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Let AI do the engineering work. Keep the decisions with your team.
Most teams stop at an AI coding assistant. Our AI-Driven Development Lifecycle (ADLC) goes further: an end-to-end delivery loop on the tools you already run, where AI agents pick up bugs and test cases from your issue tracker, reproduce and fix them, pass real-service CI/CD quality gates, undergo independent multi-model code review, and leave every merge and ticket closure in your team’s hands.
AI agents do the engineering work at every stage, and your team makes every decision.
Design thinking anchors how we deliver, so we solve for complex, human-centred applications. AI agents and a CI/CD pipeline power every turn of the delivery cycle.
- AI agents: Design · Requirement · Build · QA · Ops
- AI: Knowledge · Skill · Workflow
- CI/CD pipeline: COMMIT → BUILD → UNIT TEST · INTEGRATION → SCAN → RELEASE
Four engineering habits that make it safe
AI Bug-to-Merge & TDD
Agents pick up tickets, reproduce issues, write failing tests first, and open isolated merge requests.
Real-service CI/CD gates
Pipelines run unit tests and acceptance checks against real services—not brittle mocks—on every change.
Independent multi-model review
A second, independent AI model from another provider reviews every AI-authored diff before human sign-off.
Human approval & evidence trail
Docs live beside code; agents post commits, pipeline results, and screenshots back to the ticket while your people approve merges and close work.
From issue tracker to verified merge, on the tools you already run.
We help your team build an AI-driven development lifecycle on your existing issue tracker, repositories, and CI/CD pipelines. AI agents pass the same quality gates as any engineer, a second independent model reviews every change, and your people approve merges and close tickets.
Raise it
A bug or test case is logged in your issue tracker—for example Sanplex / ZenTao, Jira, Linear, or GitLab Issues—with reproduction context and acceptance scope.
Fix it
An AI agent picks up the item, reproduces the issue, writes both the fix and the tests on its own isolated branch, and opens a merge request.
Gate it
Your GitLab CI/CD (or GitHub Actions) pipeline runs unit tests and acceptance checks against real services, not mocks—enforcing the exact same quality gates as human-authored code.
Review it
A second, independent AI model from another provider reviews the change so one model checks another. A person on your team makes the final call and approves the merge.
Close it
The AI posts the complete evidence trail—commit, pipeline result, and verification screenshots—back to the ticket. Your team verifies the outcome and closes it.
Built inside your repositories and pipelines.
We use this setup on our own products and bring it into your engineering environment.
- Assessment of your current development and release workflowAssessment
- CI/CD pipeline and quality-gate design for AI-authored changesCI/CD gates
- Issue-tracker integration from bug report to merge requestTracker loop
- Working rules and playbooks for AI agents in your codebasePlaybooks
- Hands-on coaching so your team can run and improve the system on its ownCoaching
We use this lifecycle on our own products.
We run it ourselves
On MedScribe AI, AI agents write fixes against bugs tracked in Sanplex, CI runs them against a real database and live staging services, a second AI model reviews high-risk changes, and human QA signs off every fix.
Humans stay in charge
AI never merges code or closes work on its own; every change leaves a complete, reviewable evidence trail.
No single-vendor lock-in
We combine models from different AI providers so one independent model checks another.
Built on tools you know
We run GitLab CI/CD and Sanplex / ZenTao today, and adapt the setup to GitHub, Azure DevOps, Jira and your toolchain.
Engineering teams that want to ship more without lowering the quality bar, and teams in regulated industries that need a clear, verifiable audit trail for every change.
Five services, combined to fit the problem.
Readiness and governance
Prioritize high-return workflows & clear risk review
See this service02Legacy integration and agents
Connect current software without disruptive replacement
See this service03AI harness engineering
Confidential data protection, accuracy gates & budget caps
See this service04Custom model fine-tuning
Compact models trained privately on your terminology
See this serviceAI-driven development (ADLC)
End-to-end engineering loop with human release authority
You are hereHow ready is your organization for AI?
24 questions across six pillars. You get a score for each pillar, your strongest and weakest areas, and the services that would close the gaps.
- 01Strategy & use-case readiness
- 02Enterprise architecture & integration
- 03ADLC & engineering practices
- 04Data & LLM training / fine-tuning
- 05AI harness & observability
- 06Governance, privacy & human sign-off
Common questions.
We developed ADLC while building and operating MedScribe AI, our clinical scribe built for Ontario’s health-privacy law (PHIPA), and ZokLens, our AI observability product.
Does ADLC replace developers on our team?
No. AI agents handle ticket reproduction, isolated branch fixes, test authoring, and evidence write-back, while your engineers frame architecture, review changes, approve merges, and close tickets.
Why use a second, independent AI model for code review?
When the same model family both writes and reviews a patch, it shares blind spots. Routing review to an independent model from another AI provider catches logic, security, and contract flaws before human sign-off.
Why run CI/CD acceptance checks against real services instead of mocks?
AI agents can inadvertently author tests whose mocks mirror their own assumptions. Running unit tests and acceptance checks against real services in CI proves the fix holds across actual API, database, and runtime boundaries.
Can we adopt ADLC on our existing issue tracker and CI/CD stack?
Yes. We run GitLab CI/CD and Sanplex / ZenTao today and adapt the workflow to GitHub, Bitbucket, Azure DevOps, Jira and Linear, leaving your team with working playbooks and hands-on coaching to run it on its own.
Let AI do the engineering work. Keep the decisions with your team.
Book a 30-minute call
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