Services
ChatGPT Codex
Cloud agent for tech teams: isolated worktrees, parallel tasks, GitHub or GitLab. We set review guardrails so velocity does not break production.
Contexte
Value proposition
Codex excels at refactors, tests, and long migrations while your developers keep control of critical merges.
We deploy it with review guardrails — complementary to Claude Code or digital consulting depending on your stack.
Capabilities
What Codex unlocks
Disciplined cloud: authorised branches, mandatory checks.
01
Worktrees
Isolated agents: fewer conflicts on your main branch.
02
Long tasks
Background migrations tracked by notifications.
03
PR review
Generated descriptions and tests to speed human review.
04
Quality
Debt alerts, proposed patches — always human-validated.
05
Auto tests
Coverage increased from existing code.
06
Docs
README and release notes kept in sync with code.
Deployment
Our method
Audit
1 week — CI, protected branches, list of first issues suited to agents.
Config
1–2 weeks — GitHub/GitLab, secrets, review policies, and API budgets.
Pilot
2–3 weeks — Real squad; we measure merged PRs vs baseline.
Scale
Ongoing — Team expansion and strengthened guardrails if needed.
Impact
Observed results
PR review time
−25–40%
Reduction on standard PRs (tests, description, small refactors) with mandatory human merge before production.
Parallel issues
2–4×
Cloud agent capacity in sandbox per OpenAI quotas — run on mirror repo, not directly on main.
Measured pilot
4–6 wks
Two sprints with documented merged-PR baseline before deciding to extend to other squads.
Controlled merge
100%
Protected branches and systematic human review — *public SWE-Bench ~72% Verified benchmark: lab context, not a client commitment.
FAQ
Frequently asked questions
Codex, compliance, and costs — pragmatic framework and detailed answers before a pilot on your repository.
Key concepts
Codex vs Copilot: what is the difference?
Copilot assists typing in the IDE — line-by-line suggestions. Codex pushes end-to-end PRs in an isolated cloud sandbox (worktrees), with long background tasks. We reserve Codex for refactors, tests, migrations, and documentation sync — not critical code without review. Both can coexist if perimeters are clear.
Is Codex compatible with a company in Geneva?
We map code flows, OpenAI vendor clauses, and internal IP policy. You decide with legal and IT — we provide the risk matrix and technical guardrails. Mirror branches, secrets outside Git history, and minimal repo access are our prerequisites. Without compliance sign-off, we limit the pilot to non-sensitive code.
Strategy & execution
What happens to our proprietary code?
Repo access follows the OpenAI policy in force at contract time — we translate it without jargon. Minimum: mirror repository, protected branches, no secrets in history. We configure GitHub/GitLab scopes and review policies before the first agent. No automatic merge to production.
How are Codex costs estimated?
OpenAI subscriptions (by plan) + Optinova support for setup, guardrails, and review training. We roughly forecast tokens after a two-sprint pilot on a representative repository. Costs vary with issue complexity and requested parallelism. A usage dashboard avoids end-of-month surprises.
Measurement & follow-up
How does a Codex pilot work?
Week 1: CI audit, protected branches, list of first agent-suited issues. Weeks 2–3: GitHub/GitLab config, secrets, API budgets. Weeks 4–6: real squad, measure merged PRs vs baseline. Documented go/no-go decision — extend or stop. We only scale with strengthened review guardrails if needed.
OpenAI
Launch a Codex pilot
One repository, two measurement sprints, then a go/no-go decision.