Abstracta Introduces Quality Intelligence to Help Banks Release Software at the Speed of AI

 Abstracta, an AI-powered quality engineering company that helps enterprises release critical software with confidence, today introduced Quality Intelligence as an emerging practice for software quality in the AI era. Quality Intelligence connects systems, context and human judgment to understand the risk of every change and decide with confidence where to invest effort. Co-CEO Sofía Palamarchuk presents the vision and the first version of the Quality Intelligence Manifesto in today’s opening keynote at Quality Sense Conf 2026 in Montevideo, Uruguay.

Abstracta puts Quality Intelligence into practice through Abstracta Intelligence, its enterprise AI platform built on Tero, an open-source agent harness for QA workflows, available on Microsoft Marketplace.

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The Challenge: Quality at the Speed of AI

AI has changed how fast teams build software. Writing code is no longer what slows delivery down; the hard part is making sure that software has the quality the business needs, at the pace it needs it. More code also means more changes, and each change can introduce risk.

Organizations that speed up development while keeping the same validation and control mechanisms end up creating a major bottleneck. In banking, that risk weighs even more, because a software error can affect money, personal data, or thousands of customers. The question for engineering leaders is how to understand risk and make sound release decisions at the same speed they now build.

From Testing to Quality Intelligence

In 2025, Abstracta named the Quality Intelligence Gap: the disconnect between what AI produces and what teams need to understand to make safe release decisions. For years, quality meant testing: build something, then check whether it works. When an organization ships hundreds or thousands of changes, that is no longer enough. Quality Intelligence closes the gap by using everything teams know about their software, from requirements, code, and tests to incidents and production behavior, to continuously understand the risk of each change and whether they have the evidence to ship.

The approach shares the principles of the U.S. Department of the Treasury’s Financial Services AI Risk Management Framework, released in February 2026: managing AI risk according to each institution’s stage of adoption and embedding accountability into AI decisions. The Abstracta Quality Intelligence Maturity Model places organizations on five stages, from exploration and individual adoption to AI-assisted release decisions.

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Quality Intelligence in Practice: Abstracta Intelligence

Abstracta Intelligence helps teams validate changes at the same pace they build them. Its agents, built on Tero, connect to the systems banks already run, from Jira and CI/CD pipelines to AS/400 and core banking platforms, and prepare the release decision:

• System Understanding explains how a feature, flow, or business rule works across docs, tickets, code, and tests.
• Change Impact shows what changed and which areas may be affected.
• Risk Assessment flags risks based on history, defects, weak coverage, and incidents.
• Release Readiness summarizes the evidence for a go/no-go decision that a person signs off on.
• Knowledge Capture turns expert knowledge into reusable, auditable context.

Testing Maturity, DORA metrics, and Release Readiness dashboards give QA, engineering, and leadership a shared baseline for each release, while audit trails, defined roles, and human approval keep people accountable for every go/no-go call.

A Track Record in Banking

Abstracta has spent nearly 20 years improving software quality in business-critical systems, including work with global banks such as BBVA and Santander. With Bantotal’s R&D team and Microsoft’s AI Co-Innovation Lab, Abstracta built a generative AI copilot to help teams understand a core banking system made up of hundreds of thousands of programs.

Across engagements, Abstracta’s approach has delivered a 40% reduction in production defects in under six months, regression cycles cut from days to about 30 minutes, and more than 2,000 hours saved per month through AI agents built on Tero.

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Executive Perspective

Sofía Palamarchuk, Co-CEO at Abstracta:

“Producing software is no longer the bottleneck. AI lets us produce software much faster, but more code also means more changes and more potential risk. In banking, a software error can affect money, personal data, or thousands of customers. The conversation should go beyond how to build faster with AI to how we understand risk and make better decisions at that same speed.”

“Quality Intelligence brings together technical context, business rules, and the knowledge of experienced testers and analysts so teams can base release decisions on evidence. Our framework gives people visibility and context when they need it, and the decision about what ships stays with the people responsible for it. That is how organizations running critical software can adopt AI to ship faster with confidence.”