Every industry.
One foundation model.
Schema is a foundation model for tabular data. Connect any raw multi-table, multi-source input and develop vertical or agentic AI on your data. Same architecture, every industry.
Find your industry
- 01 Healthcare & Life Sciences
- 02 Financial Services
- 03 Sports, Entertainment & Media
- 04 Energy, Oil & Gas & Utilities
- 05 Manufacturing & Industrial
- 06 Telecommunications
- 07 Defense & Government
- 08 Logistics & Supply Chain
- 09 Retail, CPG & E-Commerce
- 10 Agriculture & Food
- 11 Legal & Professional Services
- 12 Real Estate & Construction
- 13 Climate & Sustainability
Seventy thousand codes. Eighteen EHRs. One trial due yesterday.
Train directly on raw EDC exports, claims, and EHR tables. Build outcome prediction, cohort selection, and payer analytics on the data as it ships.
- ICD-10-CM carries more than 70,000 codes; SNOMED CT roughly 370,000 concepts. Train code-to-outcome models on raw data, no pre-built crosswalk required. CMS; IMO Health, 2025
- The average US health system runs 18 EHRs across facilities. Train across all of them in their native shapes, no unification step. Becker's Hospital Review, 2024
- CDISC SDTM/ADaM exist because mapping raw EDC takes quarters. Train on raw EDC directly and learn trial structure without the intermediate mapping.
The models have been ready for a quarter. The data has not.
Connect transaction tables, core extracts, and ledger pulls directly. Train AML, credit decisioning, and reg-reporting models on data in its native shape, no canonical layer in between.
- AML false-positive rates run above 90%: alerts scored on reconciliation artifacts, not transactions. Train AML models on raw transaction tables and score on transaction-level signal. PwC
- Basel III/IV, CCAR, CECL: every report pulls from a different set of source systems. Train one model across all of them on raw extracts.
- Real-time credit decisioning fails when customer data lives across systems that don't speak. Train on the raw multi-system tables and score in real time, no pre-assembly required.
Every vendor, a different schema. One model across all of them.
Connect tracking, scouting, and audience tables from any vendor. Train performance, scouting, and audience models on the raw multi-source data.
- Tracking data fragments across Catapult, Second Spectrum, Hawk-Eye: three schemas, three timestamp conventions, three ways of identifying a player. Train one performance model across all of them, no harmonization layer.
- Scouting event definitions don't match between Wyscout, InStat, and Opta. Train scouting models on every vendor's raw events; the model learns the equivalences.
- Nielsen, Comscore, and streaming first-party numbers never agree on what counts as "a view". Train one audience model on all three sources and stop the definitional fight.
LAS from 1994. WITSML from today. One well.
Connect LAS, DLIS, WITSML, SCADA, and historian exports as they ship. Train reservoir, asset-integrity, and trading models on raw multi-vintage tables.
- On a 30,000-sensor oil rig, only about 1% of data was ever examined, a pattern that still holds across producing facilities. Train predictive models on the full sensor stream, in its native shape. McKinsey, 2015
- A single facility typically runs multiple SCADA and historian systems from different vendors, each with its own schema. Train one operations model across all of them, on the schemas as they ship.
- Trading desks pull dozens of market feeds; onboarding a single new source is weeks of pipeline engineering. Connect new feeds in their native shape and train for the trading task in days.
Billions of sensor events a day. Almost none in your models.
Train predictive maintenance, yield optimization, and digital-twin models directly on raw sensor and MES tables from any protocol or vendor.
- Industry research places sensor data reaching analytics below 5%. Train on the other 95% directly, in its native shape. No integration layer required. McKinsey, 2015
- Five to ten equipment protocols per factory: OPC-UA, Modbus, ProfiNET, EtherNet/IP, MQTT, MTConnect. Train one model across all of them, no unification step.
- Digital-twin programs spend a year or more in the "data integration phase" before any modeling begins. Train on the raw historian and MES tables directly.
Three decades of stacked systems. One model trains across all of them.
Train CDR analytics, post-merger BSS/OSS, and customer-360 models on raw exports from every vendor and every generation.
- Amdocs, CSG, and proprietary platforms stacked on top of each other since the '90s. Train on every layer in its native shape, no reverse-engineering required.
- Billions of CDRs per day at Tier-1 scale across Ericsson, Nokia, Huawei, Samsung: every vendor's schema different. Train one CDR model across all of them, no parser to maintain.
- Post-merger integration programs take years and consume hundreds of engineers. Train across both estates from day one, no mapping committee.
SIGINT. HUMINT. GEOINT. One model trained across them.
Train multi-INT assessment, joint-readiness, and portfolio-review models on raw tables from every service and allied source.
- NATO has 32 member nations (Finland joined 2023, Sweden 2024), each with different data formats and reporting standards. Train one allied-readiness model across all of them, no canonical translation. NATO.int
- Three service logistics systems (GCSS-Army, NALCOMIS, CAMS) with zero interoperability. Train one joint-readiness model across all three, no spreadsheet in between.
- Classification boundaries fragment the picture; analysts reconstruct it by hand. Train separately in each compartment on the data in residence, no cross-boundary pipeline.
Many hands. Many formats. One model reads every leg.
Train ETA prediction, route optimization, and customs-compliance models on raw multi-carrier tables across every mode.
- Every trading partner implements ANSI X12 or EDIFACT differently. Train one model on every partner's flavor, no mapping per carrier.
- Hundreds of carrier schemas across APIs, portals, and EDI. Train ETA and exception-detection models on the raw feeds, no harmonization layer.
- Ocean → truck → rail: each leg a different tracking ID, event code, and timestamp format. Train one end-to-end visibility model across all three legs.
Walmart ships one way. Kroger another. Your model needs one answer.
Train demand-planning, trade-promotion, and category models directly on raw retailer and syndicated tables in their native shapes.
- Walmart Retail Link, Kroger 84.51°, Target Partners Online, Amazon Vendor/Seller: every retailer ships data in a different shape. Train one model across all of them, no per-retailer pipeline.
- NielsenIQ and Circana use proprietary category trees that do not reconcile with your internal hierarchy. Train across all three taxonomies; the model learns the equivalences.
- Trade promotion optimization loses millions a year to data plumbing. Train TPO models on raw POS, syndicated, and shipment tables and start scoring promotions on day one.
Three OEM portals. Two soil-lab templates. One model across them.
Train precision-ag, traceability, and commodity-desk models on raw telemetry, soil tables, yield maps, and shipment records as they ship.
- John Deere alone runs over one million connected machines; Case IH and AGCO operate their own portals. Train across every OEM's export in its native shape, no consolidation required. Deere investor day, 2025
- Precision agriculture needs five or more tabular sources per field: soil test tables, weather feeds, derived-index tables, yield maps, equipment telemetry. Train one yield-and-input model across all of them.
- Farm-to-fork traceability spans five to seven parties; each handoff reshapes the data. Train traceability models on every party's export shape, no canonical chain-of-custody schema.
Every engagement is a new ERP. Same model, fine-tuned per client.
Train audit, multi-jurisdiction tax, and eDiscovery models directly on each client's ERP, billing, and metadata exports.
- SAP, Oracle, NetSuite, QuickBooks, Sage, Datev: every audit client runs a different ERP with a different chart of accounts. Train on each client's raw exports, no chart-of-accounts mapping.
- Firm-wide billing across Aderant Expert, 3E, and Elite takes a multi-month integration project that is stale the week it ships. Train one billing-analytics model across all three, on raw exports.
- eDiscovery metadata from Exchange, Gmail, Slack, and cloud storage: different fields, different date formats, different structures for every matter. Train relevance and review-prioritization models on the raw metadata, per matter.
3,143 counties. 3,143 different schemas.
Train AVMs, cross-portfolio analytics, and cost-estimation models on raw county records, MLS feeds, and portfolio exports nationally.
- The US has 3,143 counties and county-equivalents, each with its own schema for property records. Train AVMs across every county in its native shape, no parser per jurisdiction. US Census Bureau
- Hundreds of MLS systems; RESO standards help in theory, not in practice. Train listing and absorption models across every MLS as it ships, proprietary fields included.
- Portfolios span Yardi, RealPage, MRI, AppFolio. Train one portfolio-analytics model across all of them, no monthly reconciliation.
Scope 3 lives in many supplier formats. CSRD lands regardless.
Train CSRD/ESRS reporting, emission-factor matching, and carbon-credit verification models directly on raw supplier disclosures and factor tables.
- Scope 3 emissions average about 75% of a company's footprint, per CDP. That data comes from hundreds of suppliers, each in a different tabular shape. Train one Scope-3 model across every supplier's format. CDP
- CSRD sweeps in roughly 50,000 companies across phased waves; California SB 253 adds more than 5,300 US operators and SB 261 more than 10,000. Train one reporting model per framework, on raw operational data. European Commission; Persefoni / PwC
- Emission factors from EPA, DEFRA, GHG Protocol, and ecoinvent shift by geography, year, and grid. Train factor-matching models on the raw factor tables and your activity data; the model picks the right factor per row.
Invariant across every deployment.
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Foundation model, fine-tuned per customer
Schema is the shared base. Each fine-tune is a separate instance with your data and your weights, fully deletable on request.
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Native multi-table, multi-source input
Train on raw tables from any vendor, format, or system. Schema reads them in the shape they ship in, columns, types, and relationships included.
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Vertical and agentic AI on top
Build outcome prediction, recommendation, and agentic workflows on Schema fine-tuned to your data. Same architecture across every vertical above.
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Deploy where your data lives
Run inside your infrastructure for regulated or enterprise deployments: on-prem, VPC, or an accredited cloud boundary.
Bring any tabular data.
Leave with a working vertical or agentic AI.
Bring a sample from your stack: clinical, financial, industrial, anything. Train on your data and ship vertical and agentic AI.