Demand models per SKU and customer, B2B churn prediction, catalog classification, datasheet extractors and opportunity scoring — trained on your ERP and your datasheets, not on a retail benchmark.
In B2B distribution, demand isn’t driven by consumer seasonality: it depends on orders per customer, project cycles, industrial calendar and negotiated pricing. Standard ERP forecasting treats a customer who orders weekly the same as one ordering three times a year. Catalog classification and deduplication get done by hand every time a new supplier comes in.
We build models trained on your real data: daily forecast per SKU and customer, B2B account-churn scoring, catalog classifier for deduplication and categorisation, field extractor from datasheets and spec sheets, and commercial-opportunity scoring. Integrated with SAP Business One, Holded, Odoo and Microsoft Dynamics 365 without touching your master-data model.
Daily prediction per SKU and per SKU–customer pair using your order history, industrial calendar, sector behaviour and negotiated pricing — feeds replenishment and lets you anticipate specific drops, not just aggregate ones.
Model trained on lost accounts: estimates 90-day churn probability combining order frequency, average size, product mix, logistic incidents and shifts in buying behaviour — to open a real retention window.
Categorises products into your taxonomy, identifies duplicates across suppliers and proposes merges by equivalent key — critical to onboarding new catalogs without your master-data team spending weeks mapping.
Reads PDF datasheets and spec sheets (multilingual, with tables and mixed units), extracts structured technical attributes and normalises units — ready for ERP and PIM with no retyping.
Predicts close probability and expected deal size using account history, ordered SKU mix, buyer behaviour and activity signals — so the sales team prioritises accounts with a real probability of closing.
Forecast by family and month with an ERP model — average per-SKU error of 28%. Replenishment by gut on long-tail SKUs, with frequent stock-outs and excess on others. Onboarding a new catalog (3,000 SKUs) takes the master-data team 4–6 weeks. Account losses are spotted when the rep calls and the customer is already buying elsewhere.
Per-SKU–customer forecast with average error of 14% and an early signal for critical SKUs across the top-200 accounts. Catalog onboarding is done in 5 days with human review only on low-confidence cases, not on all 3,000. Churn flags 72% of at-risk accounts 60–90 days ahead, giving the rep room to act. Opportunities arrive in the CRM prioritised by score.
Enterprise CRM with fine-grained permissions — AI workflows that respect the data model.
Enterprise CRM/ERP suite in the Microsoft ecosystem — native fit with 365 and Power Platform.
Reference ERP for mid-market distribution and manufacturing — document extraction and ops orchestration.
Spanish cloud ERP widely adopted by SMBs — invoicing, expenses and reconciliation automation.
Modular open-source ERP — AI agents and workflows on top of sales, inventory and project modules.
Email, calendar and SharePoint as channel and context — triage, drafting and RAG over your inbox and files.
No, and we say so in the first meeting. Quality and consistency in product master, customer codes and initial categorisation are the foundation — without that, no model holds up. The good news is that part of the project is exactly that: the catalog classifier and deduplicator clean and normalise as we train. We start with families that have healthy data, prove results, and extend from there.
We connect via standard API, intermediate connector or scheduled exports, depending on the ERP and version. Predictions (forecast, churn, scoring) are written into custom fields or extended tables, without modifying the native model. If you need traceability for audit or ISO, we keep an audit log of every prediction and the model version that produced it.
For new SKUs we start with cold-start based on similar SKUs (same supplier, same family, comparable technical attributes) and refine as orders accumulate. For new customers we use sector, size and initial-mix profiling. The initial prediction carries a lower confidence flag and updates automatically — the team always knows how much to trust the number.
The first model (typically family-level forecast or catalog classification) reaches production in 8–12 weeks, depending on initial data quality and the pilot scope. Measurable results — reduced forecast error, reduced churn, time saved on classification — show from month three with the model in production and continuous measurement. No first-week promises.
We design for privacy from the start, human control, traceability, usage limits, permissioning and documentation. For sensitive processes, we help assess risk and applicable obligations under GDPR and the EU AI Act.
Every engagement is led personally by one of the partners. If there's a fit, you get a personal first read of your case within one business day — not a canned demo.
