AI Model Governance Software
A predictive-maintenance model trained on two years of vibration data drifts quietly as new equipment comes online, and nobody re-validates it until it misses a failure it used to catch. Kissflow governs every model's deployment approval and puts re-validation on a recurring cycle.
Trusted by energy operators worldwide
A deployment record and re-validation cycle for every model running in production, not just the ones someone remembers to check
A predictive-maintenance model trained on two years of vibration data drifts quietly as new equipment comes online. A defect-classification model gets retrained on a new product line without anyone re-validating it against the old one. A scheduling model's recommendations get trusted long after its assumptions stopped being true. Every one is a governance gap: no deployment authorization requiring validation up front, no audit log of what the model did, and no re-validation cycle to catch drift before it causes a miss.
Kissflow governs the model lifecycle: a registration and risk review before deployment, a deployment approval, a running audit log of the model's use, and a re-validation cycle that keeps it honest. Every AI model in use or proposed for production is registered with its purpose, data sources, and owner, and its risk level and validation evidence are assessed before deployment is authorized. The deployment decision routes through the appropriate reviewers before a model goes live, its use logs for review, and every deployed model sits on a recurring re-validation cycle flagging what's overdue. A dashboard shows every model's status, risk, and next re-validation date.
A model that goes into production without governance drifts quietly until it fails visibly
Models deploy without a validation review
A model moves from pilot to production because it worked in testing, without a structured risk or validation check first.
No record of what a model actually did
Once deployed, a model's recommendations and decisions aren't logged anywhere a reviewer could later audit.
Drift goes uncaught
A model's accuracy degrades as conditions change, and nobody notices until it misses something it should have caught.
Retirement is informal
An outdated model keeps running because nobody owns the decision to retire or replace it.
Six modules. Configurable to your operating model.
Every module ships with default forms, approval logic, integrations, and dashboards. Configure each one to your operating model in the visual builder.
Model registration
Captures every AI model in use or proposed for production, its purpose, data sources, and accountable owner.
Risk & validation review
Assesses each model's risk level and validation evidence before it's authorized for deployment into production.
Deployment approval
Routes the deployment decision through the appropriate reviewers before a model goes live on the floor.
Usage & decision audit log
Logs the model's production use and decisions, searchable for an audit or an incident review after the fact.
Re-validation scheduling
Puts every deployed model on a recurring re-validation cycle, flagging models overdue for review or drift check.
Governance dashboard
Shows every model's status, risk level, deployment date, and next re-validation date across the plant network.
From request to system of record in four steps
Submit
A model is registered with its purpose, data sources, and intended use, whether proposed or already piloting.
Review
Its risk level and validation evidence are reviewed before deployment is considered.
Approve
The appropriate reviewers approve, condition, or decline the model's move into production against plant production or quality data.
Record
Production use is logged and the model is re-validated on a recurring cycle to catch drift, kept as a deployment audit log.
What changes when AI model governance runs on Kissflow
| Process | Before Kissflow | On Kissflow |
|---|---|---|
| Deployment | Approved informally because it worked in testing | Authorized through a structured risk and validation review |
| Production use | Not logged anywhere reviewable | Every decision logged to a searchable audit trail |
| Drift | Discovered only after a visible miss | Caught by a recurring re-validation cycle |
| Retirement | No owner, no trigger to retire a model | Tracked status makes an overdue model visible before it becomes a risk |
| Governance visibility | No single view of what models are running where | One dashboard across every deployed and proposed model |
| Audit readiness | Reconstructed from memory when an auditor asks | Complete registration, approval, and usage record on file |
Connects to the MES, quality, and analytics platforms your models run against
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Built for keeping AI models validated after the pilot ends, not just before it starts
Risk assessed before deployment, not after an incident
No model reaches production without a recorded risk and validation review.
A real usage log, not just a deployment note
Every production decision a model makes is logged and searchable.
Re-validation on a schedule, not by memory
Deployed models are automatically flagged when they're due for re-validation.
A defined deployment approval
Every model has a recorded approver and approval condition before it goes live.
Live in weeks, not a re-implementation
Configure model registration, risk criteria, and approval routing in the visual builder without a governance platform overhaul.
One dashboard for every model in the plant
See registration, approval, usage, and re-validation status for every model, in one place.
Related apps
Exception-to-Execution AI Loop Governance Management System
Govern the human approval needed before a specific model's recommendation turns into an executed action.
AI Tool Request Intake & Governance Management System
Approve the AI tool or vendor a model runs on before the model itself is registered here.
IT Change Management Approval & Release Management System
Route a model version update through change control if it affects a connected production system.
We help manufacturers keep AI models validated and auditable long after the pilot ends
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GVP & CIO at McDermott International, Ltd
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Maria Theresa Cabigon
CIO, SN Aboitiz Power Group
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Talk to usGot questions? We're here to help.
Get SupportAny AI or machine learning model making or recommending decisions against production, quality, or maintenance data, whether built internally or from a vendor.
It governs the process: registration, risk review, deployment approval, usage logging, and re-validation scheduling. Your data science or quality team defines the actual validation criteria and methods.
That app governs whether a team can bring a new AI tool or vendor into use. This app governs the individual model's deployment, usage, and re-validation once it's in production.
A recurring schedule you define, plus any manual trigger like a retraining event, a new data source, or an observed drop in accuracy.
Yes. Process owners configure registration fields, risk criteria, and re-validation schedules in the visual builder, and the AI Builder can generate a working app from a plain-language description.
Through APIs and integration connectors, under single sign-on and role-based access, with every action written to an audit log.
Configuration and AI generation move delivery from weeks to days, without a multi-year platform program or an engineering backlog.
Kissflow is certified to SOC 1, SOC 2, SOC 3, ISO/IEC 27001, HIPAA, GDPR, and CCPA, hosted on Google Cloud with data residency in the US, EU, APAC, and Oceania.
