AIIntegration
Ship LLM features, semantic search, and real-time AI pipelines inside the products you already run.
Overview
What this is
AI integration is not a chatbot bolted onto a landing page. It is the work of connecting language models, embeddings, and inference pipelines to the systems your business already depends on — CRMs, internal databases, product UIs, and operational workflows.
We build features that belong in production: semantic search across your content, summarisation and classification endpoints, streaming responses in your app, and event-driven pipelines that keep AI outputs in sync with live data.
Every integration is scoped around your constraints — latency budgets, data residency, existing auth, and what your team can maintain after handover.
Deliverables
What we deliver
Concrete artifacts — not adjectives.
- LLM feature design and API contracts aligned to your product
- Embedding pipelines and vector search wired to your data stores
- Streaming inference endpoints with timeout and fallback handling
- Integration adapters for third-party APIs and internal services
- Evaluation harnesses and regression checks before production rollout
- Observability setup — logging, tracing, and cost monitoring dashboards
Engineering approach
How we approach it
We treat AI features like any other production system: measurable, observable, and safe to ship incrementally.
- Eval-driven development — define success criteria before writing integration code
- Input/output guardrails and content filtering at the API boundary
- Cost and latency budgets enforced per endpoint before go-live
- Graceful fallbacks when models time out, rate-limit, or return low-confidence output
- Structured logging and tracing with enough context to debug production issues
Stack
Tech & tools
AI / Data
Backend
What's next
We're selective aboutthe projects we take on.
Tell us about your ai integration project — we'll respond within 2 business days with scope direction and next steps.
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