Centerbase, a legal practice management platform serving 2,500+ law firms, was stuck with 3-4 week manual regression cycles, and a $50K/year AI testing tool that never got past 10% coverage. ThinkSys built a custom Playwright + TypeScript framework, automated the highest-value 30% of the regression suite (300+ end-to-end steps), and wired it into CI/CD. Regression now takes 2 weeks. Critical production bugs dropped from 15-20 per release to 1-2. Total savings: about $120K a year.
At the Glance
Client | Centerbase; legal practice management software for 2,500+ law firms. |
|---|---|
Industry | Legal tech / Enterprise SaaS. |
Challenge | 3-4 week manual regression cycles; a $50K/year automation tool stuck below 10% coverage; critical bugs surfacing at release. |
Approach | Custom Playwright + TypeScript framework, business-value test prioritization, performance-first architecture, CI/CD integration. |
Headline results | Regression: 3–4 weeks → 2 weeks · critical bugs per release: 15-20 → 1-2 · ~$120K annual savings · 85% fewer flaky failures. |

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Centerbase builds practice management software for law firms: matter and case management, integrated billing and accounting, timekeeping, analytics, and a client portal. Over 2,500 firms run their practice on it.
That context matters for QA. Legal software moves money and deadlines. A billing bug isn't an inconvenience; it's an invoice a firm can't send. So every release had to be tested thoroughly, and thorough, at Centerbase's scale, had come to mean slow.
Four things were compounding:
That last one shaped everything. Centerbase wasn't automation-skeptical out of ignorance, they'd paid real money for the promise and watched it miss. Whatever came next had to prove itself with results, not a pitch.
"We were losing competitive edge because our testing cycles were taking 3-4 weeks. Our customers needed faster feature delivery, but our QA process was holding us back from responding to market demands."-VP of Product Development, Centerbase.

Metric | Before | After |
|---|---|---|
| Regression cycle | 3–4 weeks, fully manual. | 2 weeks, roughly halved. |
| Critical bugs reaching production | 15–20 per release. | 1–2 per release (~90% reduction). |
| Automation coverage | <10% (failed AI tool). | 30% of regression suite, 300+ E2E steps. |
| Flaky test failures | Constant false positives. | Down 85%. |
| Annual cost | $50K+ on a tool that didn't work. | ~$120K/year saved ($50K tool eliminated + $45K reduced QA overtime and contractors + $25K fewer incidents and hotfixes). |

The bug number is the one that matters most for a legal platform: going from 15-20 critical production bugs per release to 1-2 changed what a release feels like, for the QA team, for support, and for the law firms on the other end.
Three lessons here travel to any team trying to cut regression time with automation:
This is the same framework-first approach behind our Playwright automation testing services, and the same playbook from our FreshTracks case study, where the starting point was 8,000 bloated manual test cases instead of a failed tool. Different mess, same discipline.
"ThinkSys didn't just solve our automation problem, they transformed our entire QA strategy. The 50% reduction in testing time allowed us to respond to customer needs faster than ever. The $120K annual savings was just the beginning; the competitive advantage we gained is invaluable." -VP of Product Development
"Our development team's confidence in releases has completely transformed. We went from dreading release cycles to deploying with confidence, knowing our automated tests caught issues early." -Lead QA Engineer
"The ROI was immediate and continues growing. We're now able to take on more ambitious product initiatives because our QA process supports rapid development instead of slowing it down." -CTO
Centerbase's problem was never a lack of effort or budget, they'd spent both. It was automation built on promises instead of architecture. A Playwright framework designed around their actual workflows, prioritized by business risk, and integrated into their pipeline halved the regression cycle, cut critical production bugs by ~90%, and saved about $120K a year, while leaving their own team in control of the framework. That last part is the quiet win: this time, the automation didn't just work. It stuck.
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