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Scale

Agentic Trust Accelerator

12 to 16 weeks

Set the patterns once, so every team's AI agents run the same safe way. Your team ships the first use cases with us at their side.

Who this is for
  • You've proven the value with one agent and need to do this across the business.
  • You have multiple teams asking for AI and you need shared rails so it doesn't splinter.
  • You want enablement at scale without giving up the ability to audit any agent in minutes.
And who it isn't
  • You don't have a use case yet. Start with the AI Readiness Assessment, then the AI Production Sprint.
  • You want to outsource the operating model. We design it with you and hand it off.
What we do

A plain timeline.

  1. Step 01

    Set the patterns

    We set one pattern for each of the five questions: Identity Management, Behavioral Monitoring, Data Governance, Segmentation, and Incident Response. Every AI use case on your platform reuses them, from a single assistant to a system of many agents.

  2. Step 02

    Bring on the first wave

    Two to three use cases go live on those patterns. Your team ships them. We pair with your engineers and review the work.

  3. Step 03

    Open it to everyone else

    We set up a self-serve path for the next projects: an intake form, templates, the bar an agent has to clear before it's promoted, and a review schedule.

  4. Step 04

    Hand off

    Your team owns the patterns and the runbooks (the step-by-step guides for when something breaks). We leave when they ship the next use case without us.

What you walk away with
  • The platform pattern your engineering team runs.
  • Two to three use cases live on it. Each one has its own identity and audit trail, plus a runbook.
  • A self-serve path your platform team runs for new AI projects.
  • The interactive final report, with a short executive deck.
  • Training and change support across your teams, so the new way of working sticks.
What your team learns

How to bring the next ten AI projects onto the patterns without outside help. How to explain AI risk to leadership in your own words.

Duration
12 to 16 weeks
Your team
A multi-disciplinary core team you assign for the engagement. We pair with them throughout.
Proof

One we've done before.

About 2,000 scientists. 30 days.

A global biotech had never let an outside vendor touch its data. We brought security along and vetted the vendor. In 30 days, about 2,000 scientists and researchers were using AI. It won the company's top innovation award and grew into a 5,000-license deal.

See all four stories →

Questions we get

Real objections, answered straight.

  • Who writes the code?
    Your team does. We bring the patterns and the templates, and we review what your engineers build. That's how they can run it after we leave.
  • Why not just buy a platform?
    You can. Most platforms cover one or two of the five questions well. The pattern has to cover all five. Most of the work is fitting it to how your company already handles logins and incidents. We make the pattern clear first, so the platform you pick fits inside it.
  • Does this work with our cloud provider's AI services?
    Yes. The five questions are the same wherever your AI runs. We're vendor-neutral, with no platform to sell.
  • What about agents other teams already put into production?
    We bring them onto the same patterns during the engagement. Each one gets an identity, an audit trail, a data boundary, and a runbook.
  • Can our security team really approve agents in days?
    Yes, once the patterns exist. Security reviews the pattern once, then signs off on agents that fit it. We design that standard with your security team.
  • What does it cost?
    We quote it after we scope it with you. It's a fixed scope at one set price. If you bought the Advanced ATF Assessment, the full $5,000 counts toward it when you sign within 30 days of getting your report.

Want to know if this is the right size?

One 20-minute call. We'll tell you straight. If a different engagement fits better, or if we're not the right people at all, we'll say so.