Governance
AI Governance Care
Monthly AI governance support: up to 3 new AI use cases or vendors reviewed, your AI register kept current and a quarterly…
Governance
AI Model Governance
Which AI models get approved for which use is managed on an ongoing basis, with versioning and review cycles kept current.
Approving an AI model once is easy. Keeping that approval current as versions change and new uses appear is where most teams slip. This build runs the ongoing process: requests go to the right reviewer, approved models are registered with version and use, and every approval gets a review date. Your reviewers make every decision, and the records stay in your tools.
- Model requests routed to the right reviewer with context
- Approved models tracked by version and approved use
- Review dates scheduled so approvals never quietly expire
$9,700
20–30 working days
Optional maintenance $485/mo
What it is
AI Model Governance runs the ongoing process of approving AI models for specific uses, tracking versions and setting review cycles so an approval does not quietly go stale as models change. Requests reach the right reviewer in Jira or your inbox, and every decision is recorded.
Why businesses buy it
A model approved last year may now run a newer version, serve a new team or handle data nobody considered. Without a process, nobody notices until something goes wrong or an auditor asks. Reviewing requests by email also slows teams that need answers. This build gives model approval a clear route, a register and a schedule, so decisions are faster and still careful. The written scope sets the workflows and connected systems included, so you know what you are buying.
Problems this solves
Model approvals quietly go stale as vendors update their models
Each approval is tied to a model version and a review cycle. When a review comes due or the version changes, the approval is flagged for a fresh look.
Requests to use a model reach the wrong person or nobody
Requests reach the right reviewer in Jira or your inbox. The workflow configuration sets reviewers by use case, so nothing sits in a general queue.
There is no record of why a model was approved
Every decision is recorded with who made it, when, and for which use. That history stays available in the admin console or written report for later audits.
A good fit if
You already have several AI models in use and need a defined process for approving a new one before it goes live.
Models get updated or replaced often enough that a one-time approval is not enough to keep track of what is actually running.
You need someone accountable for reviewing whether an approved model is still the right one for its use, on a set cycle.
You want new model requests to go through the same review every time, not decided ad hoc by whoever asks first.
Not the right fit if
You need the written rule that defines approval thresholds, not the ongoing process that applies it. See AI Usage Policy System.
You need a simple list of the models already in use, not an approval and review process. See AI Model Inventory.
You need a one-time rating for a specific initiative, not ongoing model approval. See AI Risk Assessment.
You need policy, risk and audit built together as one system. See AI Governance System.
How it works
- We agree the approval process with you. Who reviews a new model request, what evidence they need, and how quickly a decision should come back.
- A model register captures what is already approved. Each entry lists its version, its approved use, and the date it is next due for review.
- New requests are routed for review. A request for a new model, or a new use for an existing one, lands with the right reviewer in Jira or your inbox.
- Review cycles are scheduled, not left to memory. Each approved model comes up for reassessment on a set cadence, so approvals do not quietly expire unnoticed.
- Version changes are tracked against the approval. If a vendor updates a model, that change is logged against the existing entry rather than left invisible.
- A control test log confirms the process works. Real requests are run through the approval and review flow before handoff, not left untested.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Your business information (company basics, hours and brand voice), shared during intake, or confirmation we can reuse what you gave us on an earlier order.
- Your current list of AI models and vendors, including which uses they are already approved for.
- Any existing approval or review policy, even an informal one.
- A governance owner who can act as a reviewer, or name who should.
- Written authorization for any testing of the approval workflow.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One decision-maker who signs off the final approval process and review cadence.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
business information confirmed and your current model list received.
Weeks 1–2
Model register built and the approval and review process agreed with your governance owner.
Week 3
Request routing configured and connected to Jira, Slack or your inbox.
Weeks 3–4
Control test log produced by running real requests through the approval flow.
Week 4
Your review and two rounds of changes to the process or review cadence.
Go-live
Live, handover report sent, and the 30 days of support begins.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $9,700 |
| Express | 15–25 working days | $11,150 |
| Rush | 10–20 working days | $12,600 |
What you receive at handover
- AI Model Governance configured and deployed, covering up to three systems or AI applications
- An approval workflow with version tracking and review cycles (up to five controls)
- Request routing to reviewers in Jira or email, with a record of every decision
- A test report with a control test log for the agreed scenarios
- An admin guide and a written handover report
- 30 days of post-launch support
Expected benefits and how to measure them
- Can keep model approvals current as providers release new versions.
- Helps reviewers see past decisions before making a new one.
- Makes it easier to show which models are approved for which jobs.
- Approvals in date versus overdue
- Count them each month from the console. Overdue approvals show where review cycles need attention.
- Approval request turnaround
- Use request and decision timestamps. Compare with how long approvals took before launch.
- Version changes caught for review
- Count model version changes flagged each quarter, and how many were reviewed before wider use.
Before you start: List the models in use, their versions and who approved each. Note how long a model approval takes today.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running the approval process and register afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting for the register and the tools that route requests.
AI usage during build and testing also bills to your own accounts, so ownership of every provider relationship stays with you.
Ask about running costsCustom requirements
Need approval routing across more than three systems, or a sixth workflow beyond the five in scope? Add a note at checkout for a written Change Quote; your original order keeps its normal timeline while you wait for it. Larger or bespoke projects use the Custom AI request form.
Request a custom versionKeep it running
Optional monthly maintenance covers monitoring, fixes, model and platform updates, a monthly health report and monthly change hours for a new reviewer, an adjusted review cadence, or a fresh approval workflow as your model list grows. It starts after the 30 days of support.
$485/mo (5% of the build price a month, includes 4 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A model-ops platform bundles approval tracking into a broader subscription | A consultancy quotes for setting up a review process | Ours is one fixed price, shown on this page before you buy |
| Scope | A platform gives you workflow building blocks to assemble yourself | An agency's scope grows as the engagement continues | Ours is written on the Scope Card: register, routing and review together |
| Delivery | A platform needs weeks of configuration before approvals actually flow through it | A consultancy engagement often runs a month or more | Ours has a fixed delivery time shown before you buy, with faster options |
| Review discipline | A platform relies on someone remembering to schedule reviews | An agency deliverable depends on what was agreed | Ours schedules review cycles into the register from day one |
Questions about this product
How much does AI Model Governance cost?
It is one fixed build price covering the Scope Card: agreeing the approval process, building the model register, routing requests and scheduling review cycles, with an admin guide and a written handover report. The figure is listed for each delivery speed in the price panel on this page, so the full cost is known up front. Changes beyond the Scope Card are quoted before work begins.
What does AI Model Governance cost to run each month?
Running the approval process and register afterwards costs roughly $20 to $300 a month in your own accounts, mostly hosting for the register and the tools that route requests, such as Jira or Slack. There is no separate platform license on top of that. The exact figure depends on how many systems you connect for routing.
How is AI Model Governance different from AI Usage Policy System?
AI Usage Policy System writes the rule that says when a model needs approval. AI Model Governance runs the ongoing process that applies that rule: reviewing requests, tracking versions and scheduling reassessment. Buyers who already have a written policy often add this next, once there are enough model requests to justify a dedicated approval workflow rather than handling them one at a time.
What happens when a model request is reviewed under AI Model Governance?
The request routes to the reviewer you named, with the model's details and any relevant policy attached, so they can approve, reject or ask follow-up questions. Approval or rejection is always a human decision inside your organization; the workflow tracks the request and the review cadence, but it never approves a model on its own, since that judgment belongs with your named reviewer.
Can we add reviewers or change the review cadence later?
Yes. Adding a reviewer, adjusting how often a model comes up for reassessment, or updating an entry after a vendor change usually fits inside the monthly change hours included with maintenance. Adding a fourth connected system for routing, beyond the three in scope, or a sixth workflow, goes through a Change Quote agreed before that work starts on it.
Can we cancel AI Model Governance maintenance later?
Yes. Maintenance has no lock-in, so you can cancel any month once the 30 days of included support has ended. While it runs, it covers monitoring, fixes, model and platform updates, a monthly health report and monthly change hours for occasional adjustments as the process settles.
Who owns the model register and approval history?
You do. The register, the approval history and the review schedule all live inside your own connected systems, such as Jira or Notion, not a platform we control. We build the process and hand it over with an admin guide and written handover report, and every entry, decision and reviewer assignment stays yours to edit at any time.
Give every model approval a process, not a one-off decision.
Order AI Model Governance and your approval workflow and review cycles are live on a fixed delivery date.
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