Platform
Semantic Drift Intelligence™ Platform
The control plane for meaning across SQL, BI, documentation, and AI systems.
See where your definitions drift. Fix what matters. Restore trust in your data and AI.
Your data is not broken. Your definitions are.
Modern data and AI environments move fast. SQL changes. BI logic changes. Documentation changes. AI agents learn from all of it. Meaning drifts quietly in the background.
This drift leads to:
- Conflicting dashboards and KPI disputes
- Slow decisions and loss of trust
- AI hallucinations and governance failures
You cannot govern what you cannot see.
A control plane for meaning.
Semantic Drift Intelligence continuously analyzes SQL, BI logic, documentation, and AI agent behavior to detect where definitions diverge, contradict, or evolve.
You get a unified view of:
- How each metric is defined and where definitions differ
- What changed, why it changed, and how severe the drift is
- What to fix first with clear recommendations
This is semantic observability for the enterprise.
From ingestion to remediation—a continuous cycle of semantic observability.
01 — Ingest your sources
Connect SQL, Azure pipelines, Power BI models, documentation, and AI logs.
02 — Normalize definitions
Parse SQL, BI logic, and documentation into a unified structure.
03 — Detect drift
Identify inconsistencies using deterministic rules, embeddings, and LLM reasoning.
04 — Score severity
Rank drift events by impact and confidence.
05 — Recommend fixes
Receive clear explanations and remediation steps.
06 — Govern meaning
Track changes, assign ownership, and maintain alignment.
Complete Semantic Drift Intelligence Platform
Semantic Registry
Centralized repository for business definitions, metrics, and semantic metadata.
SQL Drift Detection
Continuously monitors SQL pipelines for changes that affect metric definitions.
Documentation Drift
Detects inconsistencies between technical definitions and business documentation.
BI Logic Drift
Analyzes Power BI models, DAX, and other BI tools for semantic divergence.
AI Agent Drift
Monitors AI agent behavior for semantic drift and hallucination patterns.
Drift Viewer
Unified interface to visualize, filter, and prioritize drift events.
Governance Workflow
Assign ownership, track resolution, and maintain audit trails.
Who SDI serves
Data Teams
Understand the full impact of schema and logic changes on downstream systems. Prioritize fixes before trust erodes.
Analytics & BI Teams
Resolve dashboard discrepancies at the source. Ensure consistent metrics across the organization.
AI & ML Teams
Train and operate models on semantically stable definitions. Reduce hallucinations and unpredictable behavior.
Governance & Data Stewards
Move from periodic audits to continuous monitoring. Enforce meaning, not just metadata.
Executives & Decision Makers
Trust the numbers. Move fast without breaking trust. Scale AI with confidence.
What you need to implement SDI
Technical Requirements
- Cloud environment (Azure, AWS, or GCP)
- Read‑only SQL access
- BI workspace or metadata access
- Documentation sources (Confluence, SharePoint, GitHub, Markdown)
- Optional AI logs
- SSO (Azure AD, Okta, or equivalent)
- Networking (VNet or Private Link if private deployment)
Organizational Requirements
- Metric owners identified
- SQL, BI, and governance contacts
- Access approvals
- Agreed drift‑triage process
Implementation Timeline
- Week 1: Environment provisioning + ingestion setup
- Week 2: Drift detectors + registry initialization
- Week 3: Validation + governance workflow configuration
- Week 4: Executive enablement + go‑live
See where your definitions drift.
Request early access and receive a drift diagnostic for your top metrics.