For CTOs
Copilot Collections is an AI product engineering framework used daily by 300+ engineers at The Software House across 50+ commercial projects — delivering a 30% average lead time reduction.
- Only 10% of teams see real gains from AI coding tools - the gap is structural, not technological
- Copilot Collections restructures your delivery workflow around AI, rather than adding AI on top of it
- 300+ engineers at TSH use it daily across 50+ commercial projects with great result: average lead time down 30%
- Covers the full lifecycle: discovery workshop → structured backlog → implementation → code review
- ~$50/person/month vs $100 for Claude Code and $200–300 for Cursor
- Open source, MIT licence
The AI productivity gap is real and structural
Gartner found that only 10% of software engineers see meaningful productivity gains from AI coding assistants. The other 90% are using the same tools and getting inconsistent results.
When engineers use AI ad-hoc the results vary by individual. You get pockets of acceleration alongside unchanged delivery speed, inconsistent code quality, and a team that's learned to trust AI selectively and unpredictably.
Copilot Collections is a structured response to this problem. It doesn't replace your existing tools or process. It gives AI a consistent place in your delivery workflow with defined inputs, expected outputs, and human review at every step.
What it actually changes
This isn't a prompt library. It's a framework that restructures how work moves through your team - from raw workshop transcript to production-ready, reviewed code.
The framework covers three delivery phases:
Product Ideation: turning discovery workshops into structured, Jira-ready backlogs. Context gathering that used to take 30–60 minutes takes 3 minutes. Workshop transcripts become fully formed tickets with acceptance criteria, edge cases flagged, and backlog prioritised.
Development: implementation guided by agents that understand your codebase, your Figma designs, and your Jira context simultaneously. The context-switching that fragments developer focus is replaced by a structured research-then-plan-then-implement loop.
Quality: automated UI verification against Figma specs, security review built into every implementation plan, and E2E test suites generated from acceptance criteria. Quality gates that currently happen late in the cycle move earlier, where they're cheap to fix.
The numbers, from production use
These aren't benchmark numbers. They come from 300+ engineers using the framework daily across 50+ commercial projects at The Software House.
| What changes | Measured impact |
|---|---|
| Average lead time | 30% reduction |
| Context gathering time | 60–80% faster |
| Planning time | 50–70% faster |
| UI defects reaching QA | 70–90% reduction |
| Design-to-code accuracy | 95–99% |
| E2E test flakiness | 50–80% reduction |
| Onboarding time for new engineers | 40–60% reduction |
The lead time figure is the headline, but the onboarding number often surprises CTOs most. A structured framework means new engineers aren't learning your way of working from scratch the framework is the way of working.
Who uses it and how
The structured nature of the framework makes it approachable at any level.
Junior and mid-level engineers get guardrails. The structured workflow prevents the most common failure modes: vague tickets turned into over-engineered implementations, missing edge cases, inconsistent code style. AI suggestions are grounded in actual codebase context, not generic patterns.
Senior engineers and tech leads get leverage. Planning and context-gathering compress. Code review becomes structured rather than free-form. The framework handles the scaffolding; judgment stays with the people who have it.
What adoption looks like
The framework is open source and installs against your existing GitHub Copilot setup. There's no new toolchain to procure, no vendor contract, no migration.
A typical rollout at TSH runs in three phases:
- Install and orient (day 1) — engineers install the framework, read the workflow overview, run their first agent interaction on a real ticket.
- First sprint (week 1–2) — one team runs the full Product Ideation → Development → Quality loop on a real project. Friction points surface and get resolved.
- Team-wide adoption (week 3–4) — framework becomes the default way of working. Leads review output quality, calibrate to project standards.
The framework is designed to be self-explanatory for engineers who already use GitHub Copilot.
Why TSH built this in the open
The Software House is a 300-person product engineering firm. We built Copilot Collections to solve our own problem - how to get consistent, high-quality AI-assisted delivery across a large, distributed team working on complex commercial projects.
We open-sourced it because the problem is structural and universal. Every engineering team using AI tools is navigating the same gap between individual capability and team-level consistency. We've done the work to close that gap. Sharing the framework costs us nothing and helps the industry get there faster.
The 30% lead time figure comes from our own delivery data. We have skin in the game.
Evaluate it for your team
Copilot Collections is MIT-licensed and free.
- Read the full documentation - understand the full framework before committing to anything
- See it applied to real use cases - nine scenarios across the full delivery lifecycle
- Get the repo - install it in an afternoon, run it against a real ticket, see what changes
If you want to talk through fit for your team's specific context, reach out to us at hello@tsh.io. I'd love to hear from you.