DataLgorithmics GlobalAI · Data · Agents · Enterprise · Research · Education · Ecosystem
DataLgorithmics
Products / AI Governance Operating System
Productised capability

AI Governance Operating System

Manage AI inventory, ownership, risk, approvals, vendor evidence, human oversight and recurring review.

Commercial model: scoped implementation, managed operation or recurring service depending on the workflow and operating requirements. Current commercial terms are confirmed after scoping; no outcome is guaranteed before evidence is established.

Capability components

AI inventory

Configured around the workflow, approved data, accountable owner and success criteria.

Ownership and accountability

Configured around the workflow, approved data, accountable owner and success criteria.

Risk classification

Configured around the workflow, approved data, accountable owner and success criteria.

Approval gates

Configured around the workflow, approved data, accountable owner and success criteria.

Vendor/model diligence

Configured around the workflow, approved data, accountable owner and success criteria.

Monitoring and review

Configured around the workflow, approved data, accountable owner and success criteria.

Reference operating architecture

Experience · users, channels, interfaces and human workflows
Intelligence · models, agents, prompts, retrieval and business logic
Knowledge & data · approved sources, structured data, context and quality
Integration · APIs, enterprise applications, events and tools
Trust · permissions, evaluation, logging, monitoring, escalation and governance

Success measures

Inventory coverageMeasure against an agreed baseline.
Overdue reviewsMeasure against an agreed baseline.
Risk closureMeasure against an agreed baseline.
Approval cycle timeMeasure against an agreed baseline.
Evidence completenessMeasure against an agreed baseline.
Policy exceptionsMeasure against an agreed baseline.

Integration landscape

✓ AI systems
✓ Procurement
✓ Risk / compliance
✓ Identity
✓ Ticketing
✓ Evidence repositories

Integration feasibility depends on the systems, licences, APIs, security model and access approved by the client.

Implementation path

Discover

Workflow, users, data, baseline.

Design

Architecture and controls.

Prove

Evaluation and decision gate.

Deploy

Integration and adoption.

Operate

Monitor, improve, govern.

Required controls

Accountable owner

A named business owner for outcomes and permitted use.

Human escalation

Defined points where people review, approve or take over.

Evaluation

Quality, safety, cost and operational performance measured before and after release.

Permissions

Least-privilege access to systems, tools and knowledge.

Auditability

Appropriate records of actions, approvals and important changes.

Review cadence

Periodic review of quality, risk, cost and continued suitability.

Build internal capability alongside delivery

Pair implementation with DataLgorithmics Academy pathways so operational, technical and governance teams can sustain the capability.

Academy pathwaysTrust CentreDiscuss AI Governance Operating System