AI Agents & Automation
LLM agents, workflow automation, and internal tools that remove repetitive work — scoped to something that actually ships.
Build
AI projects stall when they start too broad. I build narrow, useful automations: an agent that handles one workflow, a bot that answers from your docs, a pipeline that turns messy input into structured output — deployed, monitored, and cost-bounded.
- A scoped agent or automation for one clearly-defined workflow
- Retrieval over your own content (docs, tickets, knowledge base) where relevant
- Tool/function calling into your existing systems via their APIs
- Guardrails: input validation, output checks, human-in-the-loop where it matters
- Cost controls: token budgets, caching, model fallbacks, per-run spend logging
- Deployment as an API, a scheduled job, or a chat integration — plus monitoring
Start with one workflow
We pick a single process with a clear input, a clear output, and a measurable cost today. The first deliverable is a working version of that — not a platform. Breadth comes later, once the first one is earning its keep.
Grounded, not guessing
Where the task needs your knowledge, the agent retrieves from your actual content and cites what it used. Where it needs to act, it calls your real APIs with validation on both sides. Where a mistake is expensive, a human approves before anything commits.
Costs that will not surprise you
Every run logs its token spend. Prompts are cached where possible. A cheaper model handles the easy cases and escalates only when needed. You get a per-day and per-run cost readout from day one.
n8n for the plumbing
For automations that are mostly moving data between services with an LLM step in the middle, n8n is often the right tool — visual, self-hostable, and easy for your team to adjust later. I will use it instead of custom code when it is the better fit.
FAQ
Have a project in mind?
Tell me what you are building. You get a scoping call and a fixed quote — no obligation.