Your QE Metrics Look Fine. Your Releases Don’t. Introducing ExAite™️ – Intelligent Quality Engineering.

Most enterprises have automated testing. ExAite™ is built to do something harder — to make quality
intelligent, self-improving, and embedded across the entire software lifecycle.

Software delivery has fundamentally changed. Teams now ship in two-week sprints, and business stakeholders
expect release confidence in hours, not at the end of a multi-week regression cycle. Yet most QA teams are
still working from the same playbook: more scripts, more tools, more manual effort to hold it together.

The result is an invisible form of testing debt. Not defects in a log, but delayed releases, QA teams in
permanent catch-up mode, and automation coverage that looks healthy until something critical escapes into
production.

ExAite™ was built to solve the hard problems. Exavalu’s Intelligent Test Engine is a chat-driven, agentic AI
platform that brings quality intelligence to every stage of the software lifecycle from the moment a user
story is written to the first week in production. It doesn’t just run tests. It applies intelligence to
every stage of SDLC.

Quality that Starts at the Source

By the time a defect reaches a test environment, it has already cost something — a developer writing an
ambiguous requirement, a sprint committed on a story that wasn’t ready and so on.

ExAite™ starts upstream.

Before development even begins, ExAite™ analyzes user stories for completeness, validates acceptance
criteria, and calculates a vulnerability score, a risk signal that tells your team which areas need
attention before a single line of code is written. For functional areas with a history of defects, ExAite™
flags them proactively.

In regulated industries like insurance and financial services, where a misconfigured rule can create real
exposure, this upstream intelligence is not a convenience. It is risk control.

Test Cases that Reflect Domain Expertise, not just UI Workflows

Once a story is validated, ExAite™ generates test cases automatically cross-referenced against existing
repositories, aligned to frameworks like Selenium and Playwright, and enriched with deep domain intelligence
built from Exavalu’s expertise across insurance, life and annuities, Guidewire, and Salesforce.

This is the distinction that matters. Generic AI-assisted testing generates scenarios based on what’s visible
in UI. ExAite™ generates scenarios based on what’s known about the business: the edge cases that matter in a
California homeowner policy, the underwriting rules that have historically failed, the workflows that break
under specific data configurations.

A System that Learns from every Sprint, every Release

Most platforms treat a release as an endpoint. ExAite™ treats it as an input.

After every release, ExAite™ analyzes production behavior, evaluates defect validity, identifies test
scenarios that should have caught what escaped, and updates coverage automatically. The next sprint starts
smarter than the last.

This feedback loop separates a quality tool from a quality system. And it fundamentally changes how QE value
is communicated not as pass rates and defect counts, but as production escape trends, release confidence
scores, and efficiency gains: metrics that matter to the business.

Built for Insurance. Engineered for the Future.

Unlike generic AI testing tools that work across any application surface, ExAite™ is specifically engineered
for the insurance ecosystem with deep contextual intelligence for Guidewire and Salesforce environments,
built-in business-rule validation, and defect correlation tuned to the workflows that matter most in
insurance.

This is not a horizontal QE platform adapted for insurance. It is an insurance-native intelligence engine
that knows your domain before it writes its first test case.

ExAite™ is the quality engineering capability at the heart of Exavalu’s transformation platform designed to
guide enterprises through a four-phase journey: from foundational automation to self-sufficient,
continuously improving quality at scale.