TeamTechLab

Client Stories

Real transformations, measurable results

Three software development transformation case studies from across Asia-Pacific. Client details are anonymised — the numbers are real.

SaaS Product Company · Malaysia

Broke a 6-week release cycle and eliminated a chronic QA bottleneck

Unnamed client · Annual turnover > $3M USD

Duration

10 weeks

Amount Charged

$32,000 USD

Est. Annual Saving

$240,000

Original Issues

  • Manual QA process taking 2–3 weeks per release — the single biggest bottleneck
  • Test coverage below 15% — every deployment was a gamble
  • Frequent production bugs causing emergency rollbacks and customer complaints
  • Dev team burning out on repetitive testing work instead of building features
  • Backlog of customer-requested features growing faster than the team could ship

KPIs We Agreed

  • Reduce release cycle from 6 weeks to 2 weeks within 10 weeks
  • Automated test coverage to exceed 75% across all critical user flows
  • Production incident rate reduced by at least 60%
  • Zero manual regression testing runs per release

What Was Delivered

  • Release cycle down from 6 weeks to 9 days
  • Test coverage up from 15% to 81% in 10 weeks
  • Production incidents down 74% in the 3 months post-launch
  • Dev team now ships features — zero time spent on manual regression testing
  • Feature backlog cleared 40% faster in Q1 following delivery

How We Saved Them Money

eliminated QA contractor costs + faster feature revenue + reduced incident recovery time

$240,000

estimated annual saving

E-Commerce Platform · Singapore

Made an 800K-line legacy codebase safe to change again

Unnamed client · Annual turnover > $6M USD

Duration

12 weeks

Amount Charged

$38,000 USD

Est. Annual Saving

$190,000

Original Issues

  • 800,000+ lines of undocumented legacy PHP — no one understood the full system
  • Bus factor of 1: a single engineer held all critical knowledge — a major business risk
  • New developer onboarding taking 3+ months before anyone could contribute safely
  • Team afraid to touch core modules — every change risked breaking unrelated features
  • Two failed attempts to rewrite the system had already cost over $120,000

KPIs We Agreed

  • AI-generated documentation covering at least 90% of the codebase within 8 weeks
  • New developer onboarding time reduced from 3 months to under 4 weeks
  • Any module deployable independently with zero unintended side-effects
  • Key engineer dependency eliminated — 3+ team members able to own any area

What Was Delivered

  • Full AI-generated codebase documentation — 94% coverage — live in week 7
  • New developer onboarding reduced from 3 months to 3 weeks
  • All 5 developers can now safely deploy to any part of the system
  • Zero failed deployments due to unexpected cross-module impacts since go-live
  • Key engineer knowledge risk fully mitigated — team no longer dependent on one person

How We Saved Them Money

2 new devs productive 10× faster, eliminated single-point-of-failure risk, avoided third rewrite attempt

$190,000

estimated annual saving

Fintech Platform · Indonesia

Cut code review time by 85% and caught security issues before production

Unnamed client · Annual turnover > $15M USD

Duration

8 weeks

Amount Charged

$29,000 USD

Est. Annual Saving

$280,000

Original Issues

  • Code reviews taking 2–4 days per pull request — blocking every developer on the team
  • Security vulnerabilities discovered in production rather than at review stage
  • API documentation permanently out of date — partner integrations failing as a result
  • Over 40% of developer time spent on non-feature work: reviews, meetings, manual checks
  • Two security incidents in the prior 12 months with combined remediation cost of $85,000

KPIs We Agreed

  • AI-assisted code review turnaround under 2 hours for all pull requests
  • Automated security scanning on every PR — zero known vulnerabilities reaching production
  • API documentation auto-generated and always in sync with the live codebase
  • Developer time spent on actual feature work to exceed 65% (up from ~58%)

What Was Delivered

  • Code review time down from 2–4 days to under 90 minutes on average
  • Security scan runs on every PR — 0 vulnerabilities reached production post-launch
  • API docs now auto-generated on every merge — always accurate, zero manual effort
  • Developer time on feature work up from 58% to 71%
  • Partner integration support tickets down 68% due to reliable, current documentation

How We Saved Them Money

freed developer capacity + avoided security incident costs + partner integration reliability

$280,000

estimated annual saving

AI Outsourcing · App Development · Vietnam

Replaced a planned China outsource team with an AI development workflow — delivered faster for 60% less

Unnamed client · Ambitious startup · Pre-revenue, funded

Duration

11 weeks

Amount Charged

$34,000 USD

Est. Annual Saving

$172,000

Original Issues

  • Planned to hire a 6-person offshore development team in China — estimated $14,000/month for 6+ months
  • Serious concerns about IP protection, code ownership and confidentiality with an external team
  • Previous outsourcing experience had resulted in rework, miscommunication and a codebase nobody internally understood
  • Timezone gaps and language friction meant slow feedback loops and expensive back-and-forth
  • No internal technical staff — founders had no way to validate quality or progress

KPIs We Agreed

  • Deliver a fully functional app MVP within 12 weeks — matching the outsource team's quoted timeline
  • Total cost not to exceed 50% of the offshore team quote ($42,000 ceiling)
  • All code owned, documented and understandable by a future internal hire from day one
  • Founders able to make product decisions directly — no intermediary translation layer

What Was Delivered

  • Full app MVP live in 11 weeks — 1 week faster than the offshore team's best estimate
  • Total cost $34,000 vs. $84,000+ quoted by the China team for equivalent scope
  • 100% code ownership — fully documented, no vendor lock-in, no IP exposure
  • Founders reviewed and approved every feature directly through weekly demos — no translation layer
  • AI-generated test suite included — the app launched with 78% automated test coverage
  • One internal developer onboarded in week 10 — fully productive on the codebase within 5 days

How We Saved Them Money

vs. 12-month offshore team cost including rework risk — app delivered 1 week ahead of the outsource team's quoted timeline

$172,000

estimated annual saving

Sample Deliverable

What a project report looks like

Every engagement includes live progress tracking, milestone reports, and a final handover document.

TeamTechLab · Progress Report · Week 11 of 12

AI-Driven CI/CD & Test Automation

Unnamed SaaS client · Malaysia · Engagement #TTL-2024-07

88%
Complete
On Track
Status
$32K
Engagement Fee
  • 1Eliminate manual QA as a release bottleneck — the team ships on confidence, not hope
  • 2Build a self-sustaining CI/CD pipeline the team owns and can extend independently
  • 3Reduce production incidents caused by untested code paths
KPIBaselineTargetCurrentStatus
Release cycle6 weeks≤ 2 weeks9 days✓ Met
Automated test coverage15%≥ 75%81%✓ Met
Production incidents~8/month≤ 3/month2/month✓ Met
Manual regression runsEvery releaseZeroZero✓ Met
Deployment lead time3–5 days< 1 day4 hours✓ Met
1
2
3
4
5
6
7
8
9
10
11
12
13
Discovery & Codebase Audit
Done
Goals & KPI Agreement
Done
CI/CD Pipeline Architecture
Done
AI Test Generation — Phase 1
Done
AI Test Generation — Phase 2
Done
Integration & QA Validation
Done
Production Pipeline Rollout
Active
Team Training & Handover
Soon
Complete In Progress Upcoming
Kickoff, codebase audit and risk assessment complete
Project goals, KPIs and success criteria signed off
CI/CD pipeline architecture approved by engineering lead
Phase 1 AI-generated tests live — coverage 41%
Phase 2 AI-generated tests live — coverage 81%
All critical user flows passing automated regression
Production pipeline rollout — all environments
KPI sign-off report delivered to client
Team runbook, docs and training complete
Final handover and project closure
Risk

Two legacy API endpoints are excluded from automated test coverage due to undocumented side effects. Manual sign-off required before each release until these are refactored in Q3.

Insight

AI-generated tests identified 14 untested edge cases in the checkout flow that had been silently failing for months. Three were causing data inconsistencies — now resolved.

Recommendation

Schedule a monthly review of test coverage drift. As new features ship, coverage can erode if AI test generation is not triggered on each PR. We recommend adding this as a CI gate.

Recommendation

The team is ready to own this pipeline. We recommend a 30-day post-handover check-in rather than an ongoing retainer — the system is stable and well-documented.

TeamTechLab · Confidential · Engagement #TTL-2024-07 · Week 11 Progress ReportNext update: Week 12 — Final KPI sign-off & handover

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