Services · AI Development
LLM applications, agents and MCP integrations that make it to production
Most AI initiatives stall between the demo and the deployment. I build the parts that get you across that gap: the integration layer, the evaluation harness, the guardrails, and the operational plumbing around the model.
What I deliver
- · LLM application builds: chat and copilot features, retrieval-augmented generation over your own data, and structured-output pipelines inside existing products.
- · AI agent architectures: tool-using agents with clear boundaries, human-in-the-loop controls, and observability from day one.
- · Model Context Protocol integration: MCP servers that expose your internal systems to AI agents safely, and MCP clients wired into your workflows.
- · Evaluation and guardrails: test harnesses for prompt and model changes, output validation, and failure-mode handling before users find them.
Typical engagements
- · Build an MCP server for an internal system so agents and AI tooling can use it with proper identity and audit.
- · Take an agent workflow from prototype to a governed pilot with evaluation, logging and rollback in place.
- · Add an LLM-powered feature to an existing product, including the data pipeline and the quality bar to ship it.
Why Weldon Web
This is not theory: I built and operate the Sovara AI-governance products and maintain an open-source APIM MCP reference architecture. The patterns I recommend are ones I run in production.
Related reading
See the posts on MCP and AI infrastructure, or the open-source reference architecture for MCP on Azure API Management.
Start here
Tell me what you are trying to build and where it is stuck. A short scoping conversation is free and usually enough to define a first deliverable.
Get in touch →