How we build software AI-native
From request to release: AI is embedded across the entire lifecycle — tracker, repository, CI/CD, testing, monitoring. Humans own the decisions. AI handles everything in between. This is our standard and what sets us apart from a typical IT services company.
Automate the entire pipeline,
not just coding
We automate every phase — from intake quality scoring to post-release telemetry. Quality is built into the process, not bolted on by manual QA at the end. That's why we don't sell "developer hours" — we sell a predictable, measurable delivery cycle.
Seven phases. Five human gates
Every task follows the same path regardless of client or repository. Color indicates who owns the phase: humans make key decisions, AI handles the work between gates.
Intake
A client request (Slack / email / Linear) enters the system. AI evaluates the quality of the description, checks the backlog for duplicates, classifies the task (feature / bug / chore), drafts acceptance criteria and an estimate, and compiles clarifying questions — separately for the client and for the internal team.
Plan
For non-trivial tasks, AI prepares a technical plan: approach, which files and areas are affected, architectural decisions, risks, and test coverage strategy. It reads the repository's AGENTS.md to respect conventions and the tech stack. Trivial tasks pass through this phase automatically.
Build
AI implements the task in an isolated git worktree and asks clarifying questions along the way — about implementation, business logic, or architecture — directly in Linear. Writes tests derived from the spec, not from its own code (otherwise tests just validate the bug). Has a token budget per task and escalates to a human if stuck.
Gate
CI/CD runs on the PR alongside a separate AI reviewer with a different lens (security, performance, regressions — not the agent that wrote the code). Coverage, SAST, secret scan, SCA. AI posts a report to Linear: which cases are covered and uncovered, edge cases, risks, which areas and files need attention, and a diff risk map.
QA
The route is determined by risk score, not story points. Effort ≠ risk. Low-risk → AI QA runs test_cases in the browser (Playwright), deploys to staging, checks flows, and leaves only uncovered cases for the human. High-risk (auth / payments / migrations / PII) → always a human QA, regardless of task size. All routes include regression testing on affected areas plus a smoke suite.
Release
Developer sets deploy:prod only if the task requires no manual actions (migrations, scripts) — otherwise deploys manually following the migration checklist. Feature flags decouple deploy from release: code ships to prod "dark" and is enabled by a flag. AI generates release notes; deployment strategy (canary / blue-green) is chosen by risk; every deploy has a rollback plan.
Telemetry
After release, AI runs a prod smoke test, monitors error rates for the first minutes, and triggers an auto-rollback on anomaly. Separately tracks pipeline metrics: cycle time, % AI-authored PRs, rework rate, escape defect rate, how many issues human QA caught after AI. Feedback loops back to Intake.
Seven rules everything rests on
Phases are the mechanics. Principles are the reasons. They separate a real system from "throwing AI everywhere."
Spec-first
The spec is the primary artifact — more important than the code. AC is versioned and approved by a human before work begins. A vague spec means AI fills gaps with guesses about a business it doesn't know.
AGENTS.md in every repo
One file with conventions, the tech stack, and do/don't for the agent. The de-facto standard of 2026 (GitHub, OpenAI, Anthropic, Cursor, Sourcegraph). Cheap to set up, immediately raises quality.
Risk-based routing
QA routing is determined by risk, not effort. A 1-point change in billing is high-risk. Auth / payments / migrations / PII always go to a human — no exceptions.
AI reviews, but not itself
Generator and reviewer are different agents with different lenses. The same agent has the same blind spots. The reviewer looks at security, performance, and regressions.
Seven rules everything rests on
Plus the one clients value most: you see the process, not just the result
Humans own the gates
Five gates belong to humans: AC, plan, PR approval, high-risk QA, prod deploy. AI handles all execution in between. Accountability always stays with a person.
Measure autonomy
Not utilization — but % AI-authored PRs, eliminated review hours, cycle time, and escape defects. This is our proof to the client and our internal improvement loop.
Security by default
Webhook signatures, execution isolation per repo, secrets outside of code, audit log. For FinTech / EdTech clients this is the first question — we have the answer ready.
Transparency by default
AI reports, deploys, and metrics are visible to the client in Linear and Slack in real time. You watch tasks move through the pipeline — not just a demo at the end of the sprint.
Typical services company vs Aimeice
This isn't "the same process, just faster." It's a different operating model.
What we measure and show the client
You see the same dashboards we do. Delivery metrics are part of the collaboration, not internal reporting.
One feature — multiple repositories
The rule: one issue = one repository = one PR. Anything touching backend, frontend and website at once becomes a parent issue with sub-issues — one per repo. The parent carries the business value; sub-issues run the pipeline
Contract — at the parent's Plan phase
Before splitting, we lock the interface between repos: API schema, types, feature-flag names. BE / FE / WEB build in parallel against the contract. A mid-flight contract change sends the parent back to Planning — never a silent fix in one of the PRs
Deploy order
Backend ships first — backward-compatible, behind a disabled feature flag. Then frontend and website. The flag turns on once every part is in prod — no repo ever blocks another's deploy
Two-level QA
Sub-issue: verify its own part against the contract — AI can do this. Integration QA happens only on the parent, on stg, once all parts are ready. If any sub-issue is risk:high, the parent inherits it and goes to a human
Released — once per feature
The parent closes when every part is in prod and the flag is on. Release notes and the client update are one message, from the parent. Clients see features, not PRs
How not to end up with 100 issues In Progress
AI removes the limit on starting work — but the gates (humans) remain the limit on finishing it. Without rules, the pipeline just piles up queues in front of every gate
WIP limits at the gates
In Review ≤ 2 per dev · QA ≤ 3 per engineer · Ready to Deploy ≤ 5 per team. Limits sit on human-gated phases, not AI phases. Column full — you don't pull new work, you clear it
Agent concurrency cap
Cyrus runs at most 3–5 build issues at a time. "Dump 30 issues on it overnight" = a review queue that eats the entire gain and makes review shallow
Stop starting — start finishing
A dev's morning order: review others' PRs and answer the agent → own code → only then pull new work from Todo. Priority goes to moving issues right-to-left across the board
Aging alerts instead of standups
n8n checks Linear daily: an issue stuck 24+ hours in front of a gate → ping the gate owner in #ai-pipeline. The queue is a metric you see, not something you recall
Batch the gates
The PM approves ACs twice a day in fixed slots, not one issue every hour. Gates are cheap when batched and ruinous for focus when drip-fed
Clients see parents
Sub-issues are internal mechanics. Client reporting shows features: "3 in progress", not "10 issues". Less noise — more trust
Week 1 — foundation and intake
Goal: Linear becomes the pipeline's source of truth, and every new request enters through AI intake
Week 2 — execution and control
Goal: the first issues run the pipeline end-to-end, and gate queues are visible and managed
It's a system.
Not promises.
We don't sell developer hours. We sell a measurable delivery cycle — with human accountability at every gate and AI speed in between.
Ready to build together?
Let's discuss how AI-Native Delivery can accelerate your delivery and reduce defects in your project