AI-Driven Development (Contract Development)

Turn the speed AI delivers into a system you can operate.
Keep the speed of AI-driven development while automating and standardizing code quality, reviews, merges, and CI/CD.

The challenges of AI-driven development appear a few months later

With today's AI, a working prototype can be built in three days. The real issue comes after that.
When a system built in three days needs to be expanded three months later, teams end up buried under AI-generated duplicate code, unused dependencies, and giant functions that no one can read.
The problem is not that the work was left to AI. The problem is that there is no governance built on the assumption that AI will do the work.

A diagram showing a prototype built with AI in three days, and the same system three months later, buried under duplicate code, unused dependencies, and giant functions

What goes wrong

Code Quality Breakdown

Duplicate code and bloated dependencies, implementation without specifications, deprioritized testing, and suppression comments left behind

Process Breakdown

Unreviewable pull requests, committing directly to main, broken merge operations, and hotfixes that create the next incident

AI-Specific Issues

Overconfidence in AI, loss of context, failed pair programming, and secrets in commits

What is actually happening in development that uses AI

Duplicate code and bloated dependenciesBecause AI writes from scratch each time, the same functionality appears in multiple places and unnecessary libraries continue to accumulate
Implementation without specificationsTeams build something that works for now, and the documentation becomes inaccurate by the next day
Testing is deprioritizedAI can write working code, but tests are postponed and regressions are discovered in production
Suppression comments left behind// eslint-disable and # noqa are added as temporary suppressions and remain six months later
Committing directly to mainNo review and no automated verification. Production releases depend on luck
Broken merge operationsSquash, merge, and rebase are mixed, making the history unreadable. AI can merge its own pull requests as-is
Hotfixes create the next incidentCI is skipped because the issue is urgent, and the fix causes another failure
Overconfidence in AIAI's confident mistakes, or hallucinations, are merged without verification, and teams realize in production that no one had actually read the code
Failed pair programming"Pair programming with AI" becomes AI writing code while humans simply approve. Roles and handoff rituals are never defined
Secrets in commitsAPI keys and connection strings are casually committed, and apologies in Slack plus emergency key rotations become routine

What we set up during adoption

Depending on your project's situation, we design and put in place AI development rules, quality gates, CI/CD, and review operations.
Below are typical areas of support.

AI development rule design: Define the scope handed to AI, prohibited actions, and checks required before implementation. Prevent duplicate implementations, unnecessary dependencies, and leftover debug code
Development process design: Standardize roles for AI and humans, review criteria, and handoff methods. Design how pair sessions start, end, and are recorded
Quality gate setup: Automate pull request quality checks, duplicate code detection, and unused dependency detection. Continuously monitor complexity, oversized pull requests, and suppression comments
CI/CD setup: Prepare staging, automated tests, smoke tests, and production approval flows. Document decision criteria and safeguards for hotfixes and reverts
Technical debt management: Design weekly code audits, improvement priorities, and cleanup pull request operations. Make future maintenance costs visible and reduce them continuously
Template preparation: Turn decision criteria for pull requests, issues, hotfixes, and reverts into templates. Turn unwritten rules that depend on specific individuals into a format the team can reuse

Actual deliverables are designed individually according to your repository structure, development team structure, existing CI/CD, and AI tools in use. When necessary, we prepare AI rule files, pull request templates, GitHub Actions, technical debt management documents, and related materials.

Our services

Using the AI-Driven Development Framework, TIGEREYE provides the following services.

Support for adopting AI-driven development
Assessment of existing systems
Quality assurance process design
Development guideline creation
CI/CD setup support
Contract development through AI-driven development
Ongoing hands-on support for enterprises

Three quality areas that make AI development sustainable

Code Quality

Continuously detects duplicate AI-generated implementations, unnecessary dependencies, and growing complexity to prevent maintenance costs from increasing.

Process Quality

Standardizes review criteria, merge conditions, and hotfix and revert decisions so the whole team can maintain development quality.

Operational Quality

Automates CI/CD, staging, smoke tests, and production approval to enable safe releases.

Support quality management with both people and AI

The issue is not that AI development is too fast.
The issue is that teams are still trying to manage AI-generated code quality at human speed.
The AI-Driven Development Framework is a system for automating and standardizing quality management itself.

It only works when all five elements are in place: three layers of code quality (prevention, detection, cleanup) and two pillars of process quality (pairing and merge gates).

Overall structure of the AI-Driven Development Framework: three layers of code quality (prevention, detection, cleanup) and two pillars of process quality (pairing protocol, merge gates)

Manage development from design to merge

Prevention layer: Define rules before AI writes code. CLAUDE.md and AGENTS.md prevent duplication, unnecessary dependencies, and debug code from being introduced.
Detection layer: CI automatically detects dead code, unused dependencies, giant functions, and duplicate code before review.
Cleanup layer: Technical debt grows when left unattended. Weekly audits and cleanup pull requests reduce it continuously.
Pairing: Quality cannot be guaranteed when AI writes and humans only approve. Roles, start and end procedures, and handoffs are turned into a protocol.
Merge gates: Not everyone should be able to merge into main at any time. Merge conditions and responsibility are made explicit.

AI-powered quality assurance process

This framework emphasizes not only having AI write code, but also having AI verify quality.

Multiple AI models review the implemented code, continuously checking for design inconsistencies, gaps in exception handling, performance risks, security risks, and more.
AI also automatically generates tests for normal, abnormal, and boundary-value cases, estimates the causes of failures through log analysis, and suggests ongoing refactoring.
In the CI/CD environment, build, lint, unit tests, and integration tests run automatically, so only changes that meet quality standards are merged.

Quality and cost

Why development costs can be reduced

Anything that can be judged mechanically goes to CI.
People can then focus on design and decision-making.
All figures are estimates based on a scenario we modeled.

Time per review
About 1/4
About 60 minutes → about 15 minutes
Cost of incidents caused by releases
50–70% lower
Cost of technical debt
60–80% lower
Ramp-up time for new team members
About 1/2

We can also take on your development under contract as AI-driven development support

We share analysis results and support everything from planning an improvement roadmap to Framework adoption support and development on your behalf.
AI-Driven Development (Contract Development) is contract development priced by deliverables and value.

Support after analysis

Framework adoption support
Existing codebase improvement
Technical debt reduction projects
AI development team setup
CI/CD setup
AI-driven development support

Organizations this framework fits

Good fit

Development organizations already using AI but lacking governance
Medium-scale SaaS products and business systems with tens of thousands to hundreds of thousands of lines of code
Startups that want to move quickly with a small team of two to ten people
Teams aiming to revive or extend the life of an existing product
Development companies that want to balance quality and speed in contract development
IT departments where AI governance has not kept pace with internal tools and digital transformation initiatives

Not a good fit

Organizations with a culture that bans AI
Scripts or proofs of concept that are complete within a few hundred lines
Legacy systems scheduled to be retired within six months
One-off projects or short engagements of one month or less

Case study

An internal AI system used by about 10,000 people, built in about 7 weeks

An enterprise internal system that brings together business systems such as AI chat, facility reservations, visitor management, and authentication.
AI was built into the entire development process, from design and implementation to quality checks. The system is still in use today.

Users
About 10K
Development period
About 7 weeks
About 6 to 14 months with conventional development
Codebase size
About 250K lines
Productivity
4–8x
Approximate, compared with conventional development

The development team had about 30 people. This case was announced in June 2026.

View Case Studies

Frequently asked questions

Can you support monorepos?

Yes. We can also analyze individual subdirectories.

Can you analyze codebases larger than 100,000 lines?

Yes. If more time is required, we will let you know when you apply.

Will it conflict with our existing CI/CD?

No. It is designed to run in parallel with existing workflows. Checks that are out of scope are skipped automatically.

Start by telling us about the challenges you want to solve with AI

Toward workplaces where AI is simply part of everyday work.

Discuss Your Challenges

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