AI Features That Survive Production
LLM demos work. Production breaks them. Unexpected inputs, context overflow, wrong outputs at scale - that is the engineering problem this work solves.
Integration, agents, chatbots, or a full product?
AI Integration
Add LLM features to your existing product - scoped so edge cases, costs, and quality are handled from day one.
AI Agents
Autonomous agents that use tools, make decisions, and complete multi-step tasks without constant human input.
AI Chatbots
RAG-powered chatbots grounded in your own data - documentation, knowledge base, or product catalog.
Full-Stack AI Development
End-to-end AI product: frontend, backend, retrieval layer, and LLM logic - one engineer across the whole stack.
Reliable AI is 80% software engineering.
Evals from day one
Every AI feature ships with a test set and an evaluation framework. Quality is measured before launch and tracked across model updates.
Guardrails built in
Hallucination controls, context length handling, off-topic guardrails, and fallback logic - not added after the first production incident.
Cost profiled before launch
Token usage per request is measured during development. Caching is applied for repeated queries. You know cost per user before you go live.
Results that build trust
The numbers behind the work - measured on real production data, not demos.
10+
AI features shipped to production
24h
Assessment turnaround time
3
LLM providers supported
Evals
On every AI feature build
Common questions
Which AI models do you work with?
OpenAI GPT-4o, Anthropic Claude, Google Gemini, and open-source models via Hugging Face or Ollama for on-premise requirements. Model choice depends on latency, cost, and whether data can leave your infrastructure.
What is RAG and when do I need it?
Retrieval Augmented Generation grounds the model's answers in your own data. If you want AI to answer questions about your documentation, product catalog, or knowledge base accurately - RAG is how that works.
How do you prevent hallucinations?
Grounding via RAG pipelines, output validation before responses reach users, prompt engineering with explicit uncertainty handling, and evals that catch regressions when model behaviour changes.
What AI feature are you trying to build?
Describe the feature or product. I'll reply within 24 hours with an honest read on what it takes to build it reliably.