AI agent systems for manufacturers and distributors

We design and build AI agents for manufacturing and distribution businesses: systems that take on a multi-step piece of office work, check each result against your records and systems, and leave a person in charge of the decisions. If you have a workflow that eats staff time and you are unsure whether an agent could carry part of it, send us one example.

Drafted with AI assistance.

How to start

Email hi@towow.ai with one workflow your team runs today. Describe it the way you would to a new colleague: what comes in, who touches it, which systems or files are involved, and where it gets stuck. A sample input helps if you can share one.

We read it and reply with a written assessment. The assessment says which steps suit an agent and which should stay with people, how we would check the agent's results, and what we would need from your side. If it looks like a fit, the next step is a conversation to map the process, the data and the systems already in place. We agree on the scope before anything is built.

AI agents for manufacturing and distribution: where they fit

Work that suits an agent tends to share a few traits. The inputs arrive as unstructured text or files. Your staff already follow rules to handle them, often by habit and not from a written procedure. The answer depends on looking things up in more than one place. And a wrong result costs enough that a person must check it before it counts. Where a fixed rule or a spreadsheet formula already does the job, an agent adds nothing, and the assessment says so when that is the case.

What we build

An agent system for this kind of business does ordinary office work that spans several steps and several people. A request comes in as an email, a document or a spreadsheet. The system reads it, compares it with your records, drafts the next output, and routes it to the right person at the points where a human has to decide. Each step is recorded, so anyone can see what was done and on what basis.

We build these systems around three requirements, because they are where agent projects tend to go wrong after the demo:

• A person owns each decision. The agent prepares and proposes. The people who are accountable for a quote, an order or a commitment approve it.
• The result is checked against the real system. An agent saying "done" counts for nothing until the target system confirms it. In a selected set of 17 real scenarios from our own engineering records, a plain "done" label was wrong 10 times, which is why each step that changes something outside the agent gets a read-back from the target (the study).
• Work survives a change of hands. Multi-day work passes between sessions, people and systems. The handoff carries the state, the owner of each task and the evidence, so the next party does not start from a transcript (how that works in long-running agent work).

Who we are

We've worked with manufacturers, electronic component distributors, shipbuilders and building-material suppliers. Our team works in Hangzhou and Melbourne.

ToWow builds Flowness, the agent harness we run our own delivery work on. It moves work through stages from interview to review and fix, keeps a shared ledger of typed events, and uses gates that check a claimed "done" instead of taking an agent's word for it. One real run is public. In RUN-048, from June 2026, a placeholder passed a gate because the scope check was skipped. After the fix, the review ran two control commits: the one inside the declared scope was accepted, and the one outside it was rejected, with the reason kept on the ledger. You can follow it event by event on towow.ai, and read the Flowness overview on towow.net. ToWow's founder, Zhang Chenxi (Nature), leads this work (author page).

Contact

Write to hi@towow.ai with the workflow, the people and systems it touches, and what a good outcome would look like to you. We read what you send and write back.