AI features built around your product.
I design and build practical AI features that help your product do more — from AI assistants and search over your own data to automation, document workflows, and intelligent product experiences. I handle the product experience, AI models, retrieval, integrations, and production setup end to end.
Scope
What I build
AI features for existing products
AI assistants & workflows
RAG & search over your data
AI chatbots
LLM integrations
Document & knowledge workflows
Approach
From the idea to production.
I don't add a chat box and call it AI. The feature has to sit inside the product — the right context, retrieval over your data, the actions it can take, and a setup that doesn't blow the bill.
Product & UX
Figure out where AI belongs in the flow, what it should do, and what it shouldn't — not a generic chatbot in the corner.
Product integration
Build the feature directly into the product: chat, search, generation, assistants, and the interface people actually use.
AI & retrieval
RAG, embeddings, prompts, tool calling, and the model setup the job actually needs.
Production
Cost, latency, monitoring, fallbacks, and the infrastructure so the feature holds up after launch.
Capabilities
What I can handle
Design & frontend
- Next.js
- React
- TypeScript
- Chat interfaces
- Streaming
- Embeddable widgets
AI systems
- OpenAI
- LLM APIs
- Prompting
- Tool calling
- Agents
- Vercel AI SDK
Retrieval & data
- RAG
- Embeddings
- Vector search
- Chunking
- Tenant isolation
- Website & document ingest
Integrations
- Existing product APIs
- Scraping
- Auth
- Analytics
- Webhooks
- Third-party services
Production
- Cost controls
- Latency
- Monitoring
- Fallbacks
- Deployment
- CI/CD
Difference
What goes into the build
In the product, not beside it
AI is wired into real workflows and product actions — not a standalone chat layer that can't do anything.
Grounded in your data
Retrieval, tenant isolation, and context are part of the architecture, so answers come from the business, not a generic model.
Cost and latency from the start
Tokens, retrieval, and response time are considered in the build — not discovered after the bill arrives.
End-to-end ownership
You work directly with the person designing, building, and shipping the feature.
Process
How a project works
- 01
Understand
We figure out what the AI is supposed to do, who it's for, and whether it actually belongs in the product.
- 02
Design
I turn that into the flow, the interface, and the limits of what it can and can't do.
- 03
Build
The feature, retrieval, prompts, integrations, and the systems behind it.
- 04
Ship
Production, cost, monitoring, and a feature that's actually ready to use.
FAQs
What kind of AI features can you build?
Assistants, chatbots, RAG search, document workflows, automation, generation, and agentic workflows that sit inside the product. If chat isn't the right interface, I'll say so.
Can you add AI to an existing product?
Yes. That's most of the work. I'll look at the product, the data, and the job to be done, then build the feature into the workflows and interfaces already being used.
Can you build RAG systems using our data?
Yes. Ingestion, chunking, embeddings, vector search, retrieval, and generation around your website, documents, or product data — with tenant isolation where it matters.
Do you build AI agents?
Yes. When the feature needs to take actions rather than only generate text, I can build tool calling and agentic workflows around the product's existing systems.
Can you handle the frontend and backend?
Yes. The AI experience, backend services, integrations, and the infrastructure to run it. I don't hand off the hard part.
How do you control AI costs?
Model selection, token use, retrieval, caching, latency, and background processing are part of the architecture — not something we discover after the first invoice.