supplier_evidence
Account-scoped · schema-on-write · history on
source_data · auto_create_schema
Excel upload
{ "name": "Acme Parts", "tax_id": "FI-12345" }
WebApp form
{ "company": "Nordic Supply", "vat_number": "SE-98765" }
REST API
{ "supplier": "Baltic Co", "country": "EE", "contact_email": "ops@baltic.ee" }
Built-in data layer
“Operational data for workflows—not another database project.”
Platform
Datapools — operational data for your workflows
Operational records your workflows and AI share—schemas and JSON rows wired to Process Automation, the REST API, and controlled AI. No separate database project for typical workflow data.
Adoption context
- You need this when
- Operators and AI must share operational records with clear history—before data lands in the warehouse or an audit pack.
- Often bundled with
- Process Automation for live workflows on one platform.
- Not required if
- Downstream systems of record are enough for v1 and you do not need platform-owned operational schemas yet.
Related workflow: Customer & vendor intake · Adoption path
Datapools are operational data built into Tealfabric—not a separate database product.
Teams need structured records for subscribers, supplier evidence, workflow outputs, and AI context—but standing up a warehouse or master data project slows down the workflows that actually ship value.
Datapools give you schemas, JSON document rows, merge-on-write ingestion, partial updates, and access rules—already wired to ProcessFlow, the API, and read-only AI access.
- Built-in data layer for automations—schemas, records, queries, and rules inside the platform
- Wired to workflows, the REST API, and controlled AI—no extra database to provision
- Schemas that grow with your data as new fields arrive on write
- Account isolation, change history, access rules, and quality scoring built in
- AI reads operational data through validated read-only query tools
- Virtual schemas connect external systems without copying everything first
Lead use cases
Operational data patterns teams ship first.
From supplier intake to AI-assisted queries—Datapools sit at the center of your workflows, not beside them.
Schema grows as data arrives
Different sources become one shared schema.
Excel uploads, WebApp forms, and API payloads arrive with different field names. Datapool adds new fields to the schema on write—then workflows, APIs, and AI query unified records without modeling every source upfront.
supplier_evidence
Account-scoped · schema-on-write · history on
source_data · auto_create_schema
Excel upload
{ "name": "Acme Parts", "tax_id": "FI-12345" }
WebApp form
{ "company": "Nordic Supply", "vat_number": "SE-98765" }
REST API
{ "supplier": "Baltic Co", "country": "EE", "contact_email": "ops@baltic.ee" }
03 · Core capabilities
Ship-ready operational data—schemas, ingestion, API, and process integration.
Core features are production-ready today: account schemas, schema-on-write, workflow access, partial updates, scoped REST API, and account isolation.
Create and manage schemas in the app—browse data in a spreadsheet-style table with sort, search, and pagination. Every row has an ID, timestamps, and stays inside your platform account.
supplier_evidence
12 fields · 248 rows
browse · sort · paginate
Named schemas with typed fields, JSON rows, and isolation on every operation.
Rules and power features
Rules and power features without a separate master data stack.
Access control, change history, quality scoring, lineage, virtual schemas, and record matching—API-first for teams that outgrow simple JSON storage.
Access control
Schema- and field-level read, write, and query rules by role.
API-first rules for teams that need more than open JSON storage—without standing up a separate master data management stack.
role · field rules
Change history
A trail of schema, data, and query operations.
Operational changes are traceable for compliance and post-incident review.
change trail
Data quality
Validation rules and quality scores on insert and update.
Score incoming records at write time—useful for supplier evidence and intake programs.
score on write
Where data came from
Source and transformation metadata on writes.
Graph and path APIs record where data came from and how it changed—AI and workflows share the same history.
source → row
Virtual schemas
Connect external tables with query cache and time-to-live settings.
Connect external systems without copying everything first—read-through and write-through cache for virtual queries.
cache · TTL
Match and link records
Match and link records by key fields.
Manual canonical links and entity resolution for teams outgrowing simple JSON document storage.
match · link
AI read access
AI can read your business data responsibly.
Controlled AI discovers schemas first, then runs read-only queries against your data—filtered SELECT-style reads with limits. Operational workflow data, not a separate analytics warehouse.
- Discover first. Schema metadata is exposed before any query runs.
- Read-only by default. SELECT with limits inside chat and automation—not side copies.
- Same tenant rules. AI uses the same account isolation as workflows and APIs.
Trace AI · read-only Datapool tools
discover_schemas → query_datapool
Tool: discover_schemas
- supplier_evidence
- customer_sync_queue
- subscribers
Simulated — AI discovers schema first, then queries inside your rules
Who it is for
Business operations, data owners, and integration leads who need structured operational records without standing up a warehouse or master data project first— supplier intake, subscriber lists, workflow outputs, and AI context on one platform.
Related workflow: Customer & vendor intake. Platform depth: Process Automation, AI agents, Integrations, Governance.
Datapools are operational JSON storage on your platform account—not a petabyte data lake or full analytics warehouse. A shared Datapool library across accounts and object schema type are on the roadmap.
Operational data, not another database project
See Datapools inside workflows and AI-assisted steps.
We walk through schema-on-write ingestion, partial updates, account isolation, and read-only AI access—on the same platform as your connections and workflows.
Pilots start with one live workflow—system connections, steps, and full history.