LLM Integration
You already have a product, a codebase and users. Integration work is about fitting model capability into that reality — behind feature flags, with fallbacks, cost ceilings and a way to roll back.
What this includes
Provider abstraction
One interface across OpenAI, Anthropic, Google and open-weight models so you are never locked to a vendor or a price change.
Streaming interfaces
Token streaming, partial rendering and cancellation that make latency feel like responsiveness.
Prompt management
Versioned, reviewable prompts stored as configuration rather than buried in code.
Cost controls
Per-tenant budgets, rate limits, caching and automatic downgrade paths under load.
Fallback chains
Automatic failover between providers when one degrades or rate-limits.
Structured outputs
Schema-validated responses your existing code can consume safely.
What you end up with
- Model capability shipped behind feature flags
- No vendor lock-in
- Predictable cost per request
- Rollback in minutes, not days
Tools we reach for
Frequently asked
Which provider should we use?
Usually more than one. We benchmark candidates on your actual workload and keep a fallback configured — provider performance and pricing move constantly.
Can you work inside our existing codebase?
Yes. Most integration work happens in your repository, in your style, reviewed by your engineers.
Other work in this practice
RAG & Knowledge Systems
Turn scattered documents into a knowledge layer your AI can answer from — accurately, with sources.
Read moreAI Agents & Automation
Agents that use real tools, take real actions, and know their limits.
Read moreComputer Vision
Extract structure and meaning from images, documents and video streams.
Read moreLet's talk about what you're building
Tell us the problem. We'll tell you honestly whether AI is the right tool, and what it would take.