Put AI to work on a real process, and stay in control of it.
For owners and operations managers. We look at a process your team runs every day, tell you plainly whether AI will help, build it if it will, and keep it working. Nothing that matters happens without your approval.
Where you start
You do not need a programme to begin. Each step is a separate decision, and you can stop after any of them.
Decide
We map how the process runs today, compare AI with a simpler fix, check what information and access it would need, and write down what counts as done. If AI is not the answer, we say so.
Build
We build it into the tools you already use, with a review step for anything that matters, a way to recover when something fails, and written instructions for your team.
Keep it running
We re-test it whenever a model, a tool or a supplier’s format changes, look into anything that goes wrong, and send you a report each month.
Who it is for
- Owner or managing director
- Decides whether it is worth doing, and signs off what counts as done before we build.
- Operations manager
- Knows how the process really runs, including the exceptions. Approves the actions that matter and reads the monthly report.
- Whoever looks after your systems
- A member of staff or an outside IT partner who can give us access to the systems involved and see how the work is set up.
What this looks like at your size
Examples of processes a growing company brings. In each one, a person stays in charge of the step that matters.
- Orders that arrive by email
- Read purchase orders sent as PDF or spreadsheet, check each line against your products and prices, and prepare the order in your system for someone to approve.
- Supplier invoices
- Match each invoice to the purchase order and the delivery, flag any difference, and prepare the entry for approval.
- Supplier price letters
- Read the new prices, match them to your product list, show what they do to your margin, and wait for your go-ahead before anything changes.
- Quotes
- Draft a quote from a customer’s request using your price list, for a person to check and send.
- Your team’s use of Claude, ChatGPT or Copilot
- Agree what information can be used, set the right permissions, and train people on the work they actually do.
How the work runs
- A first call. You tell us the process, what goes wrong today and the systems involved. We agree which process, who owns it, and what proof you would need to trust it.
- A written proposal. What we will do, what you will get, what counts as done and what it costs. Nothing starts until you agree.
- Decide. The baseline tells you whether AI is worth it for this process. You can stop here.
- Build. We build it into your tools and test it on normal cases, awkward cases and failures before it goes live.
- Start carefully. First it is tested on sample cases, then it watches real work without acting. Then it asks for approval on each action. It acts alone only on low-risk steps you have agreed, once the evidence supports it.
- Keep it running. A monthly service checks it as things change and reports to you.
What we need from you
Four things. We reply with a proposed first step.
The process
What happens today, step by step, including the exceptions.
How it goes today
How long it takes, how often it goes wrong, and what that costs.
The systems involved
Where the information lives and which tools the process touches.
The cost of a mistake
What a wrong action would cost you, so the limits can be set to match.
What stays the same at every size
- Named people. A technical lead, a delivery lead and a named person for escalations, all named in the proposal.
- A written boundary. What the system may read, which tools it may use and which actions need approval, written down before the build.
- What counts as done, agreed in writing before we build. The result is checked in your own systems, not taken from the AI’s report of what it did.
- A person approves the actions that matter. The software enforces this; it is not left to the AI’s judgement. The system does more on its own only when you agree and the evidence supports it.
- Your data stays yours. No training or product improvement on it unless you explicitly authorise it, and deletion when the work ends.
Built for distributors and small manufacturers.
Falconic, in development, reads supplier price-increase notices, matches each line to your product list, works out what the change does to your margin and recommends a new price. Nothing is written back to your business system until you approve it.
Clients we can name
Organisations we are permitted to name.
Auxee, built for PreScouter, is live in production inside Microsoft Teams. Sony and Saudi Aramco: private-network AI deployments, delivered with PreScouter. Each name is published as agreed with the client or partner, and only with what has been cleared about it.
Questions growing companies ask
Is Chainfore only for large companies?
No. We work with growing companies, mid-market companies and enterprises, with the same method at every size: a written boundary, results checked in your own systems, and a person approving the actions that matter.
Do you publish prices?
No. The cost is set from the scope of the work and written into the proposal before you agree to anything. Each proposal shows our direct cost, our margin and how the value will be measured, so you can check them.
What if AI is not the answer?
Then we tell you. The baseline compares AI with a simpler fix, and a recommendation not to build is a valid outcome.
Do we have to commit to all three steps?
No. Each step is agreed on its own, with its own scope and its own definition of done. You decide on the next step when you have seen the results of the last one.
Start with one workflow.
Book a 30-minute call with a Chainfore engineer, or tell us the process, what goes wrong today and the systems involved.