EquAI Factory

Offer · Improve and expand under company control

Sovereign AI Scale Partnership

A 90-day operating model for teams expanding beyond the first company-controlled workflow with open-model upgrades, governance, and repeatable rollout discipline.

See if your workflow is a fit
  • 90-day operating cycles
  • Private cloud, VPC, or on-prem
  • Human review included

Offer at a glance

90-day operating cycles

Sovereign AI Scale Partnership

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

Best when
The first pilot worked and now the question is scale, upgrades, and operational ownership
From your side
An executive sponsor, plus named owners for upgrades, delivery, and operating review.
Hosting setup
Private cloud, VPC, and on-prem patterns reviewed as a portfolio rather than one-off exceptions
Model approach
Upgrade cadence as open-source models improve
In scope
A controlled queue for adding adjacent workflows after the first production signal is clear
Reduced dependence
Reduce long-term dependence on third-party API pricing for scaled workflows

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

    Workflow roadmap with sequencing across adjacent critical workflows

  2. 02

    Governance cadence for model upgrades, policy changes, and control reviews

  3. 03

    Monitoring model covering drift, failure patterns, review load, and cost signals

  4. 04

    Expansion queue with entry criteria, owner mapping, and readiness checks

  5. 05

    Optimization reviews for performance, cost, and new OSS model opportunities

Success

What success looks like

  • New workflows are added through a controlled queue instead of ad hoc requests
  • Model upgrades, governance, and monitoring become part of the operating rhythm
  • The organization can scale company-controlled AI without sliding back into unmanaged external dependence

Timeline and terms

90-day operating cycles

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

  • The first pilot worked and now the question is scale, upgrades, and operational ownership
  • Multiple teams are asking for company-controlled AI at the same time
  • Leadership needs monitoring, governance, model upgrades, and an expansion roadmap

Not a fit

When the workflow should wait

  • The first pilot has not yet produced a clear operating signal
  • There is no owner model for adding workflows through a controlled queue
  • The organization wants expansion without agreeing on change governance, upgrade discipline, and operating review

Buyer

Who usually owns the purchase

  • AI platform teams responsible for model upgrades, serving stability, and additional internal AI workflows
  • Organizations that already have one pilot or clear proof of fit under company-controlled deployment
  • Sponsors who need a repeatable operating model before demand spreads across teams

Rollout cadence

How the work moves

How the 90-day operating cycles unfold, and what we need from you.

First two weeks

  1. Review the first deployment, adjacent workflows, and current governance gaps

  2. Define the expansion queue, upgrade cadence, and owner mapping

  3. Set the first operating cadence for monitoring, regression review, and optimization

Required from your team

  • Executive sponsor alignment across the teams entering the expansion queue
  • Named owners for model upgrades, technical delivery, and operating review
  • Agreement on how new workflows are admitted, reviewed, upgraded, and measured

Partnership at a glance

One operating cycle, repeated

Each 90-day cycle reviews what is live, admits the next workflow, and plans the next upgrade.

Days 1 to 30
Review of the first deployment, adjacent workflows, and governance gaps
Days 31 to 60
Expansion queue, upgrade cadence, and owner mapping in operation
Days 61 to 90
Monitoring, regression review, and optimization across workflows
Next cycle
Re-plan with new open-model options and the next workflows

Expansion happens through a controlled queue, not ad hoc requests.

What your team skips

What your team does not have to invent alone

  1. No need to let each team invent its own model upgrade and rollout pattern after the first pilot succeeds

  2. No need to scale critical AI through disconnected API experiments

  3. No need to separate governance, monitoring, and rollout ownership into different tracks

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

    • A controlled queue for adding adjacent workflows after the first production signal is clear
    • Governance, monitoring, and ownership rules that keep new deployments from becoming vendor or platform sprawl
    • A repeatable review cadence for performance, drift, upgrade readiness, and change control across the workflow portfolio

  • Infrastructure

    How the deployment runs

    • Private cloud, VPC, and on-prem patterns reviewed as a portfolio rather than one-off exceptions
    • Serving and hosting choices revisited as workflow criticality changes
    • Infrastructure decisions tied to governance and operating cost, not ideology

  • Model approach

    How the model is adapted

    • Upgrade cadence as open-source models improve
    • Regression evaluation before switching model families or tuning paths
    • Cost, latency, and quality tuning across the active workflow portfolio

  • Dependency reduction

    What external dependence is reduced

    • Reduce long-term dependence on third-party API pricing for scaled workflows
    • Reduce migration drag when external vendors change model or API surfaces
    • Reduce lock-in by designing the workflow to stay portable across model generations

Why this offer

Choose this when the first deployment worked and the next problem is long-term control, upgrade cadence, and scale, not more abstract platform selection.

Compare with API-first AI

Rollout support

  • Ongoing operating reviews across platform owners, business owners, and delivery leads
  • Governance, upgrades, and monitoring embedded into the expansion path
  • A repeatable way to add workflows without turning new demand into unmanaged sprawl

Relevant workflows

Use cases that fit this offer

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

FAQ

Questions buyers ask about this offer

Which models do you use, and are we locked into one?

We usually start with open models and then decide whether prompting, retrieval, lightweight tuning, or fine-tuning is the right fit. The workflow design and evaluation are built to stay portable, so you can move to a better open model as they improve instead of waiting on one vendor's roadmap.

What happens as open-source models improve?

Your workflow can adopt better open models on your own timeline instead of waiting on a vendor roadmap. The Scale Partnership is built around regression testing, controlled upgrades, and portability.

How do you handle model, policy, or serving changes after go-live?

Prompt changes, policy changes, threshold changes, model swaps, and hosting changes should all follow a clear review process. The team should know who approved the change, what changed, and what needs to be tested again.

Who owns the workflow after launch?

The business owner should still own the workflow result, and the technology owner should still own how the system is hosted and changed. We help set that up, but the ownership needs to stay inside the company.

What happens after the first workflow is live?

After the first workflow is stable, the next step is usually better monitoring, careful expansion, and a decision about whether another workflow is worth adding. The goal is to grow while keeping review discipline and portability.

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