AI Coding Team + Task Automation
This regional home services company had a two-person development team responsible for all internal tools — dispatch software, technician scheduling, customer portals, and inventory management. They had a 6-month backlog of feature requests that kept growing faster than they could ship.
New features were routinely taking 3-4x longer than estimated. The dev team was stuck in a cycle of maintenance tickets and bug fixes, leaving no bandwidth for the strategic features the business needed to grow — like a customer self-service portal and automated dispatch routing.
They had tried hiring contractors, but onboarding took weeks, code quality was inconsistent, and managing external developers ate into the time the team was supposed to be gaining back. The CEO was frustrated: the technology that was supposed to drive growth had become its biggest bottleneck.
We deployed an AI coding team integrated directly with their GitHub repository and Jira board. The orchestrator agent picked up feature requests and bug reports from Jira, the developer agent built production-ready code and created branches and pull requests, the QA agent wrote and ran tests, and the deploy agent merged changes and updated ticket status — all autonomously.
The human developers shifted to a review-and-merge workflow. Instead of writing code from scratch, they reviewed AI-generated pull requests, provided feedback, and focused their creative energy on architecture decisions and complex integrations that required domain expertise.
We also built a priority routing system: urgent bug fixes were handled immediately by the AI, while feature requests were queued and worked through systematically. The dev team got their first clear backlog view in months.
Analyzed the existing codebase, documented patterns, and identified which task types the AI could handle autonomously.
Configured the orchestrator, developer, QA, and deploy agents on their repo. Trained the team on their coding standards, test patterns, and PR conventions.
AI handled tasks with full human review on every PR. Calibrated quality thresholds and refined the agent's understanding.
Agent operated independently on standard tasks. Dev team focused on architecture and complex features.
Backlog
Before
6-month backlog (47 tickets)
After
80% cleared in 5 weeks
Feature Delivery Time
Before
Weeks per feature
After
Days per feature
Dev Team Focus
Before
70% maintenance, 30% features
After
20% review, 80% strategic work
Annual Contractor Costs Eliminated
Before
$95K/year
After
$0
Primary Outcome
80% of backlog cleared in 5 weeks
Efficiency Gain
Feature delivery time reduced from weeks to days
Operational Impact
Dev team refocused on core product work
Revenue Impact
$95K/year saved in contractor costs
Comparable Outcomes
Response Time ↓94%
4 hours → 30 seconds
Lead Volume ↑40%
$180K additional annual revenue
ROI in 30 Days
Avg. client payback period
The AI + human code review model produced higher quality than either alone. The AI was fast and consistent; the humans caught edge cases and architectural issues.
Auto-generated documentation was an unexpected win — the team had virtually no docs before, and the AI documented every feature it built.
Starting with bug fixes built trust faster than starting with features. The team saw immediate value before handing over more complex work.
“We had a 6-month backlog of internal tool requests. Aurograil's coding agent team cleared 80% of it in 5 weeks — features shipped, documented, and merged. My dev team finally focuses on the product instead of internal tickets.”
Marcus C.
Home Services CEO
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Missing 60%+ of after-hours inquiries. Potential clients going to competitors who responded faster.
Top Result
85% of inquiries handled automatically
3 full-time staff members dedicated to phone scheduling. 22% no-show rate costing $15K/month in lost revenue.
Top Result
No-show rate cut from 22% to 11%