Every night, Acto reads your ERP and CRM data, scores every customer with custom-trained machine learning models and has AI agents check every result for relevance before it reaches your sales team. What comes out is a short list of high-precision signals, each with a one-sentence reason.
Acto is built like a sieve tower. Every order line goes in at the top, each stage measures, scores and enriches, and only a few signals come out at the bottom. Whatever sales reports back flows in again through the return line.
Acto takes what already lives in your systems. No new system of record, no double data entry.
Custom-built algorithms turn buying behavior into product-level KPIs, measured against each customer's own history and against similar customers.
Machine learning models score every customer, from churn prediction to cross-selling. The output is a ranked list per customer, by probability and profit.
In parallel, Acto gathers what's not in the order data. A language model (LLM) processes text like CRM notes.
The agents put model results and context side by side. Whatever isn't worth acting on right now stays in the sieve.
Every signal arrives with its reason right where sales already works, every morning or right before the meeting.
What happens in sales goes into the next nightly run.
Signals are calculated against each customer's own history and, where it fits, against similar customers. A selection per group:
of delivered signals are marked relevant by the sales team at Schäfer Shop .
That's the relevance check at work. Whatever isn't worth acting on never reaches your reps. The list stays short enough that people actually read it.
Order data shows what's happening. The why often lives in visit reports, in your sales team's know-how and in your house rules. Acto brings both together before an alert reaches your reps.
Distributors using Acto grow revenue per rep with more upsell and cross-sell, less churn, and more time in front of customers.
Language models work wherever text is involved: processing CRM notes, in the AI agents' relevance check, and in writing the reason. Whether a customer is slipping or has potential is calculated by machine learning models on your transaction data.
A dedicated machine learning model estimates each customer's probability of churning. It looks at order volumes and missed purchases and measures the customer against their own history and your entire customer base. Before that turns into an alert, Acto checks the context. If the customer announced a vacation shutdown, for example, there's no alarm.
The features are built on wholesale logic: reorder cycles, assortment structure, seasonality. Every customer is measured against their own history and, where it fits, against similar customers in your data, grouped by industry, purchase volume and order frequency.
They reject it and give a reason. The rejection only applies to that one customer, and the reason flows into that customer's next score as context.
The handoff is seamless. Acto keeps running on your current system and gets switched over to the new ERP once the migration is done. No second data project in the middle of your migration.
Your data is stored in Frankfurt, Germany, and processed in compliance with GDPR. Language models run in data centers inside the EU. Predictive models are trained exclusively on your company's business data, with no personal data and no mixing with other customers. The language models are general-purpose models and are never fine-tuned on your data.
Algorithms, machine learning models and AI agents work through every customer before a signal reaches your sales team.
Field reps only see the result: which customer, why, and what to bring up. In Outlook, in the CRM or in the Acto app.
Field sales app
See in 30 minutes how Acto works, with examples from your industry.
