MATURITY MODEL
Semantic Drift Intelligence™ Maturity Model
A four-phase progression from semantic visibility to closed-loop remediation — the only reliable path to institutionalizing semantic integrity across your organization.
Semantic governance is not a tooling problem. It is an organizational readiness problem. Most organizations attempting to govern meaning jump straight to automation and discover that agents operating without trust, ownership, and a hardened semantic contract amplify drift rather than prevent it. The SDI Maturity Model defines the four phases that must be completed in sequence — because each phase creates the conditions that make the next one safe, predictable, and effective.
Skipping phases does not accelerate progress. It breaks the operating model.
Each phase in the SDI maturity model builds the trust, ownership, and semantic discipline required for the next. Phase 1 makes drift measurable. Phase 2 makes it owned. Phase 3 makes it preventable by agents operating against a hardened contract. Phase 4 makes it remediable by agents operating within a proven governance framework. This sequence is true for every enterprise, regardless of industry or scale.
PHASE 1
Establish the Control Plane
Semantic observability and trusted scoring
CAPABILITY — Semantic observability and trusted scoring
Organizations begin Phase 1 moving from fragmented, invisible meaning to semantic observability. Drift becomes measurable, explainable, and prioritized. The Semantic Registry is populated with priority metric definitions. Drift detection is running across SQL and documentation sources. Teams are actively using the Drift Viewer to understand what is changing and why. For the first time, the organization can see its semantic environment as it actually is — not as documentation says it should be.
This is the moment when teams finally understand what is changing and why — and until that trust exists, governance is performative and agents are dangerous.
RISK POSTURE
From unknown drift to measured and explainable drift. The organization moves from discovering semantic failures after the fact to detecting them as they emerge.
VALUE UNLOCK
Reliable visibility into semantic stability. Stakeholders can see which metrics are drifting, how severely, and what the downstream impact is.
SDI Focus
• Platform implementation and client agent deployment
• Semantic Registry initialization with priority metric definitions
• SQL and documentation drift detection active
• Drift Viewer operational — drift events scored and prioritized
PHASE 2
Activate Governance
Repeatable, accountable semantic governance
CAPABILITY — Repeatable, accountable semantic governance
Organizations move from visibility to governed response. Ownership is assigned to every metric definition. Workflows are defined for how drift events are reviewed, resolved, and documented. Audit logging becomes the operational center of gravity. Semantic change is no longer an incident — it is a managed workflow with accountability, traceability, and a documented resolution record.
The shift here is from “we see drift” to “we own drift” — and ownership is what makes agents safe to introduce later. Without documented ownership and proven resolution workflows, automated agents have no contract to enforce.
RISK POSTURE
From ad hoc response to governed response. Drift events are no longer resolved randomly or not at all — they follow a repeatable, documented process.
VALUE UNLOCK
Semantic drift becomes a managed operational workflow. The organization builds the audit trail and ownership model that supports both regulatory compliance and future agent deployment.
SDI Focus
• Governance workflows deployed — ownership assigned, resolution tracking active
• Audit logging enabled — full change record for every drift event
• Cross-functional alignment workflows operational
• SDI Enterprise tier active
Available Phase 2 Accelerators
• Semantic Model Hardening — deep validation and lock-down of critical metric definitions across SQL, BI, documentation, and AI agents ($7,500 one-time)
• BI Governance Accelerator — BI semantic alignment and dashboard governance to ensure BI logic matches hardened definitions ($15,000 one-time)
PHASE 3
Drift Prevention Agents
Predictive stability and contract enforcement
CAPABILITY — Predictive stability and contract enforcement
Organizations move from reactive governance to predictive stability. Prevention agents operate against a hardened semantic contract — flagging potential drift before it impacts critical metrics or reports. The agent is not guessing: it is enforcing a contract that the organization has already agreed on through the discipline built in Phases 1 and 2. This is when AI begins to work for governance rather than against it.
Prevention agents only succeed when humans already agree on the semantic contract — otherwise the agent becomes another source of noise. The trust built in Phases 1 and 2 is not overhead. It is the precondition that makes Phase 3 safe.
RISK POSTURE
From responding to preventing. The organization detects potential drift before it reaches production metrics, not after it has already produced conflicting reports.
VALUE UNLOCK
AI enforces the semantic contract safely. The organization gains the speed and coverage benefits of automated drift prevention without the governance risk of deploying agents on an unstable foundation.
PRECONDITIONS FOR THIS PHASE
⚠ Trusted drift scoring established in Phase 1 — the agent must be operating on a drift signal that humans already trust
⚠ Ownership and resolution workflows proven in Phase 2 — the agent must have a contract to enforce and a process to escalate to when exceptions arise
⚠ Semantic definitions hardened — the agent operates against definitions that have been reviewed, validated, and locked
PHASE 4
Active Remediation Agents
Closed-loop semantic operations
CAPABILITY — Closed-loop semantic operations
Organizations move from prevention to closed-loop remediation. Remediation agents propose safe definition changes based on drift evidence and governance history. Where approved through the established workflow, agents execute changes, document lineage, and route exceptions to human reviewers. The semantic layer becomes self-maintaining — with humans in the loop for judgment, not for every routine resolution.
This phase works only because the organization has already demonstrated discipline — without the foundation built across Phases 1 through 3, automated remediation becomes a liability, not an asset. The value here is not the speed of automation. It is the confidence that automation is operating within a proven, trusted framework.
RISK POSTURE
From human bottlenecks to AI-assisted remediation. Routine drift resolution is handled automatically. Exceptions and ambiguous cases are escalated to human reviewers with full context.
VALUE UNLOCK
Continuous, automated, auditable remediation. The organization achieves semantic stability at scale — without the manual overhead that makes governance unsustainable as data environments grow.
PRECONDITIONS FOR THIS PHASE
⚠ Proven agent judgment in Phase 3 prevention scenarios — remediation agents are deployed only after prevention agents have demonstrated reliable, trustworthy behavior
⚠ Governance framework battle-tested across real drift events — the escalation paths and exception handling must be established before agents can use them
OUTCOMES AT THIS PHASE
✓ Semantic stability maintained continuously — drift is detected, assessed, and resolved without requiring manual intervention for every event
✓ Auditable remediation record — every agent-proposed change is documented with evidence, lineage, and approval status
✓ Human governance focused on judgment — reviewers handle exceptions and ambiguous cases, not routine resolution
✓ Enterprise AI operates on a governed, self-maintaining semantic foundation — models and copilots built on this layer are more reliable and more defensible under regulatory scrutiny
SELF-ASSESSMENT
Where Is Your Organization Today?
Each question below maps to a specific phase entry point. The risk on the right is what the organization is currently experiencing if the answer is yes.
Do we lack visibility into where our metric definitions are inconsistent across SQL, BI, and documentation?
⚠ Meaning is changing silently. Metric incidents are discovered in executive meetings, not before them.
Can we see drift but have no ownership model for resolving it?
⚠ Drift events resolve randomly — or accumulate unaddressed. Governance is present in name only.
Do we have governance workflows but no automated prevention?
⚠ Humans are the bottleneck for semantic stability. Coverage shrinks as the data environment grows faster than the governance team.
Are we considering deploying AI agents before our semantic contract is hardened and trusted?
⚠ Automation amplifies drift instead of preventing it. Agents operate on an unstable foundation and produce unpredictable outputs.
If any of these descriptions matches your current situation, the SDI Maturity Model gives you a clear, sequenced path from where you are to where you need to be. Schedule a strategy session to assess your current phase and map the fastest safe path forward.
OFFERING MAPPING
Maturity Phases Mapped to Commercial Tiers
Each phase of the SDI Maturity Model maps directly to a commercial offering. The model below uses the same terminology as the pricing page to ensure complete consistency.
PHASE 1
SDI Professional
$8,500/month + $120,000 implementation
Semantic observability, Registry, and drift detection. Establishes the control plane and makes drift measurable.
• Semantic Registry
• SQL and documentation ingestion
• Drift detection
• Drift Viewer
• Up to 50 metrics
PHASE 2
SDI Enterprise
$12,500/month + $120,000 implementation
Full cross-system governance across SQL, BI, and AI. Governance workflows, ownership, and audit logging operational.
• All Professional capabilities
• BI ingestion
• AI agent drift detection
• Governance workflows
• SSO and audit logging
• Unlimited metrics
• Semantic Model Hardening and BI Governance Accelerator available
PHASE 3
Prevention Agents
Available after Phase 2 — contact for pricing
Prevention agents deployed against a hardened semantic contract. Available only after governance workflows are proven and definitions are hardened.
• Requires SDI Enterprise active
• Semantic Model Hardening completed
• Drift scoring trusted by governance team
PHASE 4
Remediation Agents
Available after Phase 3 — contact for pricing
Closed-loop remediation agents that propose and execute safe definition changes with human approval. Available only after prevention agents have demonstrated reliable judgment.
• Requires Phase 3 prevention agents proven
• Governance framework battle-tested
• Escalation paths established
WHY SEQUENCING MATTERS
Each Phase Creates the Conditions That Make the Next One Safe
This is not a theoretical model. It reflects the operational reality of deploying semantic governance in organizations where data environments are complex, ownership is contested, and the stakes of getting it wrong are high. Skipping Phase 1 means deploying governance workflows against a drift signal nobody trusts. Skipping Phase 2 means deploying prevention agents against a semantic contract nobody owns. Skipping Phase 3 means deploying remediation agents that have never been tested in prevention scenarios. Each of these failures is recoverable — but all of them are more expensive than doing the phases in order.
Phase 1 builds trust
Teams learn to see drift accurately. The Semantic Registry becomes the reference point everyone uses. Drift scoring is validated against real incidents.
Phase 2 builds ownership
Every definition has an owner. Every drift event has a resolution process. The audit trail is established. Governance is operational, not aspirational.
Phase 3 builds confidence in agent judgment
Prevention agents demonstrate reliable behavior against the hardened semantic contract. The organization learns to trust automated enforcement before automation makes changes.
Phase 4 delivers safe closed-loop automation
Remediation agents operate within a proven framework. The semantic layer becomes self-maintaining. Human reviewers focus on judgment, not routine resolution.
EXPLORE FURTHER
Ready to Go Deeper?
Schedule a Strategy Session
Talk to our team about your organization’s current semantic maturity and the fastest safe path to governed, AI-ready operations.