AI in supply chain: automating what your team does by hand today
We do not sell "AI transformation". We take one concrete process in your operation that eats hours every week, automate it with the right technique (sometimes machine learning, sometimes a language model, sometimes well-written rules) and put it into production with measurement.
- One concrete process, one measurable result
- ML, language models or rules: whatever solves it
- Your data stays where you decide
- From pilot to production, with monitoring
What we automate
Master data
Classifying new items into the right hierarchy, detecting duplicates and substitutions, filling missing attributes from descriptions. The invisible work that ruins the forecast.
Planning alerts
Items at risk of stock-out, persistent forecast bias, purchase orders misaligned with the plan. Detected daily and sent to whoever can act, not to a dashboard nobody opens.
Assistants over your data
Ask in plain language "which families have under 15 days of coverage in the north warehouse?" and get the answer with the figure and its source. Over your ERP and dashboards, with permissions.
Documents and orders
Reading orders, invoices and price lists that arrive as PDF or email and loading them into the system with validation. What someone transcribes today.
S&OP cycle workflows
Automatically preparing the materials for each meeting: extracts, plan-versus-actual comparisons, exceptions. The cycle starts with the data ready.
Custom models
When the problem is prediction but not demand forecasting: supplier lead times, returns, cancellations, promotion demand.
How we work
Choose the process
A short workshop to list what the team does by hand, how long it takes and what happens when it goes wrong. We pick one with clear impact and available data. One, not ten.
Pilot in weeks
We build the solution on real data and run it in parallel with the current process. We measure: hours saved, errors avoided, response time.
Production
Integration with the ERP, email or dashboards you already use; permissions; a log of every action. With the technique the problem calls for, without forcing AI where a rule is enough.
Monitoring and improvement
Models degrade and processes change. We leave monitoring with thresholds and review performance with the team.
Language models are used with your company's data under no-training agreements, or with models deployed on your infrastructure when data policy requires it.
When it is not worth it
- When the process is not defined. Automating chaos produces faster chaos. The process is put in order first; if it is planning, we start with consulting.
- When there is no data or it is on paper. Capture first, then the model.
- When a simple rule solves 90 %. The rule is implemented and measured; AI is left for the 10 % the rule does not reach.
- When the process happens twice a year. The cost of maintaining an automation exceeds the saving.
We will tell you in the first workshop if your case falls into any of these.
Frequently asked questions
How is this different from machine learning forecasting?
Machine learning forecasting solves one specific problem: forecasting demand per item. This service covers the rest of the operation: master data, alerts, documents, assistants, models for other variables. They share team and method.
Which systems does it integrate with?
With what you already have: ERP (SAP, Dynamics, Oracle, Odoo, local systems), email, Excel, Power BI, WhatsApp or Teams for alerts. Integration is defined in the pilot.
Is my data used to train third-party models?
No. When we use vendor language models, it is under enterprise agreements with no training on your data. If your policy requires it, we deploy models on your own infrastructure.
How much does it cost?
It depends on the process and the integration. The initial workshop and the pilot scope are quoted separately, so you decide with a concrete number before committing to the full project.
Do I need an internal technical team?
Not to start. It does help to have a process owner who validates results and an IT contact for access. If you have a technical team, we leave it able to maintain the solution.
What does your team do by hand every week?
Tell us about a process that eats hours. In one call we will tell you whether it is worth automating, with what, and what to expect from the pilot.
Tell us about a process →