Retrieval/data flow
Document or content ingestion, chunking strategy, metadata structure, vector search, and retrieval API.
Applied AI integration
RAG and LLM integration for real product workflows.
I help product teams integrate AI assistants, RAG workflows, vector search, and LLM-powered features into existing or new web platforms.
The focus is production usefulness: reliable retrieval, clear UX, sensible guardrails, maintainable code, and a rollout path that does not treat AI as a toy demo.
When this is useful
Use this when AI needs to become a reliable product feature.
- You have product data, expert content, documents, or support material that should become queryable.
- You need an assistant inside a real product, not a standalone chatbot demo.
- You need retrieval, UI, backend/API work, logging, and rollout thinking together.
- You want to avoid vague AI features that users cannot trust.
Technical depth
- Retrieval quality
- Chunking and metadata
- Vector database shape
- Prompt and system instruction design
- Assistant UX states
- Latency and cost tradeoffs
- Logging and debugging
- Privacy and data handling
- Fallbacks when the model should not answer
Deliverables
Concrete outputs from the engagement.
Assistant UX
A chat or copilot interface that explains what the assistant can do, handles uncertainty, and guides users clearly.
Integration code
Frontend, backend, API, database, provider integration, and deployment support.
Guardrails and rollout plan
Safety copy, fallback behavior, privacy considerations, logging/evaluation hooks, and release path.
Process
Engagement shape.
Data and use-case audit
Clarify what the assistant should answer, what data it can use, and what it must avoid.
Prototype inside product constraints
Build the first useful assistant flow with retrieval, UI, and backend integration.
Production hardening
Improve reliability, UX, fallback behavior, deployment, monitoring, and rollout.
Relevant proof
Why this maps to my experience.
FAQ
Common questions.
Can you add AI to an existing product?
Yes. This is often the best case: existing product data, users, and workflows make the AI feature more useful.
Do you only build chatbots?
No. Chat is one interface. The same underlying work can power copilots, search, content workflows, internal tools, or automation.
Do you work with sensitive domains?
Yes, but the implementation needs careful boundaries. The product must avoid pretending that the model is a source of truth where expert or legal review is required.
Can you use our existing LLM provider?
Usually yes. The integration should be provider-aware but not unnecessarily locked to one vendor.
Next step
Send a concise project brief.
Share what you want to build or improve, where the project stands, and what kind of senior technical help you need.