AI Integrations
Ship AI features into the stack you already run: production-grade 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 searchVector search finds the nearest matches to a query by comparing embeddings, not exact keywords. 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 and retry handling, with fallback 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 suiteAn eval suite is a versioned set of test cases that measures whether outputs are good. 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 and data stores rather than standing up a parallel system you'll have to reconcile later.
Link to this answer: Can you build on our existing stack?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.
Link to this answer: Which model providers do you support?03 How do you keep latency and cost predictable?
We instrument every call path with observabilityObservability captures traces of every prompt, retrieval, tool call, and response for debugging. from day one and set explicit budgets for latency and token spend. We build caching and fallback behavior so a slow or expensive call degrades gracefully instead of breaking the feature.
Link to this answer: How do you keep latency and cost predictable?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.
Link to this answer: Do we own the integration afterward?