
Completely. Source code, infrastructure-as-code, eval suite, and documentation all transfer to you. There's no dependency on us to operate or extend it.