Forecast earlier. Inspect every image.
In plain wordsAnticipate changes in your business and automate visual inspections with AI.
The challenge
Decisions are made on yesterday’s figures, and visual checks rely on people looking at samples. Both are costly: stock-outs and overstock, faults spotted too late, anomalies found after the fact.
- Forecasts rebuilt in spreadsheets, week after week
- Quality checked by sampling, at the end of the line
- Fraud and anomalies found after the damage is done
- Breakdowns that could have been seen coming
What we build
Demand, sales and stock forecasting
Forecasts by product, site and period, computed from your history and the factors that drive it, with reorder points.
Anomaly and fraud detection
Unusual transactions, readings or behaviours flagged as they happen, with the reason.
Predictive maintenance
Early signs of a breakdown spotted in sensor and usage data, so interventions are planned in advance.
Churn prediction and scoring
The customers likely to leave, and scores to rank leads, risks or files, explained for the people who use them.
Visual quality control
Every part or product checked on images, on the production line or at reception, with suspected faults shown to the operator.
Documents, objects and video
Reading and extracting documents, recognising objects, analysing video streams.
Where we apply it
The same methods, adapted to the data and constraints of each sector.
Industry
Visual inspection on production lines, predictive maintenance, quality incidents analysed together.
Retail
Demand and stock forecasting by store and by product, churn, recommendations.
Logistics
Volume forecasting, delay risks, reading of delivery and customs documents.
Finance
Fraud and anomaly detection, scoring, extraction from financial documents.
Examples of use cases
Typical situations where predictive AI and computer vision apply.
Demand forecasting for replenishment
- Context
- Planners estimate demand in spreadsheets, and stores run out of their best sellers.
- Outcome
- Weekly forecasts by product and by store, with their uncertainty, sent straight to the ordering tool.
Visual inspection on a production line
- Context
- Faults are spotted by eye, by sampling, at the end of the line.
- Outcome
- Every part is checked on camera images. The operator sees the suspected fault and decides.
Fraud detection on transactions
- Context
- Suspicious transactions are found weeks later, during manual reviews.
- Outcome
- Each transaction receives a risk score. The riskiest go to an analyst, with the reasons.
Predictive maintenance on equipment
- Context
- Breakdowns stop production without warning, and maintenance follows a fixed calendar.
- Outcome
- Sensor data reveals the early signs. Interventions are planned before the breakdown.
Expected results
- Decisions based on forecasts you can trust
- Every part checked on images
- Anomalies spotted as soon as they happen
- Models measured on your own data, which belong to you
From your data to a model in production
- 01
Assess the data
History, images, sensors: what exists, its quality, and what the model needs.
- 02
Build and measure
A first model tested on your past data, compared with the way you work today.
- 03
Integrate
Results shown where your teams already work: ERP, planning tool, screen on the line.
- 04
Monitor
Accuracy followed in production, and models retrained when your data changes.
Included in every project
What comes with every projectData
Gathering, cleaning and structuring your data. It is the foundation of every AI project that works.
Security & compliance
Data under your control, hosting on your premises possible, traceability and controlled access. The GDPR, Japan’s APPI and Hong Kong’s PDPO are taken into account, and we adapt to your own security requirements and audits.
Operations & quality evaluation
Monitoring in production, measurement of the quality of the AI’s answers, guardrails, continuous improvement and cost control.
Training & handover
Training for executives and teams in AI, transfer of skills, documentation.
What would you like to anticipate, or to check?
Tell us about your data and the decision you want to improve. We will tell you what a model can do, and what it needs.