AI Product Development
I add reliable AI features to real products — chatbots, assistants, and LLM workflows — engineered for cost, latency, and graceful failure, not just demos.
Building an AI-powered product is mostly software engineering: the LLM call is the easy 10%, and the reliability around it is the other 90%. I integrate AI features into web products with the retries, fallbacks, and cost controls that keep them working in production.
What you get
- ›AI feature design scoped to a real user problem, not a tech demo
- ›LLM integration (OpenAI API and compatible models) behind a clean service layer
- ›Reliability engineering: timeouts, retries, streaming, and fallbacks for LLM calls
- ›Cost controls — caching, prompt sizing, and model selection to manage token spend
- ›RAG or tool-calling where the feature needs private data or actions
- ›A polished frontend that streams responses and handles the failure states
How I approach it
- 01Find the right feature. Pick an AI feature that solves a real problem and degrades gracefully when the model is wrong — that is what makes it shippable.
- 02Engineer the integration. Put the LLM behind a service layer with validation, retries, and fallbacks so the rest of the app never depends on a single fragile call.
- 03Control cost and latency. Cache, size prompts, stream responses, and route to the right model so the feature is fast and affordable at scale.
- 04Measure and tune. Watch quality, cost, and failure rates in production and tighten the weakest link.
Frequently asked questions
How do I add an AI feature to my existing product?
Add it as a bolt-on: put the LLM call behind its own service with clear inputs and outputs, wire it into one well-defined place in your app, and give it fallbacks so the rest of the product keeps working if the model fails or times out. Start with a single high-value feature rather than rewriting the product around AI.
What does it take to make an AI feature production-ready?
The model call is the easy part. Production readiness is the engineering around it: input validation, timeouts and retries, streaming responses, graceful fallbacks when the model is unavailable, cost controls on token usage, and monitoring of quality and failure rates.
How do you keep AI/LLM costs under control?
By caching repeated work, keeping prompts as small as the task allows, routing simple requests to cheaper models, and only reaching for the most expensive context windows when a use case truly needs them. Cost is a design constraint from the start, not an afterthought.
Can you build both the AI logic and the user interface?
Yes. As a full-stack engineer I build the AI integration, the backend service around it, and the frontend that streams responses and handles the edge cases — so the whole feature ships as one coherent piece.
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