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MassiveScale.AIMassiveScale.AI
Scale

Enterprise AI Transformation

12–16 weeks

Roll out AI across the whole company. Your team learns to run it all.

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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 Assessment, then the 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

    Architect the platform

    Identity, data, observability, and incident-response patterns standardized once, then reused for every AI use case on the platform, from a single tool to a multi-agent system.

  2. Step 02

    Onboard the first wave

    Two to three use cases land in production on the same rails. Your platform team learns by shipping, not by reading.

  3. Step 03

    Enable the rest of the org

    A self-serve path for the next ten AI projects: forms, templates, golden-path code, an architecture-review cadence that doesn't bottleneck.

  4. Step 04

    Handoff

    The platform, the rails, the runbooks, and the people who know how to use them. We leave when your team ships the next one without us.

What you walk away with
  • A governed AI platform pattern your engineering org runs.
  • Two to three AI use cases live on it, each wired for identity and audit, with a runbook in place.
  • A self-serve onboarding path your platform team operates for new AI projects.
  • An executive-level interactive application that shows where every AI system in the company stands, today.
  • A rollout your people adopt. Enablement and change support across teams, with knowledge captured and shared, so AI sticks instead of stalling after launch.
What your team learns

How to evaluate and onboard the next ten AI projects on the platform without external help. How to brief the board on AI risk in your own language, not a vendor's.

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

One we've done before.

2,000 scientists. 30 days.

A global biotech had never let an outside vendor touch its data. We brought security along, vetted the vendor, and put 2,000 scientists on AI in 30 days. 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.

  • Why not just buy a platform?
    You can. Most platforms cover one or two of the five questions well. The governance pattern needs to span all five, and most of the work is the integration with how your company already runs IAM, data classification, and incident response. We make the pattern explicit so the platform you pick fits inside it.
  • Does this conflict with our cloud provider's AI services?
    No. The five questions are runtime-agnostic. We've done this on AWS, Azure, GCP, and hybrid. Vendor-neutral by design.
  • What about agents the rest of the business already deployed?
    We bring them onto the same rails as part of the rollout. Audit, identity, data boundary, runbook. The pattern works as retrofit and greenfield.
  • Can our security team really approve agents in days, not months?
    Yes, once the rails exist. The point of the platform is that security reviews the platform once, then signs off agents that fit. We design the standard with your security team in the room.

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.

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