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AI Integration

ProductionAIsystems.

Notexperiments.Notdemos.

Most AI demos fall apart in production. Wrong answers under load, timeouts at scale, no fallbacks when the API fails. I build the version that ships and holds: hardened, monitored, and designed to improve.

Capabilities

Production AI, end to end.

RAG Pipelines

Document ingestion, chunking, embedding, and retrieval. Your AI answers using your actual data.

Multi-Agent Systems

Orchestrated agents that route, plan, and execute multi-step workflows: research, synthesis, and action. No human hand-holding.

LLM Routing

Dynamic routing across models based on task complexity, cost, and latency. Falls back automatically when a provider goes down.

Streaming & Caching

Token-by-token streaming responses with semantic caching. Faster UX, lower API costs.

Production Error Handling

Graceful fallbacks, retry logic, rate limit management, and monitoring. No silent failures. When something breaks, you know before your users do.

Vector Databases

Pinecone, Weaviate, or pgvector. I pick the right one based on your data volume, query latency, and cost constraints.

In production

Real systems. Real numbers.

Sanofi · Global Pharma

Chatbot accuracy improvement

0%
before
0%
after

Part of Sanofi's 65-person engineering team. Live in production.

Wizz Air · European Airline

Monthly AI interactions

0K+

Handled every month. Live in production across multiple countries.

ThynkQ AI Engine · live
0%
Success rate
0s
Avg response
$0
AI infra cost/mo
View live stats →

Stop shipping AI that breaks in production.

700K+ monthly interactions at Wizz Air. 72% to 96.2% accuracy at Sanofi. These aren't demos. They're running in production right now.

Get engineering insights

1-2 emails a month on AI engineering, shipping fast, and building products that work.