Spoggle reads the schema no one wrote.
A patented semantic clustering engine that reads your sources, finds the structure, and publishes warehouse-ready models a data architect can ship.
No hand-written mapping spec. No pipeline team.
Semantic clustering engine
Source connectors
Runs in your environment
Live industry deployments
Why the warehouse project never finishes
Every midmarket company has spent the last decade building a data warehouse, a lake, a lakehouse, and a semantic layer. Each one required pipelines, schema work, and a team to maintain them. The six-month project is always six more months from done. These are the five places it actually breaks.
The sources never joined themselves
CRMs, ERPs, spreadsheets, SaaS tools, and SFTP drops that describe the same customers and the same orders under different column names. The join keys were supposed to live in a spec no one wrote.
Five schemas for one entity
The same customer exists in five systems with five slightly different shapes. Every downstream report picks one and prays. The reconciliation runs nightly and is wrong by morning.
The engineers you need are hired by someone else
Senior data engineers are expensive, scarce, and busy building at companies ten times your size. A midmarket ops team cannot carry a dedicated data platform group just to get the numbers right.
Fortune-500 tools on a midmarket budget
Platforms designed for companies with a hundred data engineers arrive with the licensing, the services contract, and the eighteen-month rollout that come with them. The midmarket has a different problem and is forced to solve it with the enterprise tool.
AI agents are ready. The data is not.
An ops lead can prototype a useful agent over a weekend. The agent asks a simple question. The data layer cannot answer it. Six months later the agent still cannot.
Average pipeline build time
Most of these come back to the same missing piece: a join-key spec no one wrote, and no budget for a team to write one. Spoggle closes the join-key gap by reading the data itself. The patented clustering engine groups fields that describe the same thing across every source, profiles the result, and proposes warehouse-ready models a data architect reviews and ships.
The data layer finishes in weeks instead of quarters. The AI work waiting on it starts inside the same engagement.
Side-step the grunt. Semantically cluster data. Autobuild data models.
Five composable parts sit under the platform. Click any one to see the mechanic.
Three views of the same engine.
Platform capabilities walks the subsystems one by one. Built for real work groups the same mechanics by the job they do inside an engagement. Where it runs is the deployment shape, the security posture, and the commercial model.
The patent. Reads column names, value distributions, and text content across every source Spoggle has ingested. Groups fields into canonical clusters and derives data lineage from source to consumption without a hand-written mapping.
Clustering
Fields with different names and different shapes that describe the same entity collapse into one canonical model.
Lineage graph
Every derived object carries a path back to the source system and column it came from.
Every dataset gets profiled on ingestion. Completeness, uniqueness, consistency, and validity scored automatically. The catalog makes the entire substrate searchable, tagged, and reviewable before any report runs against it.
Health score on ingestion
Four axes. Real numbers. The quality problem gets surfaced where it can still be fixed, not after the board deck shows the wrong chart.
Searchable catalog
One pane across every dataset Spoggle knows. Filter by source, tag, table, or the downstream report that uses it.
Ask a plain-English question. Spoggle grounds the query against the clusters it built, returns the chart, and shows the SQL it ran. The semantic layer it queries is the one it generated, so the answer maps to real columns in real sources.
Ask Spoggle
Plain-English question in, chart and SQL out. The query always grounds against the canonical model, not a hallucinated schema.
"Top 10 customers by revenue this quarter"
Revenue by customer
Auto-generated insights
Distributions, correlations, and charts generated the moment a dataset lands. A starting point for the actual investigation.
Distribution
Trend
Also in the box: 90+ source connectors · cleanse and transform primitives · sandbox environments per engagement · REST and GraphQL outputs · alerts on drift and quality regression · role-based access control with SSO via Microsoft AD.
Get to an agent-ready data model.
The substrate every AI agent in your business will eventually query is the same substrate your reporting stack should already be sitting on. Spoggle is the part of Bastion that builds it.