AI ASSISTED ENGINEERING

Make Your Engineering Team
AI-Native

AI coding tools are evolving fast. Directors and VPs of Engineering are asking the same question: how do we take advantage of this without generating a pile of unmaintainable code? Compoze Labs brings an opinionated approach — with embedded engineers who make AI-assisted development actually work in production.

Schedule a Free Assessment

Code quality is slipping

AI-generated code ships faster, but introduces subtle bugs and architectural inconsistencies that reviewers miss because it looks clean on the surface.

Security & IP risks are unmanaged

Developers paste proprietary code into public AI tools with no policy in place. Not all AI providers treat your data the same way.

The tool landscape is fragmented

100+ AI coding tools on the market. Your developers use them in silos with no shared standards for prompting, reviewing, or deciding what AI should generate.

Most teams are still at Level 1

Industry research shows 30–50% productivity potential, but most teams are still at the earliest stages — individual experimentation with no structure.

No consistent AI dev standards

No shared approach to AI-assisted code review, no quality gates in CI/CD, and no way to measure whether AI is helping or creating tech debt.

Productivity gains aren't materializing

Without a deliberate adoption methodology, developers spend as much time fixing AI output as they save generating it. The net gain stays close to zero.

92%
of developers using AI coding tools

DX Research

30–60%
potential productivity improvement

Microsoft/GitHub

70%
of orgs lack AI coding governance

Checkmarx

45%
of AI code has security issues

Veracode

We'll Map Where Your Engineering Org Stands & What to Do Next

A 2-week assessment that maps exactly where each team and project sits on the AI maturity spectrum, audits your current tooling and security posture, and delivers a prioritized 90-day roadmap.

compoze labs team working on ai engineering practices

Ad Hoc

Individual developers experimenting with AI tools on their own, with no shared approach to prompting, review, or measurement. This is where most teams start.

No shared standards or governance

Standardizing

Approved tool list in place. Basic policies for AI code review. Starting to measure productivity impact. Beginning to define what "good" looks like.

Initial policies defined

AI-Native

Engineers manage parallel AI agents across tasks. AGENTS.md files, codebase-aware skills, autonomy scoring, and nightly agent runs. Continuous optimization with measurable ROI.

Full agent orchestration

HOW WE WORK

Research → Plan → Implement

We place senior engineers inside your org who specialize in AI-assisted software development. They follow a structured methodology — the same one we use internally — to establish standards, build tooling, and drive measurable productivity gains.

Assess & Plan
A 2-week assessment that maps where each team and project sits on the AI maturity spectrum, audits your tooling and security posture, and delivers a prioritized roadmap.

Per-team and per-project maturity mapping
Tool landscape evaluation (Copilot, Cursor, Claude Code, and others)
Security & data privacy review
Prioritized 90-day roadmap

Embed & Build
Our engineers join your team — standing up AI coding standards, building custom task playbooks and codebase-aware configurations, and running workshops that level up your developers.

AI coding standards & guardrails
Custom skills and task playbooks
Spec-driven development workflows
Tiered code review with AI involvement

Measure & Scale
We track productivity metrics, code quality scores, and autonomy scoring for AI agents — then use that data to scale what's working across your entire engineering org.

Session telemetry & productivity dashboards
Autonomy scoring & evaluation frameworks
Governance framework
Org-wide rollout playbook

AI Coding Standards & Governance

A complete framework for how your team uses AI coding tools: what's approved, how AI-generated code gets reviewed, what can and can't be sent to external models, and how to handle data privacy across providers.

Custom Skills & Task Playbooks

Reusable, codebase-aware task playbooks tailored to your frameworks and patterns. Configuration files (CLAUDE.md, AGENTS.md) that encode your team's architecture decisions so AI agents produce consistent, maintainable code.

CI/CD AI Quality Gates

Automated checks that flag AI-generated code in pull requests, run additional security scans, enforce your AI coding standards, and track what percentage of your codebase is AI-assisted.

Developer Training & Enablement

Hands-on workshops where your developers practice the Research → Plan → Implement workflow on real tasks from your codebase — not generic demos. Covers context management, spec-driven development, and tiered code review.

Practice What We Preach

Our engineers use the same AI-assisted workflows we implement for clients — including custom tooling we've built to track session telemetry, evaluate agent quality, and refine development processes across our own teams.

Embedded, Not Advisory

Our engineers attend your standups, submit PRs to your repos, and use your Slack channels. They're part of your team — augmenting your capacity while your developers absorb the workflows through daily pairing.

Opinionated Methodology

We follow a structured Research → Plan → Implement workflow with task playbooks, codebase configurations, and tiered review processes. On a recent project, this compressed work estimated at 1–2 weeks into 3–4 hours.

Measurable Outcomes

Every engagement includes baseline metrics and ongoing tracking: cycle time, defect rates, AI-assisted code percentage, autonomy scoring, and developer satisfaction. The dashboards show what's actually changing, week over week.

Make Your Engineering Team
AI-Native

AI Coding Tools Are Everywhere. Standards Aren't.

Where the Industry Stands Right Now

We'll Map Where Your Engineering Org Stands & What to Do Next