Assess tooling and policy
Map current co-pilot use, security posture, IP exposure, and the gaps between team-level practice and CISO expectations.
/ IT et données /
Développement assisté par IA à l'échelle entreprise, gardes-fous inclus
Du pilote à l'adoption à l'échelle de l'org : évaluation outils, eval harness, prompts gouvernés, déploiement sécurisé et métriques d'adoption—les copilotes IA livrent de la valeur, pas du risque.
The problem
Co-pilot adoption is uneven, ungoverned, and unmeasured
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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.
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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.
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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'.
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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.
Fonctionnement
Tooling, governance, eval, and adoption — wired together
Govern prompts and evaluation
Governed prompt library, evaluation harness for AI-generated code, security and IP gates wired into the SDLC.
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.
Développement assisté par IA à l'échelle entreprise, gardes-fous inclus
Ce qui est inclus
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.
Propulsé par Enterprise AI-SDLC
Résultats
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
Notre façon de travailler
From first call to production—without the usual drag
Assess
Assess
Week 1–3
Map current co-pilot use, stack, security posture, and the top blockers to broader adoption.
Design
Design
Week 4–6
Define eval harness, prompt governance, security gates, and adoption playbook tailored to your org.
Build
Build
Week 7–10
Wire eval into CI, deploy governed prompt library, integrate with IAM/SIEM, pilot in one tribe.
Scale
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.
Aller plus loin
AI in the SDLC, governance, and what to explore next
Commencer
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.