Adam M.

I build production AI systems that businesses run on every day: AI agents, RAG pipelines and LLM workflow automation for companies where a wrong answer costs real money.

WHY CLIENTS PICK ME

• Expert-Vetted, the badge Upwork awards its top 1% by interview, not by algorithm. 100% Job Success across 70+ projects and six years.

• Production, not prototypes. Not proof-of-concepts. Every system ships with schema validation, evals and a regression suite, so it still works six months after launch and you can prove it.

• Scale. I have processed 12 million documents through a single RAG pipeline and run agents live in front of real consumers.

• I integrate with what you already have. Your CRM, your database, your APIs, your cloud. AWS certified (ML Specialty), equally at home on GCP.

• You always know where the project is. A written update whenever something moves and a weekly Loom walkthrough. Clients tell me this is the part they remember.

WHAT HAPPENS WHEN THIS GOES WELL

A process that eats your team's week runs on its own, and you get the evidence it did not get worse. On an AI underwriting platform used by 15+ insurers across the US, policy review went from days to 3 to 5 minutes at 99.55% precision on the rules that move money. A UK mortgage broker cut manual payslip review by 80%, because the automation only escalates the documents two models disagree on. A UK government-data client got 12 million planning documents extracted, embedded and searchable in under 48 hours at 65% below the GPT-4 baseline cost.

Most of my engagements run long-term. The first project automates one workflow; the ones after that tend to automate the rest.

WHAT I DO

1. AI agents and workflow automation. Multi-agent systems that take a manual process end to end and make real decisions along the way. Built in LangGraph and Claude with MCP, schema-validated, observable at every step, with a human in the right place by design. I built the assistant inside a UK consumer money app: seven specialised agents, 43 deterministic tools, every financial figure computed by code rather than the model, so it cannot make up a number.

2. RAG and LLM engineering. Retrieval pipelines, knowledge assistants and chatbots over your own data, and cost and accuracy work on LLM systems already in production. 700+ UK planning policy documents summarised weekly, fully automated, at 75% lower LLM cost through context caching and batch APIs.