You don't pay for compute. You pay for governed execution — every AI agent run inside defined boundaries, across every team and function, with a traceable record your security and compliance teams can rely on.
Pricing is based on governance metrics: how many teams you govern, what retention you need, what isolation level you require, what policy packs you deploy. Not CPU seconds. Not API calls. The value is in the governance layer — the infrastructure that makes AI adoption safe for every function in your enterprise.
Every tier includes the same core: sandbox definitions, isolated execution, evidence trails, and reviewer workflows. The difference is scale — how many teams, how much retention, how strong the isolation.
For security and platform leaders validating governed AI agent execution before a broader rollout. One team. One function. Prove the value in 90 days.
Outcome: validate governed execution with your security and compliance teams. Decide with evidence.
For a single team — engineering, legal, marketing, finance, or any function — adopting AI agents under shared governance with full evidence retention.
Outcome: one team running agents with full governance, evidence, and reviewer approval — regardless of which function.
For organizations standardizing AI agent execution across multiple teams and functions with centralized policy control.
Outcome: every team runs agents under the same governance framework — engineering, legal, finance, marketing, operations — with centralized visibility and control.
For customers requiring stronger isolation, dedicated execution environments, or account-level separation for regulatory or architectural reasons.
Outcome: governed execution on infrastructure you control, with the strongest isolation available — for every function in the enterprise.
No. Pricing is based on governance metrics — how many teams you govern, what retention you need, what isolation level you require. The compute cost of individual runs is included in your plan. You're paying for governance, not compute.
Yes. The Enterprise Pilot is designed to validate governed execution in 60–90 days with one team. When your security and compliance teams confirm the value, you transition to the governance tier that fits your organization — adding teams, functions, and retention as needed.
Claude Code and OpenAI Codex CLI today. Designed as a cross-vendor execution layer for the broader AI agent ecosystem. Additional agents follow the same governance model — whether your teams use them for code, content, contracts, or financial models.
Your first governed run happens within the first weeks of the pilot. The governance catalog eliminates the cold-start problem: 600+ skills, 17 managed egress profiles, and pre-built sandbox templates mean your teams start in hours, not weeks. We guide the process end-to-end.
Any team using AI agents. Engineering writing code. Marketing generating content. Legal analyzing contracts. Finance building models. Operations automating workflows. The governance model is the same — the sandbox definitions and catalog skills adapt to each function.
ContactLab complements your existing security infrastructure. Audit exports integrate with your SIEM. Evidence trails feed your compliance workflows. Policy packs align with your existing security standards. We don't replace — we extend governance to AI agent operations.
No long commitment. Govern your first AI agent in weeks — whether it's engineering, legal, finance, or marketing. Validate with your security and compliance teams. Decide with evidence.