EquAI Factory

Offer · Launch one company-controlled system

Sovereign Pilot Build

A build engagement for teams that need one AI workflow live in private cloud, VPC, or on-prem with open models, human review, monitoring, and post-launch tuning.

See if your workflow is a fit
  • 6 to 8 weeks
  • Private cloud, VPC, or on-prem
  • Human review included

Offer at a glance

6 to 8 weeks

Sovereign Pilot Build

Launch one company-controlled workflow with human review built in.

Best when
You know the workflow and need a company-controlled deployment live in about 6 to 8 weeks
From your side
A business owner, an active reviewer, and a technology owner for systems and hosting.
Hosting setup
Private cloud or VPC deployment for teams that need company-controlled hosting
Model approach
An adapted or fine-tuned open model aligned to the workflow
In scope
One important workflow in production with scoped automation and a named human checkpoint
Reduced dependence
Reduce exposure to third-party API availability incidents in critical workflow paths

Built to make company-controlled AI real for one important workflow.

What is included

What you leave with

Clear hosting, model, review, and handoff decisions.

  1. 01

    One deployed company-controlled workflow with scoped automation and explicit operator boundaries

  2. 02

    Week 2 architecture pack with hosting setup, model choice, tuning plan, and success criteria

  3. 03

    Pilot launch pack with the human-review path, escalation logic, backup behavior, and operator handoff

  4. 04

    Evaluation harness with success criteria and issue taxonomy

  5. 05

    Monitoring baseline and change log covering quality, latency, review load, cost, and workflow health

Success

What success looks like

  • A live workflow is running under company-controlled infrastructure with humans in control
  • Leaders can see quality, review, throughput, and cost signals in one place
  • The team has a clean path to tune, expand, or stop based on evidence instead of API dependency momentum

Timeline and terms

6 to 8 weeks

Scoped to your workflow and hosting setup after the first review.

Buying conditions

When this is the right controlled first move

Use the offer that answers the next workflow, hosting, and review question.

Best fit

When to buy this

  • You know the workflow and need a company-controlled deployment live in about 6 to 8 weeks
  • The workflow has clear owners, reviewers, source systems, and a viable private deployment path
  • The team wants one production system before it commits more critical work to API vendors

Not a fit

When the workflow should wait

  • The workflow is still ambiguous and should be narrowed before anyone builds
  • There is no real backup path, reviewer checkpoint, or success criteria for the first deployment
  • The team cannot yet support the scoped systems access or infrastructure required for a controlled rollout

Buyer

Who usually owns the purchase

  • AI platform teams that already know the first workflow and need it deployed under company control
  • Operations leaders who need a production system under company control, not another API-based proof of concept
  • Organizations that need one governed deployment before expanding internal AI adoption

Rollout cadence

How the work moves

How the 6 to 8 weeks unfold, and what we need from you.

First two weeks

  1. Confirm workflow scope, owners, hosting setup, and success criteria

  2. Lock the model choice, adaptation plan, human review steps, and backup behavior

  3. Present the week 2 architecture pack so leaders can approve the build before it touches real work

Required from your team

  • One accountable business owner and one active reviewer or approver
  • One technology owner who can support the private deployment path and serving boundary
  • Access to representative inputs, current systems, and the rules the workflow actually depends on

Pilot rollout at a glance

From workflow review to live pilot

About 6 to 8 weeks, with the build approved at week 2 before it touches real work.

Weeks 1 to 2
Workflow map, owners, hosting fit, model choice, human review steps, and success criteria
Weeks 3 to 6
Build, evaluation, and the human review flow
Weeks 7 to 8
Controlled pilot release with sampling and overrides
After launch
Monitoring, reviewed updates, and tuning

Hands-on rollout, reviewer handoff, and post-launch tuning are part of the work.

What your team skips

What your team does not have to invent alone

  1. No need for the client team to assemble hosting, adaptation, rollout, and exception design alone

  2. No need to treat API access as the same thing as a company-controlled production system

  3. No need to wait for a broad internal AI initiative before the first important workflow goes live

How it is built

Scope, hosting, and model, spelled out

The decisions this offer makes, so your team can check them before work starts.

  • Scope

    Workflow scope

    • One important workflow in production with scoped automation and a named human checkpoint
    • The systems, policies, queues, and escalation paths required to operate that workflow under review
    • The success criteria, backup rules, and post-launch review loop needed for launch

  • Infrastructure

    How the deployment runs

    • Private cloud or VPC deployment for teams that need company-controlled hosting
    • On-prem deployment when workflow sensitivity or control posture requires it
    • Hosting boundaries scoped to one production workflow before expansion

  • Model approach

    How the model is adapted

    • An adapted or fine-tuned open model aligned to the workflow
    • Evaluation and regression logic tied to the chosen model family
    • Upgrade logic for swapping or improving OSS models without losing the workflow design

  • Dependency reduction

    What external dependence is reduced

    • Reduce exposure to third-party API availability incidents in critical workflow paths
    • Reduce dependence on token economics for recurring high-volume usage
    • Reduce dependence on vendor-controlled migration cycles for a production workflow

Why this offer

Choose this when the buyer needs one company-controlled deployment in production, not another API-first prototype or a broader internal AI initiative.

Compare with API-first AI

Rollout support

  • Embedded support for workflow design, serving rollout, and operator handoff
  • Human review and backup logic built into the deployment plan
  • Post-launch sampling, override review, and issue triage as part of delivery

Relevant workflows

Use cases that fit this offer

Each has visible business pain and a clear reason to bring it under company control.

Next offer · Offer 03

Sovereign AI Scale Partnership

Improve and expand company-controlled AI without losing visibility or control.

Scale with Sovereign AI Scale Partnership

FAQ

Questions buyers ask about this offer

What internal team do we need on our side?

A Readiness Sprint needs one business owner and one technology owner who can decide on hosting. A Pilot Build adds an active reviewer or policy owner, and the team should stay small enough to decide quickly. The first review needs only the workflow shape, volume, hosting constraints, and systems involved; representative inputs come later, at pilot scoping.

How do you handle security review and data handling?

The workflow is designed to run in your private cloud, VPC, or on-prem, with access limited to that workflow rather than your whole environment. Approvals and escalation points are documented before rollout, and prompt, policy, and model changes follow a documented review path. Your own security review still applies.

How do you evaluate whether a pilot is working?

We look at practical operating signals such as quality, overrides, handling time, backlog pressure, exception rates, latency, and recurring cost. A pilot is working when the workflow is genuinely better to run, not just impressive in a demo.

What happens if the model performs inconsistently?

The workflow should have a backup path before it goes live. Reviewers need to be able to override outputs, unclear cases need to be escalated, and the team needs a way to pause or narrow automation if quality drops.

Do you replace our current systems?

No. We usually work with the systems you already have and add an AI layer that supports routing, review prep, or case handling without forcing a full replacement.

Keep reading

Next step

Ready to move this workflow?

Request a review if the workflow is clear. Use the assessment if the team still needs to narrow the first move.

See if your workflow is a fit