Multi-agent AI systems. Shipped to production.

Tailor-made productized services. I ship production-grade code using Claude, agents, and the right tools for the job. I don't lean on no-code or low-code platforms unless they genuinely fit the problem, which is rare. Every engagement starts with a comprehensive diagnostic of your current workflow and the bottlenecks inside it. What I propose is specific to what I find. Nothing cookie-cutter, nothing templated.

Templates and abstraction frameworks make demos easy and production hard. The moment you need a coordinator agent that reconciles three sub-agent outputs, or a reviewer that gates draft to ready, or chat-as-orchestrator where the user steers the pipeline, you fight the framework instead of the problem. I use the Anthropic SDK directly because it's the same surface area Anthropic optimises for, and it doesn't lock you into anyone else's roadmap.

Because there's a chasm between a single-shot prompt and a system that runs unattended in production for months. ChatGPT and Claude Code are excellent assistants for one-off work. They are not eval-tested, monitored, version-controlled, regression-gated systems. Real production AI is multiple agents reconciling each other's outputs, structured schemas you can trust, fallback paths, observability, and a runbook your ops team can use at 2am. That's what I build, and most teams that try to do it themselves spend six months reinventing the same plumbing.

I don't guarantee absence of bugs. Anyone who does is lying. I guarantee detection. Every agent has an eval set with seed cases that lock the baseline before code ships. Every release runs the eval set, and regressions block merge. Automated alerts fire the moment something errors. Structured logs with correlation IDs let you trace any single run end-to-end. When something does go wrong, you find out in minutes, not when a customer calls.

A Discovery and Audit takes one to two weeks. A full custom AI product build typically runs six to twelve weeks, depending on integration surface and how messy the source data is. For the right scope, a multi-agent system can be in production beta inside seven weeks. Numbers shift if your stack is non-standard or if access takes a while. I tell you that on the diagnosis call.

Yes. That's the point of how I build. Every system ships with a runbook for step-by-step troubleshooting, structured logs, eval sets your team can re-run, and full handoff docs. The architecture is intentionally readable, so any competent senior engineer can take it over. The optional AI Ops Partner retainer covers the parts most teams don't want to own (model and tool updates, monthly eval regression, on-call), but it's opt-in, not lock-in.

Any industry. I have experience across many domains, from ops and sales to compliance and document-heavy workflows. The multi-agent patterns I use (extract, enrich, reconcile, review, ship) are portable. The architecture works anywhere an expert reads long documents to produce structured output, or anywhere a repetitive workflow is currently being done by hand. The agent prompts and eval sets get built for your specific domain after the diagnostic.

Yes. You own the codebase, the prompts, the eval sets, and the runbooks. They land in your GitHub org or wherever you keep code. Co-credited builder + customer IP arrangements are possible if you'd rather brand the system as yours and credit Accenzio as the engineering partner.

A real conversation, not a sales pitch. In fifteen minutes we walk through your most painful workflow, what it costs you in time and errors, and where the bottlenecks live. I tell you what I would deliver, how I would approach it, and a rough sense of timeline and shape. If there is a fit, I follow up with a written audit and a proposal. If there isn't, you still walk away with a clearer view of the problem. No deck, no recording, no obligation.