Solutions
One foundation model across every industry. Your data ships as tables: exports, extracts, feeds, logs. The model reads them raw and answers six questions that every team running on tables ends up asking.
api.example.com, keys, and ids are placeholders.Raw means no shared keys, no labels and no preprocessing. The six questions below follow the order in which people usually need them.
1. What is this data?
Someone hands you tables: an export, a legacy extract, an inherited warehouse. Before anyone can use them, a person has to work out what they are. The model does that in one pass, scored, and it works even when every header has been stripped.
- You bring: raw tables, any number, from any system. Documentation and column names are optional.
- You get: what each table is, what every column contains, where personal data sits, what is missing, and what the data can support.
| Use | For whom | Example |
|---|---|---|
| Understand any dataset in one pass | Every team; the place to start | Reading clinical-trial and health-record extracts on sight |
| Work when column names are stripped or coded | Owners of legacy or privacy-constrained data | Historian tags such as val_B and metric_14 in a plant |
| Audit every table for sector, personal data and quality | Data governance teams | Personal-data sweeps across banking, trading and CRM systems |
2. 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. Vertical AI products pay the same tax as weeks of onboarding per customer. A data-understanding layer removes the dependency.
- You bring: your agent stack, product or workflow tool, pointed at raw customer tables.
- You get: data understanding that your agent or app consumes directly: what each table is, how sources relate, what they predict, all scored. Raw rows never enter the language model's context.
| Use | For whom | Example |
|---|---|---|
| A foundation layer for a vertical product | Vertical AI and software builders | Clinical AI products over health records |
| Onboard customer data in minutes | Implementation teams | Mapping a raw customer export to a canonical schema, with confidence and evidence |
| Scoring inside workflows | Operations and automation owners | Scoring leads and orders inside CRM automations |
3. How do I unify systems that share no keys?
Your customer exists in five systems under five identifiers. Most tools want mapping workshops or match rules before they start. The model matches from the data itself, and every match carries its evidence.
- You bring: exports from any number of systems, with no shared keys, labels or preprocessing.
- You get: matched columns, matched records and a merged-schema proposal, each with a confidence score and evidence, proposed into a review queue and never applied automatically.
| Use | For whom | Example |
|---|---|---|
| One customer, one supplier, one patient across every system | Data platform teams | Know-your-customer and customer-360 across cards, core banking and wealth |
| Reconcile systems after a merger or migration | Integration and migration teams | Hospital record migrations |
| Golden records that keep their receipts | Master-data owners | Golden supplier and parts masters, each source's raw value preserved |
4. What can it predict?
The usual path to a prediction model is a pipeline, a training run and a leap of faith. Here an endpoint goes live in minutes, proposes its own target when you have none, and ships a held-out report with every answer, so the model is never taken on faith. Where a score feeds a decision about a person, predictions inform and people decide.
- You bring: a table and, if you have one, a target column.
- You get: a live scoring endpoint, and a held-out report against a baseline with every answer.
| Use | For whom | Example |
|---|---|---|
| Continual learning | Platform owners | Per-plant tasks accumulated in one checkpoint without full retraining |
5. What industry is this dataset from?
An unlabelled dataset lands in your warehouse: inherited, third-party, undocumented. The model names its industry from the raw cell values alone, with top candidates and confidence, for any sector and any domain. No other tabular model returns this as an output.
- You bring: a dataset with no metadata, no labels and no column names.
- You get: its industry sector, the top candidates and a confidence for each.
- Example: classifying marketplace seller data.
6. What about the gaps?
Real tables have holes, and most pipelines mean-fill them and hope. The model fills gaps from the joint structure of your own data, not from generic priors, and scores every fill so a pipeline can gate on it. Missing values are signal, not errors.
- You bring: tables with missing values, no cleaning pass first.
- You get: filled numeric and categorical values with a row-level confidence.
- Example: incomplete labs and vitals in health data.
Industries
The same model is used across healthcare and life sciences, financial services, manufacturing and industrial, retail and commerce, energy and utilities, telecommunications, logistics and supply chain, defense and government, agriculture and food, sports, entertainment and media, legal, real estate, climate and environment, and vertical software.