# AI Integrations Source: https://customlabs.io/services/ai-integrations/ Updated: 2026-09-18 Services # AI Integrations Ship AI features into the stack you already run: production-grade and yours to own. [Book a Ship Audit →](https://customlabs.io/diagnostic/ship-audit/) Problem 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. What we build - LLM features wired into your existing app and data - Retrieval / [vector search](https://customlabs.io/glossary/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 and retry handling, with fallback across providers Deliverables 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](https://customlabs.io/glossary/eval-suite/) and monitoring dashboards - A runbook and handover documentation - A clean handover: you own it all Ownership terms ## 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. Outcomes - 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 Stack LLM opsVector DBWebhooksAuth & SSOStreamingObservability Questions ## 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. 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](https://customlabs.io/glossary/observability/) 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. 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. Related & next steps [12% → 3% Field hallucination rate > “The difference wasn't a better model. It was finally being able to measure when the model was wrong.” Head of Clinical Operations · A mid-market healthtech](https://customlabs.io/case-studies/extraction-notebook-to-production/) Related services [Custom Development](https://customlabs.io/services/custom-development/)[Strategy & Architecture](https://customlabs.io/services/strategy-architecture/) Tools [AI Cost Calculator](https://customlabs.io/tools/cost-calculator/) Learn more [Our process](https://customlabs.io/process/) [How we run evals](https://customlabs.io/capabilities/#evals)