What I Build
Three core service areas covering the full spectrum of applied AI engineering for web businesses.
AI Agent Pipelines
Autonomous, multi-step agents that research, reason, and act on your behalf — connected to your APIs, databases, and business tools.
- Multi-agent orchestration (LangChain, LangGraph)
- Tool-use & function calling (Claude, GPT-4o)
- Agentic loops with human-in-the-loop checkpoints
- MCP (Model Context Protocol) integrations
- Streaming outputs to web frontends
RAG & Knowledge Systems
Transform documents, databases, and institutional knowledge into intelligent systems that surface precise, cited answers instantly.
- Vector database setup (Pinecone, pgvector, Weaviate)
- Embedding pipelines & chunking strategies
- Hybrid search (semantic + keyword)
- Document ingestion & automated indexing
- Citation-grounded responses via LlamaIndex
AI-Enhanced Web Applications
Modern web apps with intelligence built in — from smart search and content generation to real-time AI assistants and personalisation.
- Streaming chat interfaces (Vercel AI SDK)
- AI-powered content generation pipelines
- Semantic search across your data
- Personalisation and recommendation engines
- Next.js + TypeScript full-stack delivery
Common Use Cases
These are the problems AI solves best — and where the ROI is clearest.
Customer Support
AI agents that handle tier-1 support, pulling from your knowledge base to resolve queries instantly.
Internal Knowledge
Ask your company documents, Notion, or Confluence a question and get a precise, cited answer.
Lead Qualification
Autonomous agents that qualify inbound leads, score them, and route them — without human intervention.
Data Analysis
Agents that query your database, run analysis, and surface insights in plain language on demand.
Content Production
AI-powered pipelines that draft, review, and publish content at scale while maintaining your brand voice.
Process Automation
Replace repetitive manual workflows with agents that execute, verify, and report — 24/7.
Technical Capabilities
The tools and techniques I use to build production-grade AI systems.
LLM Integration
Anthropic Claude, OpenAI GPT-4o, and open-source models — selected for the task, not the trend.
RAG Pipelines
Retrieval-Augmented Generation systems that ground your AI in your own data for accurate, trustworthy outputs.
Agentic Systems
Multi-step agents with tool use, memory, and real-world integrations that work autonomously to get things done.
Web Integration
AI features delivered into your existing web platform or as a net-new application — React, Next.js, TypeScript.
API & Tooling
Custom MCP servers, REST and GraphQL API wrappers, and webhook-driven automation pipelines.
Evaluation & Monitoring
Structured evals, LLM-as-judge pipelines, and observability tooling so you know your AI is performing.
Prompt Engineering
Systematic prompt design, chain-of-thought structuring, and few-shot optimisation for consistent, high-quality outputs.
Rapid Prototyping
From concept to working AI prototype in days — so you can validate the idea before committing to a full build.