AI Engineering Team
Move the product forward.
Not just the code.
Build and improve web, mobile and desktop applications. Bring a high-level idea. The AI Engineering Team writes the specification and delegates the work. The AI QA Team checks it and reports its findings. After approval, the AI DevOps & MLOps Team deploys the update to staging for your review.
Example change / account export
One feature. Three surfaces.
Client idea → specification
Define account-scoped export. Delegate client + API tasks.
Build + developer tests
Platform-aware clients + API. Check access and empty states.
AI QA Team review
Verify the specification. Return findings for fixes + retest.
Approved by the AI QA Team. The AI DevOps & MLOps Team deploys to staging.
Less work lost between roles
Turn the client’s idea into a detailed specification, then delegate tasks with shared interfaces, acceptance criteria and clear owners.
A change you can evaluate
Inspect the revision, test evidence, review findings and known gaps instead of relying on a completion message.
A handoff people can act on
The AI QA Team approves the update or explains what needs fixing. The AI DevOps & MLOps Team deploys approved work and shares staging access and deployment checks.
Web + mobile + desktop
Build for the way
your customers work.
A browser, a phone and a desktop have different constraints. Share services and code where it makes sense, while designing and verifying the experience for each supported platform.
Web applications
Build responsive web apps, customer portals and internal tools. Connect accessible interfaces to APIs, authentication and data, with browser behavior and performance in view.
Responsive UI + browser journeys
Mobile applications
Develop iOS and Android apps with native or cross-platform approaches suited to your product. Plan device permissions, offline states, synchronization and app-store release requirements.
iOS + Android
Desktop applications
Create Windows, macOS and Linux apps around the operating systems you support. Address local files, system permissions, packaging, signing and update behavior as part of the change.
Windows + macOS + Linux
Useful work, deliberately bounded
Choose the next change.
Make the scope explicit.
Start with a piece of product work that has an owner and an observable outcome. The team works through your repository conventions and existing system, not around them.
01 / Change type
Ship a focused feature
Turn a web, mobile or desktop requirement into interface, API and data changes with explicit acceptance criteria and contracts.
Feature brief + tested change
02 / Change type
Fix a reproducible bug
Capture the failing behavior, isolate the cause and add regression coverage. Explain why the fix addresses the defect, not just its symptoms.
Reproduction + regression test
03 / Change type
Refactor without losing the contract
Improve a bounded part of the codebase while preserving agreed behavior. Document trade-offs and use characterization tests where coverage is missing.
Design rationale + behavior evidence
04 / Change type
Upgrade with a recovery path
Assess dependency changes, compatibility and data migrations. Prepare verification, configuration changes and recovery instructions before a release decision.
Compatibility review + migration plan
From client idea to staging
Each step leaves evidence.
Each handoff has an owner.
You supply the product idea, not a finished technical specification. The AI Engineering Team develops the detail and assigns the work. The independent AI QA Team checks the result. The AI DevOps & MLOps Team deploys only the approved update.
- 01Client + Engineering Lead
Start with the client’s idea
The client shares a high-level idea, plan or desired outcome. Engineering clarifies users, constraints and open questions without requiring the client to write a technical specification.
Product intent + clarified constraints
- 02Engineering
Specify and delegate
Create a detailed specification: behavior, acceptance criteria, interfaces, platform constraints, security and data changes. Clarify material product decisions with the client, then delegate scoped tasks to engineering specialists with owners and dependencies.
Detailed specification + delegated task plan
- 03Engineering
Implement + developer tests
Specialists implement their assigned tasks against the shared specification. Coordinate interfaces and run unit, component, integration and contract tests as relevant; attach actual command results.
Revision + draft PR + test evidence
- 04Engineering
Review, fix, re-check
Review the design, code and tests. Return findings for fixes and re-check the changed revision. Failed checks remain visible.
Review disposition
- 05AI QA Team
AI QA Team review
The AI QA Team checks the candidate against the specification and acceptance criteria, then approves the staging handoff or rejects it with detailed findings. Engineering fixes rejected work; the AI QA Team independently retests the new candidate.
AI QA Team decision + evidence
- 06AI DevOps & MLOps Team
Deploy to staging
After approval by the AI QA Team, the AI DevOps & MLOps Team deploys the approved candidate to staging within configured permissions, checks deployment health and shares the staging URL, relevant mobile or desktop test builds and results with the client. Your authorized human owner decides on merge and any later production release under your policies.
Staging URL + revision + deployment checks
Independent work can run in parallel. Shared interfaces, dependent changes and unresolved findings need coordination, not a race to mark tasks complete.
Engineering responsibilities
Specialist judgment.
A shared definition of done.
An Engineering Lead keeps scope, dependencies and acceptance coverage connected. The lead delegates scoped tasks to specialists, coordinates dependencies and brings their work back into one reviewed candidate for the AI QA Team.
Engineering Lead
Turns the client’s high-level idea into a detailed specification, delegates work to engineering specialists and tracks dependencies and acceptance coverage. Brings product questions to the client and coordinates fixes.
Scope, ownership and delivery package
Software Architect
Maps affected components and interfaces. Weighs design alternatives, non-functional requirements and migration risks before implementation expands the change.
Architecture decisions + verification plan
Web, Mobile & Desktop Engineer
Builds client applications around your design system, target platforms and repository conventions. Covers navigation, loading, errors, accessibility and device or OS interactions with developer tests.
Client changes + platform-aware tests
Backend Engineer
Implements services and API contracts, including validation, authorization boundaries and failure handling. Adds unit, integration and contract tests for affected behavior.
Service changes + API test evidence
Database Specialist
Reviews schema changes, queries and data integrity. Plans migration sequencing, compatibility and recovery, with checks appropriate to the affected data.
Data changes + migration instructions
Code and Security Reviewer
Challenges design, correctness, authorization, data handling, dependencies and test quality. Records findings for fixes and re-checks rather than treating generated code as accepted.
Review findings + disposition
Three connected AI teams
Specify. Build. Verify. Stage.
Keep ownership clear.
Three separate teams share one delivery flow. The AI Engineering Team specifies and builds the change. The AI QA Team approves it or returns detailed findings. The AI DevOps & MLOps Team deploys approved work to staging. Client acceptance and production authorization remain distinct decisions.
Three teams. Distinct responsibilities.
Delivery workflowThe client brings a high-level idea or plan. Engineering creates the detailed specification, clarifies product decisions with the client and delegates implementation within its own team.
01 / Specify + delegate + build
AI Engineering Team
Client idea → detailed specification → delegated tasks
- Implement, test and review specialist contributions
- Candidate revision + specification + test evidence
- Fix rejected work and return a new candidate
02 / Approve or reject staging handoff
AI QA Team
Specification → independent checks → explicit decision
- Approve the tested candidate for staging
- Reject with detailed findings and reproduction evidence
- Independent retest after the AI Engineering Team fixes the issue
03 / Deploy the approved update
AI DevOps & MLOps Team
AI QA Team approval → staging deployment → client review
- Approved revision + environment configuration
- Deploy to staging and verify deployment health
- Share staging URL, revision and check results
AI QA Team findings → AI Engineering Team fixes → AI QA Team retests. Rejection includes detailed findings. The staging handoff stays blocked until the AI QA Team approves the new candidate.
Staging is not production approval. The client reviews the staged update. Merge and any later production release follow separate authorization and repository policies.
The delivery package
Review the change.
Keep the reasoning.
A PR is the starting point for a decision, not the whole handoff. Keep evidence attached to the revision and environment it actually describes so the next owner knows what was checked and what remains open.
01 / Handoff
Change and decision trail
The client’s original idea, detailed specification, delegated responsibilities, revision or PR, acceptance coverage, architecture decisions and unresolved questions.
02 / Handoff
Evidence you can inspect
Developer test commands and results tied to the revision and environment, review findings and fixes, the AI QA Team’s decision, detailed findings and known gaps.
03 / Handoff
Operational handoff
A candidate approved by the AI QA Team, with configuration, migration and recovery instructions for the AI DevOps & MLOps Team, followed by staging access, the deployed revision and health-check results for the client.
A blocked check stays blocked. An untested path stays untested. Your owner can request changes, accept an explicitly documented risk or defer the release. Agent completion is not acceptance.
Controls inside the engineering work
Work in your system.
Respect its boundaries.
Agree on repository access, protected branches, sensitive data and test environments before work begins. Keep credentials out of source, use only necessary permissions and escalate unclear requirements or authority instead of guessing.
Before the first change
Questions from
engineering leaders.
Scope, developer tests, review, permissions and the decisions that stay with your people.
What is an AI engineering team?
The client starts with a high-level idea or plan. Engineering creates a detailed specification, delegates work across architecture, web, mobile, desktop, backend and data specialists, and reviews the implementation with developer tests. The independent AI QA Team approves or rejects the staging handoff with evidence. The AI DevOps & MLOps Team deploys the approved candidate to staging for client review. Production release is a separate authorized decision.
Can the team build web, mobile and desktop applications?
Yes. Scope can cover responsive web applications, iOS and Android mobile apps, and desktop applications for Windows, macOS and Linux. Choose native or cross-platform approaches based on user experience, device integration, performance and maintenance needs. Agree on supported platforms and versions first; sharing code does not replace platform-specific testing, packaging or release requirements.
What work is a good fit?
Bring an idea for a feature, a reproducible bug, a bounded refactor, a dependency upgrade or a planned migration. Engineering develops the specification and acceptance criteria with the client. Broad platform rewrites and ambiguous product decisions need discovery and decomposition before implementation; a larger brief is not permission to change everything.
Does Engineering write tests, or is that the AI QA Team's job?
Engineering owns developer tests: unit, component, integration and contract tests as relevant to the change. The separate AI QA Team independently verifies acceptance criteria, explores risks and reports defects. Engineering fixes those defects and the AI QA Team retests the candidate revision. Neither code review nor a passing unit suite replaces this independent assessment.
Can the team merge code or deploy to production on its own?
No self-approved merge or production deployment. Your authorized human owner approves merge and production release under your repository and environment policies. The AI DevOps & MLOps Team owns environments and operational delivery within that authorization. Preparing a PR, passing checks or completing an agent task is not release permission.
What do we need to provide?
Bring a high-level idea, business goal or product plan, relevant repositories and setup instructions, known constraints and a client contact who can resolve product questions. Engineering develops the detailed specification and acceptance criteria rather than expecting you to write them. Grant scoped access and identify sensitive data, protected branches and test environments. Missing access remains an explicit blocker.
How are security concerns and review findings handled?
The reviewer checks authorization, validation, data handling, dependencies and test quality alongside design and correctness. Findings go back for fixes and re-checks; unresolved risks stay in the delivery package for a human decision. Security review reduces risk but is not a guarantee of secure or defect-free software, a certification or a substitute for specialist assessment when required.
What happens when tests fail or the AI QA Team finds a regression?
Record the failing command, environment, revision and reproduction evidence. Engineering investigates and fixes the issue, then reruns relevant developer checks and returns the new candidate to the AI QA Team for independent retesting. Blocked or unrun checks remain visible. A finished task does not override failed acceptance criteria or authorize a release.
What will our team receive at handoff?
The specification, implementation revision or PR, developer test results, review disposition and the AI QA Team’s decision with findings. Rejected work returns to Engineering, followed by independent retesting by the AI QA Team. After approval, the AI DevOps & MLOps Team deploys the tested candidate to staging and shares its URL, revision and deployment checks for client review. Configuration, migrations and recovery instructions travel with the package; production release remains a separate authorized decision.
One brief. A reviewable change. Clear ownership.
Give the next change
a team, not just a prompt.
Bring your product idea or high-level plan. Follow the AI Engineering Team’s work through specification and implementation, independent approval by the AI QA Team and staging deployment by the AI DevOps & MLOps Team.