AI That Works in Production
The demo worked fine. The problem is what happens in production. Edge cases, context overflow, unexpected inputs - that is what this work specialises in.
6 services - integration to full AI product.
AI Engineering
LLM integration, AI agents, RAG chatbots - built for production with evals, guardrails, and observability.
Products & Programs
Ready-made developer tools and a discounted program for students and early-career builders.
AI that ships, not slides.
Evals from day one
Every AI feature ships with a test set. You see the quality score before launch and track it across model updates.
Guardrails and fallbacks built in
Hallucination controls, context length handling, and fallback logic - not added later when production breaks.
Models chosen for the task
Smaller models for classification, larger ones for generation. Token cost profiled during development, not after the invoice arrives.
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. Model choice depends on your latency, cost, and data sovereignty requirements.
What is RAG and do I need it?
RAG (Retrieval Augmented Generation) grounds the AI's answers in your own data - documentation, product catalog, knowledge base. Without it, the model answers from training data that doesn't include your content.
How do you prevent hallucinations?
Grounding via RAG, output validation before responses reach users, and an eval suite that catches regressions when model behaviour changes across versions.
What are you building with AI?
Describe the feature or product. I will reply within 24 hours with an honest read on what it takes to build it properly.