Stanley cuts supplier scoring from 2 days to 1 minute with AI

Before Pipefy, Stanley’s credit and risk analysis was tied up in manual steps and spreadsheets that didn’t talk to each other. Supplier records lived in Excel, with no standard format and no way to trace them, so every new entry left room for rework and inconsistencies.

The bottleneck was time. A full credit and risk analysis cycle took as long as 25 days, and a big share of that fell on the buyers, who calculated each supplier’s score by hand. That step alone burned through as much as two days of skilled work per analysis.

The credit reports moved at the same pace. The finance team needed about 20 minutes to put each one together, a cost that piled up across hundreds of cases and made it hard to tell where any given request was stuck.

The turnaround started by rebuilding the process inside Pipefy. Supplier registration left the spreadsheets behind and moved into a structured workflow, with data validated at the source and a complete history behind every request. On that standardized base, Stanley built a risk matrix that scores each supplier automatically. What once depended on the buyers’ manual review became a calculation the platform runs on its own, applying the same criteria the same way every time.

The AI layer is where the real jump came. Five AI agents went into production in the credit analysis workflow. They read and structure data from credit bureau reports such as Serasa, interpret documents and images, and write the credit reports for customers and suppliers without a person doing it by hand. In the most critical steps, each agent carries a confidence score. Anything above the set threshold moves ahead on its own, while cases below the cutoff go to a person for review.

Approvals now run through multiple levels in one place, and notifications reach the right owner at the right moment. With traceability from end to end, Stanley can see in real time exactly where each request stands. In the first phases of adoption, the company kept people validating the agents’ decisions, tuning the models inside the platform until they hit the accuracy needed to run with full confidence.

Read together, the numbers point to more than isolated wins. They mark a step change in how credit gets handled. A process that once leaned on manual effort and individual know-how now runs in a standardized, auditable, and scalable way, backed by automation and an AI layer that Stanley has publicly claimed as proof of its pioneering edge.

The effect is structural. By reclaiming the equivalent of dozens of professionals and cutting the cycle by nearly three quarters, Stanley can now analyze more credit with the same team, and the decisions land faster, hold up more consistently, and stay traceable from start to finish.

Founded in 1913 by inventor William Stanley Jr., Stanley changed how people drink by creating the vacuum-insulated steel bottle. Now part of PMI Worldwide and headquartered in Seattle, USA, Stanley 1913 has become a global lifestyle and hydration phenomenon. Present in dozens of countries, the company runs a strong commercial operation across a range of markets, with rapid growth and an established presence in Latin America, including Brazil. In that operation, the company modernizes its processes and broadens its use of artificial intelligence across its business.