Every industry.
One foundation model.
Your data ships as tables: exports, extracts, feeds, logs. Schema reads them raw, with no shared keys, no labels, no preprocessing, and answers six questions every team that runs on tables ends up asking. One tab per question, each with real output and a worked industry example.
One model across healthcare, finance, manufacturing, retail, energy, telecom, logistics, defense, agriculture, media, legal, real estate, climate, and vertical SaaS.
What is this data?
Someone hands you tables: an export, a legacy extract, an inherited warehouse. Before anyone can use them, a human has to work out what they are. Schema does that step in one pass, scored, and it works even when every header has been stripped.
Raw tables, any number, from any system. Documentation optional. Column names optional.
What each table is, what every column contains, where the PII is, what is missing, and what the data can support. In one pass.
- Data Understanding Every team; the place to start
Understand any dataset in one pass
Drop in raw tables and get back what they are, what is in them, what is missing, and what they can predict.
Example · Healthcare Read EHR and trial extracts on sight
- Nameless Operation Legacy and privacy-constrained data owners
Works when column names are stripped or coded
val_B and metric_14 are enough: full operation on stripped headers and code-named columns.
Example · Manufacturing Legacy historians and coded tags (val_B, metric_14)
- Database Intelligence Data governance teams and CDOs
Audit every table for sector, PII, and quality
Sweep your databases and warehouses: every table identified and profiled, documentation not required.
Example · Financial Services PII sweeps across core banking, trading, and CRM
What can I build on it?
Agents fail on structured data for a structural reason: raw rows overflow the context window, and querying presumes someone already understands the schema. An agent that meets a new source at runtime has nobody to ask, and no human team can sit inside an inference loop. Vertical AI products pay the same tax in per-customer onboarding weeks. Schema removes the dependency.
Your agent stack, product, or workflow tool, pointed at raw customer tables.
Data understanding your agent or app consumes directly: what each table is, how sources relate, what they predict, scored. Raw rows never enter LLM context.
- Agent Perception AI engineers building agents
Give your agents structured-data perception
Table in, data understanding out: the agent reasons over the scored output, never over raw rows. No context overflow, no hallucinated joins.
Example · Energy & Utilities Grid ops agents reading meter and asset data
- Vertical Foundation Vertical AI and SaaS builders
Build your vertical AI product on it
A foundation layer that already understands your industry's data, instead of a per-customer ML stack.
Example · Healthcare Clinical AI products over EHR data
- Schema Mapping Heads of implementation at vertical AI companies
Onboard customer data in minutes, not weeks
Map any raw customer export to your canonical schema: no keys, no dictionaries, no mapping workshops. Schema proposes the mapping with confidence scores and evidence, and implementation weeks become a review pass.
Example · Vertical SaaS The flagship motion: customer onboarding
- Workflow Scoring Ops and automation owners
Score anything inside your workflows
A scoring node for any automation: enrich a record, score it, route on confidence.
Example · Retail & Commerce Score leads and orders inside CRM automations
How do I unify systems that share no keys?
Your customer exists in five systems under five IDs, and the systems share no keys. Every incumbent tool wants schema mapping workshops or match rules before it starts. Schema matches from the data itself, and every match ships its evidence.
Exports from any number of systems. No shared keys, no labels, no preprocessing.
Matched columns, matched records, and a merged-schema proposal, each with a confidence score and its evidence, proposed into a review queue.
- Entity Unification Data platform teams serving marketing and CDP
One customer, one supplier, one patient, across every system
Unify entities across systems without shared keys or schema mapping. Every match ships its evidence.
Example · Financial Services KYC and Customer 360 across cards, core, and wealth
- Source Reconciliation Integration, migration, and M&A teams
Reconcile systems after a merger or migration
Any number of systems, no shared keys: one unified schema and matched records in days.
Example · Healthcare Hospital system EHR migrations
- Golden Records MDM and data platform owners
Golden records that keep their receipts
One merged record per entity, provenance-stamped, with every source's raw value preserved.
Example · Manufacturing Golden supplier and parts masters
What can it predict?
The usual path to a prediction model is a pipeline, a training run, and a leap of faith. A Schema endpoint goes live in minutes, proposes its own target if you have none, and every answer carries its held-out report. You never take the model on faith. Where a score feeds a decision about a person, it ships with its evidence into your review flow: predictions inform the decision, people make it.
A table and, if you have one, a target column. If not, Schema proposes one and shows its reasoning.
A live scoring endpoint, and every answer ships with a held-out report against a baseline.
- Proven Predictions Enterprise ML and data science teams
Prediction endpoints in minutes, with proof
No training run, no preprocessing pipeline. Every endpoint carries its own held-out report.
Example · Financial Services Fraud, credit risk, and AML alerts
- Anomaly Detection Ops and risk teams
Flag the rows that do not belong
An anomaly score for every row, labels optional. Calibrated where labels exist.
Example · Energy & Utilities Meter and grid anomalies
- Continual Learning ML platform owners and TCO buyers
Add tasks without a full retrain
Enterprise checkpoints take on new tasks sequentially, no full retrain per task, with retention across fifty consecutive fine-tunes measured in the model card.
Example · Manufacturing Per-plant tasks accumulated in one checkpoint, no retrain
What industry is this dataset even from?
An unlabeled dataset lands in your warehouse: inherited, third-party, undocumented. Schema names its industry from the raw cell values alone, a capability no other vendor returns as an output at all.
A dataset with no metadata, no labels, no column names required.
Its industry sector, with top candidates and confidence. Any sector, any domain.
- Sector Detection Governance teams; the opening demo
Name any dataset's industry from raw values alone
Any sector, any domain, no metadata. No other vendor returns this as an output.
Example · Retail & Commerce Classify marketplace seller data
What about the gaps?
Real tables have holes, and most pipelines mean-fill them and hope. Schema fills gaps from the joint structure of your own data, not generic priors, and scores every fill so your pipeline can gate on it.
Tables with missing values. No cleaning pass first: missing values are signal, not errors.
Filled numeric and categorical values with row-level confidence scores your pipelines can act on.
- Data Completion Analytics engineering
Fill missing values, with confidence scores
Numeric and categorical gaps filled from your own data, each with a confidence your pipeline can gate on.
Example · Healthcare Incomplete labs and vitals
Start with your rawest tables.
No shared keys, no labels, no preprocessing. If your data ships in rows and columns, Schema understands it. The models doing the work are in the Model Registry.