First
Data Governance
Quality, lineage, access — making sure data is trustworthy.
WikiSure in 10 seconds
Semantic Governance is the discipline of giving every critical business term one approved meaning that humans, systems and AI agents all resolve against.
WikiSure operationalizes that discipline: upload your documents, see where meaning is drifting across teams and systems, approve a canonical definition, and watch the same term stay aligned over time.
Before · Semantic drift
One term. Many silent meanings.
- Legal → contractual obligation in §4.2
- Sales → everything unless excluded
- AI agent → coverage_flag = TRUE if policy IN ('A','B')
Result: inconsistent decisions, audit risk
WikiSure governs
↓ WikiSure governs ↓
After · Governed meaning
One approved definition. Resolved by everyone.
Coverage · v4 · Aligned · Owner: Underwriting
The contractually defined scope of financial protection a policy provides, subject to documented exclusions and limits.
GET /api/resolve?id=coverage → 200 OK
Result: consistent decisions, audit-ready
Plain-language glossary
- Semantic Drift
- When the same term means different things to different teams, systems or AI agents — quietly producing different outcomes.
- Canonical Definition
- The single approved, versioned, owner-accountable meaning of a term that everything else resolves against.
- Meaning Resolution
- Looking up a term and getting back the one approved definition — for a human, a system or an AI agent.
- Organizational Learning
- Every governance decision, drift fix and approval is preserved so the platform gets sharper with every scan.
WikiSure · The Semantic Governance Layer · Industry: Insurance
AI Exposes Semantic Debt.
WikiSure identifies conflicting business definitions, detects semantic drift, and creates a trusted source of meaning for people, systems, and AI.
A new categoryThe Semantic Governance Platform for Insurance.Govern meaning. Reduce risk. Enable trusted AI.
Required evidence for EU AI Act Art. 9–15, ISO 42001 and DORA. Used by AI Governance Leads, CDOs and Enterprise Architects. Why semantic governance?
Latest Run
2 hours agoInsurance Policies Q2
Alignment
64%
AI Risk
Medium
Drifts
4
18 concepts analyzed. 4 require governance attention.
Open findings →The Hidden Cost
Every enterprise accumulates Semantic Debt — the cost of inconsistent and unmanaged meaning.
WikiSure identifies, scores and remediates it before it reaches your AI systems.
The next governance challenge
AI amplifies inconsistency in meaning. Governance has to catch up.
Then
AI Governance
Models, prompts, agents — making sure AI behaves safely.
Now
Semantic Governance
Meaning — making sure humans, systems and AI agree on what every term means.
WikiSure defines and operates the Semantic Governance layer — the missing layer between data and AI. See proof of learning →
Governance Evidence
Live signals from the WikiSure registry
Versioned
—
Definitions tracked
Workflow
—/—/—
Aligned · In review · Draft
Governance
—
Decisions logged
Audit trail
—
Immutable events
Org memory
—
Reuse signals
Last audit
—
Most recent scan
Counts pulled live from the public registry. Every record carries owner, version and timestamp.
Sample Analysis Data
See a complete scan, end to end
One click runs the full semantic governance workflow on a sample insurance policy. Every stage shows what WikiSure produces — no hidden steps, no marketing claims.
1. Upload
Sample policy document is ingested and parsed.
policy_motor_v3.pdf · 14 pages · 1.2 MBExtracted 3,412 tokens · 47 candidate concepts2. Semantic Analysis
Concepts extracted and compared against the canonical registry.
- •
total loss— matched canonical (insurance) - •
claim trigger— 2 conflicting definitions found - •
excess— drift vs. v2.1 detected
- •
3. Findings
Drift, contradictions, and warnings surfaced with severity.
High Contradiction in claim triggerMed Drift on excess (semantic shift)Low Missing source for betterment4. Governance
Stewards review, approve a canonical definition, or escalate.
Decision: Approve canonical for claim triggerSteward: a.steward@acme · Reused prior approval from Scan #3125. Report
Executive summary with business risk and recommended actions.
3High-risk+14%Alignment12Reused6. Learning
Approved definitions and decisions feed organizational memory.
Registry now contains 1,284 canonical definitions · +1 reused next scan.
This demonstration uses pre-computed sample outputs so reviewers without an account can evaluate the workflow. The same pipeline runs on real documents inside the product.
What WikiSure Is Building
WikiSure helps organizations create a trusted layer of business meaning across documents, teams, data, processes, and AI systems.
The current Drift Analysis experience is the first step in identifying where meaning has diverged and where alignment is needed.
Free · No signup · 30 seconds
Run a live Semantic Drift Audit
Paste 1–3 of your own business definitions. WikiSure shows exactly how Legal, Risk, IT and an AI agent will interpret them differently — and which EU AI Act / DORA articles you are exposed to.
Definition 1
Why Start with Drift Analysis?
Organizations often use critical business terms differently across documents, departments, systems, and teams. Drift Analysis helps reveal these inconsistencies and makes hidden knowledge risks visible.
This is the first step toward establishing trusted business definitions across the organization.
What Happens After Drift Detection?
Detecting semantic drift is only the beginning. Once inconsistencies are identified, organizations typically need to:
- ›Align on trusted definitions
- ›Assign ownership
- ›Manage change over time
- ›Improve consistency across teams
- ›Support reliable AI and automation
WikiSure is evolving to support this broader journey.
How it works
From upload to governed report.
Why Semantic Alignment Matters
When critical business terms are interpreted differently across documents, departments, and systems:
- •Decisions become inconsistent
- •Reporting becomes unreliable
- •Governance becomes harder
- •Compliance risk increases
- •AI systems become less trustworthy
Semantic alignment creates a stronger foundation for operations, governance, and AI.
Strategic Insight
AI Reveals Semantic Debt
Many organizations have lived with inconsistent terminology for years.
AI does not remove these inconsistencies.
It often exposes and amplifies them.
As organizations adopt AI, shared business meaning becomes increasingly important. Semantic Governance helps create a stronger foundation for trusted AI.
The Problem
The same business term often means different things across departments, systems and AI agents. Humans compensate. AI cannot.
What WikiSure Does
WikiSure resolves every enterprise term to a single, versioned, owner-accountable definition consumable by humans, systems and AI.
The Outcome
Consistent decisions. Better governance. AI systems that act on the same meaning the business operates on.
A New Governance Challenge
Insurers govern data, risk and compliance — but not yet meaning.
Insurance companies have long governed data, processes, risks, and compliance.
However, many organizations still lack a systematic way to govern the meaning of their most important business concepts.
When critical terms are interpreted differently across teams, documents, systems, and AI applications, inconsistency becomes unavoidable.
WikiSure addresses this challenge through Semantic Governance.
Category Definition
What Is Semantic Governance?
Semantic Governance is the practice of ensuring that critical business concepts are defined, understood, and applied consistently across an organization.
It provides a foundation for:
- ›Trusted reporting
- ›Consistent decision-making
- ›Regulatory alignment
- ›Reliable automation
- ›Trustworthy AI
WikiSure is designed to help insurers establish and maintain this foundation.
Category Differentiation
Why Traditional Tools Are Not Enough
| Tool category | What it governs |
|---|---|
| Data Catalogs | Data assets |
| Enterprise Wikis | Information and documents |
| AI Governance Platforms | AI models and policies |
| WikiSure | Business meaning |
Most governance tools manage information. WikiSure helps organizations manage interpretation and meaning.
Industry Relevance
Why Insurance Needs Semantic Governance
The same term can have different meanings across underwriting, claims, actuarial, risk, compliance, and operations.
These differences often remain hidden until they affect:
- •Reporting
- •Audits
- •Compliance reviews
- •Transformation programs
- •AI initiatives
Semantic Governance helps identify and reduce these gaps.
Business Impact
The Cost of Semantic Misalignment
Different interpretations of the same business concepts can create significant operational, governance, and AI-related challenges.
Reporting
- ›inconsistent KPIs
- ›conflicting reports
- ›reduced confidence in decision making
Leaders spend time debating definitions instead of acting on insights.
Risk and Compliance
- ›inconsistent classifications
- ›unclear ownership
- ›audit challenges
Higher governance and compliance exposure.
Transformation Programs
- ›data modernization
- ›system migrations
- ›operating model redesign
Projects become slower, more expensive, and harder to align.
Artificial Intelligence
- ›conflicting source material
- ›inconsistent terminology
- ›different interpretations of key concepts
AI outputs become less predictable and harder to trust.
Executive Perspective
Why This Matters to Enterprise Leaders
For executives, semantic alignment is not primarily a documentation challenge.
It influences:
- ›decision quality
- ›governance effectiveness
- ›regulatory confidence
- ›transformation success
- ›AI readiness
Organizations that cannot consistently define critical business concepts often struggle to scale governance and AI initiatives effectively.
AI exposes semantic debt.
Semantic Governance helps reduce it.
AI and Governance
AI systems inherit the strengths and weaknesses of organizational knowledge. When business terms are interpreted inconsistently, AI outputs can become difficult to trust.
Semantic alignment helps create a stronger foundation for AI-driven decision making.
Platform Roadmap
The Semantic Governance Journey
1Available today
Detect Semantic Drift
Identify inconsistencies across documents, teams, and systems.
2Future
Establish Trusted Definitions
Create one approved, versioned meaning per critical term.
3Future
Govern Meaning and Ownership
Assign accountable owners and manage change workflows.
4Future
Align Data, Processes, and AI
Ensure humans, systems, and AI agents resolve the same meaning.
5Future
Create Trusted Enterprise Knowledge
Build a reliable foundation for governance, compliance, and AI.
Only Step 1 is available today. Future steps represent the platform direction and may evolve based on design partner feedback.
The End State
A future where business concepts have clear ownership, trusted definitions, traceable changes, and consistent interpretation across people, systems, and AI.
This is the long-term vision behind WikiSure.
Beyond Drift Detection
Drift detection identifies the problem.
Semantic governance helps manage the solution.
Over time, WikiSure aims to become a trusted reference layer for business definitions and organizational knowledge.
Beyond Document Analysis
Today's experience focuses on identifying semantic drift in insurance documentation.
The broader vision is to help organizations create and maintain a trusted, shared understanding of business concepts across people, processes, data, and AI systems. This helps reduce ambiguity, improve governance, and increase confidence in decisions and AI outputs.
Enterprise Vision
Building the Semantic Layer of the Insurance Enterprise
Organizations have systems of record for customers, policies, claims, and financial data. Increasingly, they also need a trusted system for business meaning.
WikiSure is being developed to support this capability by helping organizations identify, align, and govern critical business concepts across the enterprise.
Built for regulated enterprises
- EU AI Act
- NIST AI RMF
- ISO 42001
- ISO 27001
- DORA
- Solvency II
- GDPR
- SOC 2 (in progress)
Mapped to EU AI Act Art. 9–15 (risk management, data governance, traceability) and ISO 42001 §6–8.
The cost of doing nothing
Semantic drift is invisible until your AI gets it wrong in production.
38%
of GenAI agent errors
trace back to inconsistent definitions of the same business term across source systems (internal pilots, 2025).
€2.4M
average annual cost
of definition drift in mid-size insurers — claims leakage, reporting rework and disputed decisions.
9 months
typical audit delay
when a regulator asks who owns the meaning of a critical term and no canonical answer exists.
Indicative figures from WikiSure design-partner programme. Your numbers vary by industry and AI maturity.
Why not what you already have
WikiSure is the meaning layer your existing stack is missing.
| Tool | What it does | What it cannot do |
|---|---|---|
| Excel / SharePoint | Static glossary list | No versioning, no owner, no API. AI agents cannot consume it. |
| Confluence / Wiki | Searchable prose | One page per term — no canonical resolution, no drift detection. |
| Collibra / Informatica / Atlan | Data catalog & lineage | Catalogs columns, not meanings. No alignment layer for AI agents. |
| Vector DB / RAG | Retrieves relevant text | Retrieves whatever exists — including conflicting definitions. |
| AI Governance suites | Model risk & policy | Govern the model. Do not govern the meaning the model acts on. |
| WikiSure | Governed, versioned, owner-accountable definitions resolvable by humans, systems and AI agents via one API. | Designed to sit alongside — not replace — your catalog, wiki and RAG. |
Design Partner Programme
We are onboarding 5 regulated enterprises before general availability.
Design partners get a 12-week guided pilot, direct access to the founding team, and pricing locked at programme rates. In return we ask for one customer reference once the value is proven.
- › Insurance carriers
- › Banks & asset managers
- › Healthcare providers
- › Public sector AI programmes
Pricing
Start with a free audit. Grow into a governed control plane.
Pilot
Free
Single team, one glossary
- ›Upload up to 5 documents
- ›Semantic Drift Report (PDF)
- ›Public API for resolved terms
Team
From €1,500 / month
One business unit
- ›Up to 5 namespaces
- ›Governance workflow & owners
- ›SSO, audit log, signed reports
Enterprise
Custom
Multi-BU, regulated industry
- ›Unlimited namespaces & users
- ›Private deployment / EU data residency
- ›ISO 42001 & EU AI Act evidence pack
All plans include encryption in transit and at rest, EU data residency option, and a signed DPA.
FAQ
Common Questions
- Is WikiSure only a document analysis tool?
- No. The current experience focuses on identifying semantic drift and definition inconsistencies. This capability helps organizations understand where alignment problems exist and serves as the starting point for broader governance and knowledge management efforts.
- Why is semantic drift important?
- When key business concepts are interpreted differently across teams, documents, and systems, organizations face increased operational, reporting, compliance, and AI-related risks.
- What is coming next?
- Future capabilities may include trusted definition management, governance workflows, ownership tracking, semantic traceability, and AI knowledge alignment.
Don't ship another AI agent on undefined meanings.
Run a free Semantic Drift Audit on your existing glossary, or book a 20-minute call with the team to see WikiSure on your own documents.
Category model
The Semantic Governance Category
Five interconnected concepts. One category.