AI & agentic operations

We start narrow and real. The first build is one workflow with a measurable owner and a number attached, shipped as working software over your actual systems in roughly six to ten weeks, not a strategy deck. Once that first workflow proves the number moved, we expand from there toward a production platform across more of your operational estate. What we advise against is open-ended "AI strategy" engagements that never touch a live system.

Start with an architecture question, not a tool question: can your systems give an agent accurate, current data, and accept actions back? If yes, pick one measurable workflow and build. If no, the first AI project is fixing the integration gap, and that work pays for itself even before any AI arrives. We can assess this in days because we build both layers.

Usually partly, rarely completely. Agents need reliable data from the systems they act on, but that doesn't mean a two-year replatform first. Often the practical path is fixing the specific data flows one workflow needs, shipping the agent on top, and expanding from there. What doesn't work is AI over a core the business itself doesn't trust.

No. A chatbot answers questions; an agentic system does work. The systems we build read from and write to the ERP, fulfilment and channel systems: reconciling stock discrepancies, triaging support against live operational data, routing exceptions. Guardrails and human approval are designed in, with an audit trail for every action an agent takes.

Your operational data stays in your systems; agents query it where it lives rather than copying it into someone else's platform. Where large language models are involved, we use commercial APIs with no-training terms or private deployments, and every design states plainly what leaves your environment and why before anything is built.

An operational agent has a running cost, mostly the language-model calls it makes, and we design so that cost is predictable and tied to the work done rather than open-ended. Because agents query your systems where the data lives, there is no second platform subscription sitting underneath. We size the likely running cost as part of scoping the first workflow, so the number is on the table before you commit, not discovered later.

No. What we build runs on your systems and commercial platforms you can hold accounts with directly, and it is documented and handed over like any other software you own. We stay on where you want us for the next workflow or ongoing support, but that is your choice, not a dependency designed in.

Two things by design. First, the agent only takes the actions we scope it to take, and the ones that carry real consequence wait for a person to approve them. Second, every action is logged with the data behind it, so nothing an agent does is invisible or unexplained. We tune where the human sits in the loop workflow by workflow, tighter where a wrong call is expensive, looser where it is cheap and reversible.

AI agencies mostly can't touch the ERP: they demo well and then stall at the point of touching real operations, which is why so many pilots die. We come from the other direction, years of NetSuite and integration work on operations-led businesses, with AI built on top of that. The agents we ship act on the system of record, and we already run this in production, including an agentic knowledge platform over a logistics software estate serving retail and wholesale operators.