Answers grounded in your data
Retrieval pipelines that bring relevant documents into the conversation and preserve source references for review.
01 / Agent engineering
Custom AI Agent DevelopmentIntelligence that
gets things done.
Give your software the ability to reason, retrieve context and take useful action. I build AI agents around your systems, with explicit permissions and a clear path back to a human.
SYSTEM BLUEPRINTFIG. 01
- 01Your knowledge
- 02Agent + context
- 03Tools & approvals
- 04Useful action
THE TOOLKIT
- Node.js
- Tool calling
- RAG
- Ollama
- Vector search
01 / THE CAPABILITIES
Built for the way
your business works.
I develop custom AI agents that connect language models to your documents, APIs and workflows. Engagements cover retrieval, tool execution, evaluation and deployment, with human approval for sensitive actions.
Tools with boundaries
API integrations with input validation, scoped access and approval steps for actions that change important records.
Evaluation before expansion
A representative task set, observable tool traces and explicit fallback behavior to test what the agent can actually handle.
02 / THE SCOPE
A clear scope.
A useful handover.
Every engagement starts with your constraints. We agree on the integrations, delivery milestones and operational ownership before the build.
WHAT YOU CAN EXPECT
- Agent workflow and tool contracts
- Retrieval and context pipeline
- Evaluation scenarios and failure handling
- Deployment notes and operational handover
Scope-based proposal · Provider and hosting costs agreed separately
03 / HOW WE WORK
From first conversation
to a working system.
- 01
Understand
Map the workflow, constraints and what success should look like.
- 02
Design
Agree on the architecture, scope and the decisions worth testing early.
- 03
Build & validate
Deliver in reviewable increments and test the failure paths too.
- 04
Launch & hand over
Deploy, document and agree on ongoing operational ownership.
04 / RELATED WORK
AI session coordination
A Node.js service coordinating concurrent AI sessions with Redis, job queues and explicit state transitions.
Private project · Source code is not publicly available
Read the architecture walkthrough05 / A LITTLE CLARITY
Good questions.
Straight answers.
A few things you might want to know before we start.
What is the difference between a chatbot and an AI agent?
A chatbot primarily responds to messages. An agent can also call tools, retrieve business data and complete a sequence of tasks. I define which actions it may perform and where human approval is required.
Can an agent use private company documents?
Yes. A retrieval pipeline can search authorized documents and provide relevant passages to the model. Access controls, hosting requirements and retention rules are agreed before implementation.
Can you work with locally hosted models?
Yes. I work with Ollama and local model hosting. Model selection depends on available hardware, task accuracy and response-time requirements; I assess those constraints before proposing a deployment.
How do you measure whether an agent works?
We define representative tasks and expected outcomes, then test answer quality, tool correctness and failure handling. An evaluation set and reviewable traces are more useful than a generic accuracy promise.
LET’S BUILD SOMETHING USEFUL
Your next idea.
Let’s make it work.
Tell me what you’re building, what’s getting in the way, and where you want to go.