Services — Overview
Semantic Drift Intelligence™
The operating system for meaning across SQL, BI, documentation, and AI systems.
A high‑level overview of why SDI exists, the problem it solves, and the outcomes it enables.
What is Semantic Drift Intelligence™?
Semantic Drift Intelligence™ is the control plane for meaning inside the enterprise. It continuously detects, explains, and prevents semantic drift across SQL, BI logic, documentation, and AI systems — giving organizations a stable foundation for analytics, governance, and AI.
Why SDI exists
Three forces make SDI necessary:
Meaning changes faster than governance can keep up — SQL evolves, BI logic diverges, documentation lags. Drift is constant.
AI amplifies drift — models trained on inconsistent meaning inherit and magnify semantic errors.
Governance is often performative without visibility — you can’t govern what you can’t see or measure.
When meaning drifts, trust collapses — and teams lose confidence in their data, dashboards, and AI systems.
The real problem: semantic instability
Organizations don’t lack data. They lack semantic stability — a shared, trusted understanding of what their data means and how that meaning changes over time. SQL evolves, BI logic diverges, documentation lags, and AI learns from all of it. Without semantic stability, every downstream system becomes fragile.
How SDI works at a high level
Semantic Registry
The source of truth for meaning.
Drift Detection
Continuous monitoring of semantic change.
Governance Workflows
Ownership, accountability, and alignment.
Agents
Prevention and remediation once the organization is ready.
What makes SDI different
- SDI focuses on meaning, not metadata.
- It detects drift before it becomes a metric incident.
- It creates a governed semantic contract that AI can safely enforce.
SDI defines a new category — the control plane for meaning.
Who SDI is for
Data & BI Leaders — Need semantic stability for analytics.
AI & ML Teams — Need consistent meaning for safe model behavior.
Executives — Need clarity, alignment, and trusted metrics.
Outcomes you can expect
- Fewer metric incidents
- Faster governance cycles
- Higher trust in dashboards and AI
- Safer introduction of agents
- A stable semantic foundation for enterprise AI
The SDI maturity journey
SDI is adopted in four phases — from observability to governance, prevention, and finally closed‑loop remediation. Each phase builds the trust, ownership, and semantic discipline required for the next.