Case studies

What the work actually looks like.

Each one is written the way we would want to read it: the situation as we found it, what we changed, the numbers that moved, and the part we would do differently.

Payments processing & financial services
12×
faster regression validation cycle
Turning delivery friction into an executive-ready improvement plan
A US-based payments and financial-services leader engaged a structured assessment of the QA and delivery process behind a strategic internal application - quantifying where the release cycle was actually losing time, mapping how the underlying issues compounded one another, and producing a sequenced, role-tagged improvement plan projected to compress regression cycles by an order of magnitude, reclaim significant QA capacity, raise code coverage to industry-standard levels, and shift quality validation from a single late-stage release gate to every pull request.
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B2B FinTech
50+
person-days per year reclaimed
Why every Mobile performance test crashed - and what we built instead
A B2B technology platform needed to validate application performance before every release - a process that consumed 50+ person-days per year and couldn't catch regressions until the final release gate. The internal team had invested in automation with AI coding assistance - the implementation was well-structured, but every eight-hour endurance test crashed, and no one could explain why. We diagnosed the architectural root cause, built a custom performance engineering platform, and delivered continuous quality intelligence that now validates every code merge.
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Customer Communication Management (CMM) & Payments SaaS
60%
increase in AI adoption across the entire engineering organization
From Quality process assessment to AI-powered transformation: accelerating software quality and speed
A growing B2B software company partnered with us to conduct a Quality process assessment, deploy an AI-driven QA agentic workflow, and build an organization-wide AI adoption measurement system-resulting in measurable gains in speed, quality, and team capability.
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Fintech
2 days
Regression cycle
Six-week release trains, cut to one
Regression ran for nine days and still missed the defects that mattered. We rebuilt the release decision around evidence, and the train left weekly.
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B2B SaaS
900
Tests in the suite
4,100 tests nobody trusted, cut to 900
A 38% flake rate had trained a whole org to hit rerun. We rebuilt the suite around the failures that actually predicted incidents.
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E-commerce
11×
Normal peak held in rehearsal
Peak season, without the war room
Checkout had folded under load two years running. We found what actually broke, fixed the mechanism, and rehearsed it before the season arrived.
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Health tech
81%
Requirements with traceable coverage
AI wrote the tests. We made them count.
An AI-generated suite doubled in a month and caught nothing new. We put review, traceability and a cost ceiling around it until the coverage was real.
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Logistics
1
Release standard across teams
Fourteen teams, fourteen definitions of done
Every team had a quality bar and no two matched, so nothing could be compared or escalated. We built one standard the teams could actually meet.
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Marketplace scale-up
11 weeks
To the first fully automated release
From no QA function to a weekly release
Two years of shipping with no dedicated testing had started to show. We stood up an embedded team without making delivery slower on the way.
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Bring us the messy one.

Tell us what your delivery actually looks like: the slow part, the flaky part, the release nobody quite trusts. We'll say what we would do first, and whether it needs us at all.

Get in touch See the services

What the work actually looks like.

Turning delivery friction into an executive-ready improvement plan

Why every Mobile performance test crashed - and what we built instead

From Quality process assessment to AI-powered transformation: accelerating software quality and speed

Six-week release trains, cut to one

4,100 tests nobody trusted, cut to 900

Peak season, without the war room

AI wrote the tests. We made them count.

Fourteen teams, fourteen definitions of done

From no QA function to a weekly release

Bring us the messy one.