Developer AI

The problem

Developer AI subscriptions without delivery impact

Organizations buy Cursor, Copilot, or Antigravity seats and expect velocity gains. Without team norms, architecture context, and metrics, usage stays sporadic and leadership cannot justify renewal.

Prompt chaos

Every developer improvises. No shared rules for when to accept AI suggestions, how to review generated code, or what must never leave the repo boundary.

Missing repo context

Agents without architecture docs, coding standards, or ADO work item context produce code that does not match your patterns—and creates review backlog.

Security & IP concerns

Engineering leads block IDE AI because nobody documented data handling, secret scanning, or approved model settings for your compliance scope.

No throughput metric

Leadership asks whether developer AI improved cycle time or defect rate. The answer is anecdotal—not a baseline and a 90-day review.

Our approach

Activate developer AI in real delivery teams

We embed with squads on live work—not a generic “prompt engineering” webinar. Standards, context, and measurement come first.

Baseline

Measure current PR cycle time, review load, and tool adoption. Align with engineering leads on success metrics leadership will accept.

Context

Feed agents architecture decision records, coding standards, and repo structure—so suggestions match your stack, not generic tutorials.

Activate

Role-based playbooks for Cursor, Antigravity, or GitHub Copilot: when to use agents, how to review output, and integration with ADO or GitHub workflows.

Measure

90-day review of throughput, quality signals, and seat utilization. Expand what works; retire patterns that add review debt.

Outcomes

What delivery teams gain

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Team standards & playbooks

Documented norms for AI-assisted development—review gates, test expectations, and approved tool configurations.

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Repo-aware agent setup

Context files, rules, and integration patterns so agents understand your codebase boundaries and deployment constraints.

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Measured throughput

Before/after metrics on cycle time and utilization so engineering and finance can discuss ROI with data, not hype.

AI-accelerated pipeline case study — agent-assisted ops on a lean platform team.

Discuss a developer AI sprint Agents & automation

Turn IDE subscriptions into measured velocity

Start with an AI Activation Assessment to map developer AI opportunities—or contact us if you already have a squad ready for a 90-day activation sprint.

Get an AI Activation Assessment Contact Us