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Companies already running on it

Anonymized descriptions; client names are not disclosed. Results vary by business.

E-commerce supply chain

Case 1: A supply-chain company with 1,000+ employees

Livestream e-commerce supply chain — fast-changing and highly non-standard.

Why they started: Pressure across the industry — competitors were all adopting AI, results were down, and they wanted to cut costs and streamline staff.
What was stuck: Requests went from business to product to engineering — far too long a chain. Internal management, post-sourcing supply-chain processes and settlements kept going wrong.
How it works now: Business staff build their own systems with AI; more than a dozen are already running.
What we did: Provided the platform and tools, trained the business staff, and backed them up on technical issues.

40 → 16dev team headcount, two months in

A dozen+systems built by business staff, running

It's not really about saving money: when business staff build systems themselves with AI, requests no longer wait in line for engineering.

Factory

Case 2: A factory that rebuilt its entire system

Its ERP was dated, hard to use and impossible to change. The factory rebuilt it with AI and replaced the old system.

1 BeforeThe ERP vendor's system was dated and clumsy. Every change meant scheduling, quoting and waiting — and still didn't happen.

2 The decisionInstead of waiting on the vendor, people who knew the business, working with AI, rebuilt the entire system.

3 NowThe system runs on the factory's own scheduling, processes and staffing, and changes whenever the business does.

No two factories are exactly alike — equipment, people and processes all differ. Traditional factories in machinery, processing and factory-trading often need this most, and benefit most.

Cases are anonymized descriptions of real client projects. Every business has a different starting point; outcomes depend on the scope agreed in the consultation.

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