Discovery gave us the confidence that 2× faster is realistically achievable on the pilot scope, and we can see the concrete hotspots.
QA Director
The client is a large grocery and retail operator that runs the whole customer experience on one eCommerce platform and sells across several channels. Their internal QA team owned a regression suite of about 5,000 tests across several regions and roles. The business was pushing for market share and needed features shipped faster, so QE had to change rather than hold that back.
Quality was never the issue. Speed was. The client did not want another tool trial. They wanted a phased, evidence-gated program. a1qa was hired to find where the cycle actually lost time, prove a faster mechanism on a small slice, and scale it only where the numbers held.
With no clear ownership, making a release decision took about two weeks, and the suite had become so large that adding even a single test made the next regression cycle slower. The usual answers had all reached their limit. Automate more, add people, run the suite overnight: none of it made a difference anymore. The bottleneck was not the number of tests. It was the shape of the cycle. Heavy UI setup ran before every check. The same configuration-equivalent cases were re-run across region, role, and mode. Triage meant engineers re-reading failures by hand, with no clear owner. And every release decision rested on an estimate instead of data.
AI changes the economics of all four, but only when it is built into the cycle rather than bolted onto the side. The program went after those four costs together: setup, redundant runs, manual failure analysis, and the missing release signal.
The engagement ran fixed-price and phase-gated, so the client could stop at any gate: each phase closed at a gate, and the next one was funded only if the evidence held. Money never ran ahead of proof, and the team co-owned every helper, metric, and triage routine from day one, so the knowledge stayed in-house after a1qa left. The work ran in an Agile delivery model with iterative releases, staffed by a small cross-functional team: a QA lead, two automation engineers, a test designer, and a DevOps engineer for cloud execution.
Hotspot diagnosis. A fixed-scope Discovery tested the hypotheses against the client’s own codebase and pipeline. It then broke the release decision into nine concrete hotspots covering scope definition, automated run, triage, re-run, and decision. Rather than attempting everything at once, a1qa deliberately scoped one slice with the QA lead: a single role-and-region slice. A tightly scoped slice is easier to measure, cheaper to get wrong, and gives the client real numbers before any bigger commitment.
AI test design. An LLM helped with test design. It drafted candidate cases from user stories and existing specifications, flagged gaps in test coverage against the live code changes, and generated realistic test data for multi-region, multi-role scenarios. A QA engineer reviewed every AI-drafted artifact before it entered the suite, so test coverage grew where the risk actually sat.
AI test automation. Slow UI setup steps gave way to AI-staged API preconditions that put the system into the right state in milliseconds instead of minutes. The one-time setup each run pays before it can start was shared across grouped tests, runs went parallel across every region-role-mode combination, and self-healing locators absorbed routine UI changes, so the suite stopped failing because of them. What the team got was a leaner, faster automation layer they could maintain on their own.
AI-assisted triage and failure classification. Triage was the biggest hidden cost. a1qa gave every failure a named owner at each stage and trained classification on failure signatures to sort each one into environment, test, or product. Detection of flaky tests reduced false positives, while automated re-runs handled environment-related failures. Engineers stopped re-reading logs and went back to fixing product issues.
Quality decision intelligence. Every release decision was linked to a specific build and stored for auditability. Instead of relying on estimates, teams used test and quality data. For any build, leadership could see what passed, what was deferred, and why.
Cloud execution at scale. The suite ran on elastic cloud infrastructure across web and mobile, covering the delivery and checkout flows. a1qa dropped configuration-equivalent re-runs, split the business-critical suite, so it fired only on relevant client-version events, and scaled capacity on demand for parallel runs. A fixed nightly window became throughput the team could turn up when a release needed it.
