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Platform module

Data House

A shared data foundation for documents, media and enterprise records. Preserve identity, versions and permissions across retrieval and AI operations.

Data management

  • File and API ingestion
  • Original and structured records
  • Text, vector and relationship search
  • Access, version and retention policies

Scope

Its role in the system.

Inside the application

Application workflow

Selected use cases, source information and operational outputs.

Data ingestion

Files, media and API records are ingested with source identity. New data is prepared for processing while retaining its file and version references.

Data HouseScenario / 01
Interface conceptSchematic example

Data ingestion

Collection
Document
Media
API record
Source version
Decision boundary

Source access, action scope and human review are configured for the application.

  • Files, media and API records
  • Source identity and version
OutputIngested data linked to its source

Data structure

Raw sources and shared field structures remain separate. Standardized values link back to the original record and transformation.

Data HouseScenario / 02
Interface conceptSchematic example

Data structure

Information fieldControl
Raw sourceSource format
Shared fieldShared representation
Record identityTransformation rule
Source relationshipHuman review
Decision boundary

Source access, action scope and human review are configured for the application.

  • Raw source records
  • Field and transformation rules
OutputStandardized records linked to original values

Knowledge access

Text, vector and relationship retrieval address different search needs. Access to results follows the permissions on their sources.

Data HouseScenario / 03
Interface conceptSchematic example

Knowledge access

Source record
Text search
Vector retrieval
Relationship search

Connections represent shared sources or relationships, not sequential processing steps.

Decision boundary

Source access, action scope and human review are configured for the application.

  • User query
  • Accessible records
OutputSearch results with source references
01 / INPUT

Documents, media and records

Source identity and access scope remain part of the work.

02 / PROCESS

Ingestion, structuring and indexing

The module operates within the configured application and control boundaries.

03 / OUTPUT

Source-linked retrieval and access

The next step uses the result with its source and execution context.

Control design

Progress, exceptions and decisions.

Reference behavior for implementation; each deployment needs its own configuration and verification.

Source received

Keep the file and source identity together. Link the structured record to the same version.

Incomplete or conflicting record

Keep sources separate until a match is confirmed. Show conflicting fields for review.

Access boundary

Retrieval and model context use only sources the user may access.

01

Preserve provenance

Original documents, extracted fields and derived results are stored separately. Each result can be traced to the file and version that produced it.

02

Storage that fits the data

Text, images and time series follow appropriate processing paths. Relational storage, object storage, vector retrieval and knowledge graphs are combined as needed.

03

A shared foundation

Retrieval, source-linked answers and evaluation use the same data layer. Access and retention are configured within the deployment scope.

Technical operating model

A data layer that can return to its source

Without a known source version, new information can become mixed with old interpretation. Data House makes this distinction in the data model: a file, extracted fields, an index and a derived result are not the same object.

Component relationship / schematic

Source identity and version

Preserve the evidence for an earlier review when a file changes.

Normalized field

Track original values, transformation rules and standardized values together.

Retrieval and relationship result

Apply source access boundaries to text, vector and relationship retrieval.

01

Connecting it to an application

Define incoming data, resulting records and the people or systems that use them together. A functioning component alone is not an acceptance criterion for the entire workflow.

02

Checking a change

When a schema, tool or model changes, existing use cases are tested again. Errors, permissions and human-review paths matter as much as successful execution.

Components and operating approach

A data layer that can return to its source

Select a topic to explore the relationships between information, responsibility and delivery.

01

Source identity and version

Preserve the evidence for an earlier review when a file changes.

Original value09.09.2026DEMO-DOC / v2
Standard value2026-09-09Source link retained
Source version
DEMO-DOC / v2
Date normalization
09.09.2026 → 2026-09-09
Record relationship
Document → organization
02

Normalized field

Track original values, transformation rules and standardized values together.

Original value09.09.2026DEMO-DOC / v2
Standard value2026-09-09Source link retained
Source version
DEMO-DOC / v2
Date normalization
09.09.2026 → 2026-09-09
Record relationship
Document → organization
03

Retrieval and relationship result

Apply source access boundaries to text, vector and relationship retrieval.

Original value09.09.2026DEMO-DOC / v2
Standard value2026-09-09Source link retained
Source version
DEMO-DOC / v2
Date normalization
09.09.2026 → 2026-09-09
Record relationship
Document → organization

An operating-model illustration, not live operations or measured performance results.

Connected modules

Explore the rest of the platform.

Workflow orchestration

Orchestrator

Coordinate models, tools and people within the same operation. Preserve state and manage errors, retries and review across long-running work.

Explore the module ↗Enterprise integration

WebKit

Connect defined operations in ERP, CRM and enterprise applications to AI workflows, with clear input, output and access boundaries.

Explore the module ↗AI processing modules

AI Modules

Combine document, image, speech, sensor and knowledge processing. Turn different source formats into usable, source-linked data.

Explore the module ↗Human review and execution history

Sessions + HITL

Keep sources, processing steps and human decisions together. Give responsible users the context needed to review an outcome.

Explore the module ↗Model evaluation and adaptation

Model Adaptation

Prepare evaluation and training data from verified examples. Compare changes on consistent tasks and track model versions.

Explore the module ↗

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