AI agents & LLM features
I build AI agents and LLM features that connect to your data and tools and complete defined tasks. They are scoped, tested against real cases and monitored, because a demo that works once is not a system you can run a business on.
What I build
Task agents
Agents that handle a defined job end to end, such as research, triage or drafting, using your tools through controlled actions.
Knowledge assistants (RAG)
Assistants that answer from your documents and data, and show the sources behind each answer.
LLM features in your product
Summaries, extraction, classification and drafting added to software you already run.
Multi-agent systems
Specialized agents working together on a larger job. GetFoundAgent, my AI search audit platform, is built this way.
Evaluation and guardrails
Test sets built from your real cases, so changes to a prompt or a model are measured and not guessed.
Human review
Clear points where a person approves, corrects or takes over, with the context needed to decide quickly.
How it works
Diagnose
We define the task and what a good result looks like. Sometimes the honest answer is that AI is not needed.
Prototype
I test the approach on your real cases and compare models on results and cost.
Build
I connect it to your data and tools, with permissions, guardrails and logging in place.
Monitor
I track quality after launch and hand over documentation, so the system keeps working as models change.
This is a good fit if
- You have a repetitive task that needs judgment, not just rules.
- Your team loses time searching for answers across documents.
- You want AI in your product but need it to be reliable.
- A prototype impressed people, then fell apart on real data.
GetFoundAgent uses multiple AI agents and measured checks to audit websites and score them across 11 areas.
Questions about this service
What is an AI agent?
An AI agent is software that uses a language model to plan and take actions through tools toward a goal, rather than only generating text. A good one has a narrow job, limited permissions and a clear point where a person can step in.
What is RAG?
RAG stands for retrieval-augmented generation. The system finds relevant passages in your documents and gives them to the language model, so answers come from your content and can cite their sources.
When is AI the wrong choice?
AI is the wrong choice when a task follows fixed rules. A standard automation is cheaper, faster and more predictable in that case. If that is your situation, I will tell you and build the simpler thing.
Which AI model do you use?
I use whichever model passes your test cases at an acceptable cost. I compare models on your own data rather than defaulting to a favorite, and I build so the model can be changed later.
Have a process that should run itself?
Tell me what is slowing your team down. I will tell you what I would automate first, and whether AI belongs in it at all.