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AI-assisted development at enterprise scale, guardrails included

From pilot to org-wide adoption: tooling assessment, eval harness, governed prompts, secure rollout, and adoption metrics—so AI coding co-pilots ship value, not risk.

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The problem

Co-pilot adoption is uneven, ungoverned, and unmeasured

  • Adoption is uneven across teams

    Some teams use AI co-pilots for everything; others avoid them entirely. There's no shared playbook, no shared metrics, and no shared lessons.

  • No evaluation harness for AI-generated code

    PRs ship code the author didn't fully write. Without an eval harness, the only quality gate is the same overworked reviewer who used to catch bugs alone.

  • Security and IP review bottleneck

    InfoSec wants to know what model saw what code and what was generated from where. Without governed prompts and audit, the answer is 'we don't know'.

  • ROI claimed without baseline

    Leadership hears about productivity gains; engineering teams don't have baselines or telemetry to confirm or correct. The conversation drifts into anecdote.

How it works

Tooling, governance, eval, and adoption — wired together

Step 1

Assess tooling and policy

Map current co-pilot use, security posture, IP exposure, and the gaps between team-level practice and CISO expectations.

Step 2

Govern prompts and evaluation

Governed prompt library, evaluation harness for AI-generated code, security and IP gates wired into the SDLC.

Step 3

Adopt and measure

Adoption playbook with training, coaching, and telemetry—so leadership sees lift with evidence, not anecdote.

Flow adapts to your stack, security model, and engineering culture.

AI-assisted development at enterprise scale, guardrails included

What's included

Everything you needfor governed AI development at scale

Tooling assessment, eval harness, governed prompts, security gates, and adoption telemetry—delivered with engineering owning the rollout cadence and CISO signing off on the controls.

Tooling assessment

Compare Copilot, Cursor, Claude Code, and others against your stack, security model, and team needs.

Eval harness for AI code

Automated evaluation of AI-generated code against test suites, security rules, and policy—pre-merge.

Prompt & policy governance

Versioned prompt library with policy enforcement, IP scoping, and exfiltration controls.

Secure adoption playbook

Phased rollout plan with security review, IP guardrails, and CISO sign-off built in.

Telemetry & adoption metrics

Baseline and ongoing lift measurement—lead time, PR throughput, defect rate—with team-level visibility.

Training & coaching

Hands-on enablement for engineers and engineering leaders to use co-pilots with judgment, not just acceleration.

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Results

What changes when this runs in production

Results vary by stack, team maturity, and current SDLC discipline. We scope honestly before we promise precisely.

20–35%

lead-time reduction on standard PRs

Orientative—depends on baseline maturity and language mix.

Full

audit trail of AI-generated code with governance lineage

CISO-ready

rollout with documented controls and approval

How we work

From first call to production—without the usual drag

Assess

Week 1–3

Map current co-pilot use, stack, security posture, and the top blockers to broader adoption.

Design

Week 4–6

Define eval harness, prompt governance, security gates, and adoption playbook tailored to your org.

Build

Week 7–10

Wire eval into CI, deploy governed prompt library, integrate with IAM/SIEM, pilot in one tribe.

Scale

Week 11+

Roll out by tribe, expand training, tune telemetry, govern policy updates and new tools.

Timelines vary by stack diversity, security model, and tribe count.

Get started

Ready to roll out AI in the SDLC without compliance drag?

No commitment. We start with a scoped session to map your stack, current co-pilot use, and security model.

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