Semantic Intelligence

The problem

The number nobody can agree on

One metric. Asked in every tool, by every team, through every copilot — and the answers never line up. It looks like a reporting bug. It’s a control-layer problem, and it spans the whole stack.

Now 200+ AI agents pull these to make decisions. Which one is the source of truth?

“Q3 Revenue”6 different answers

Finance · Board deckPower BI

$48.2M

recognized · GAAP

Sales · PipelineTableau

$51.7M

bookings

Marketing · Attr.Qlik

$46.1M

attributed pipeline

FP&A · ForecastSharePoint

$49.8M

net of returns

RevOps · Board v2Databricks

$52.3M

incl. renewals

Exec · copilotCopilot

≈ $50M?

LLM estimate

How it works

Reveal your semantic layer, don’t rebuild it

The Datalogz Similarity Engine runs this loop every day, on its own — turning metadata you already have into a living map of meaning.

Read in place

We read definitions where they already live — warehouse, BI, copilots, even spreadsheets in SharePoint.

Cluster into concepts

Group every object that represents “Revenue” into one concept, regardless of which tool or layer it sits in.

Detect drift

Score where the same concept is defined with contradictory logic, 0–100, with real usage mapped onto each.

Surface overlap

A composite similarity score flags duplicate and conflicting assets automatically. No tagging, no humans in the loop.

The product

See the overlap, similarity, and drift

Datalogz · BI Similarity — Report Clusters

Every definition, clustered into concepts

The Similarity Engine groups thousands of reports, measures, and datasets into the concepts they actually represent — so “Revenue” is one node, not five hundred scattered objects.

✓Clusters by name, logic, and schema overlap

✓Tunable similarity thresholds at 70 / 80 / 90%

✓Works across every connected BI tool at once

Catch the near-duplicates instantly

Redundant reports and copy-pasted logic light up the moment metadata changes. No tagging queue, no manual review — the intelligence is automatic.

✓Composite similarity score, 0–100%

✓Surface duplicate and overlapping assets in bulk

✓Quantify the BI footprint you can safely retire

Datalogz · Duplicate detection

Datalogz · Semantic Graph

One map of meaning across the estate

Zoom out and the whole BI estate becomes a single force-directed graph — entities, definitions, and dependencies across every tool and team, drift and all.

✓Force-directed map of entities across tools

✓Conflicts and low-usage assets flagged in place

✓From the data foundation up to every AI copilot

Where it fits

Above every tool, not inside any one

Most enterprises run three or more BI tools on top of multiple warehouses. Datalogz spans all three tiers as the observability layer, rather than living inside any single one.

No agents, no pipelines, no config changes. Read-only via native APIs and existing service accounts.

Emerging AI

Copilot