AI wired into the systems you already run
Most AI projects stall after the demo because nothing is connected to anything. I wire AI into the systems your company already runs, so it triages, looks things up, drafts, routes and reports — the work that quietly eats hours every week.
How I build it
I turn your services into tools an agent can call, through MCP and proper integrations, and put a single assistant in front of them. I built this in-house once already: a Slack-driven agent platform that exposed company services as MCP tools, so every team talked to one bot instead of juggling five models.
Agents read your own data through views that exclude the sensitive fields, so the boundaries sit in the tool layer rather than in the model's good behaviour. Where a deterministic automation is more reliable than an LLM, I'll tell you and write that instead.
Where it pays off
- Repetitive internal processes that quietly cost hours every week
- Knowledge and tools scattered across systems nobody wants to stitch together
- Teams each reaching for a different model, with no shared, trusted interface
Let's talk
Looking for the broader pitch? My AI automation page has more. Otherwise, tell me which internal process wastes the most time and I'll tell you whether AI is the right fix.