No hype Fixed-scope discovery + build
AI / LLM Integration (RAG, Agents, Inference)
Integrate LLMs into real products with correct boundaries, evaluation, and cost control.
AI features that are reliable, testable, and cost-aware—not a demo that breaks in production.
When it fits
- You want search + Q&A over private docs (RAG) with traceability
- You need structured outputs, tool calling, or workflows/agents
- You want to run inference locally / on GPU / in controlled infra
- You need to decide if AI is worth it (and how to avoid risky coupling)
Deliverables
- RAG pipeline: chunking/embeddings/vector store + retrieval strategy
- Evaluation plan: quality metrics, regression tests, prompt/version control
- Guardrails: PII boundaries, content controls, fallbacks and timeouts
- Cost/perf tuning: caching, batching, routing, model selection
Not a fit for
- “Add ChatGPT” requests with no product goal, user journey, or evaluation criteria
- Teams unwilling to treat AI as a production system (monitoring + testing)
Contact
Tell me a bit about your context (stack, constraints, timeline) and what outcome you want.
Recommended info
- Current architecture + biggest pain
- Success metric (latency, cost, delivery speed, reliability…)
- Constraints (team size, deadlines, infra, compliance)