Pipeline
Connect controllers, instruments, and operational systems through standard connectors, with edge buffering that prevents data loss during outages.
AI-Ready Data Platform
QData
AI-Ready Data Platform
QData
Structured, unstructured and document data connected — shaped for RAG, CAG and TAG based AI to use.
Unstructured Processing
Organize documents containing tables and drawings into meaningful units, then turn them into searchable data with OCR and speech recognition.
Semantic Knowledge
Unify names and units with a shared data model and terminology while supporting relationships and semantic search.
Data and Model Operations
Track data quality and change history, retraining and validating AI models when the underlying data changes.
Overview
Data piled up in different formats and to different rules, brought together
QData connects what is scattered across systems and teams, and puts it in a form analysis and AI can work with.
Collection and integration
PLC, sensors, MES and ERP connected, with differing protocols and formats turned into one common input
Format and meaning standardised
Names, units and relationships that differ by machine, brought to one standard
Building AI-ready data
Shaped for document retrieval (RAG, CAG), data querying (TAG) and predictive models
Analysis and services
Dashboards and live queries, ready for QFactory and AgentQ to useArchitecture
How the data becomes usable by AI
Scattered sources are connected, refined through six steps, shaped for their purpose, and carried on into services.
Data and System Connectivity
Data already available across the organization
Structured Data
ERP · MES · DB · CSV · API
Unstructured Data
PDF · DOCX · PPT · Email
Industrial Data
PLC · Sensors · Time Series · Video
Knowledge Data
Manuals · Reports · Standards · History
QData Core Pipeline
Six steps that turn industrial data into AI-ready data
Connect
Collect from multiple sources
Parse
Analyze formats and extract structure
Clean
Remove duplicates and repair errors and gaps
Standardize
Align schemas and measurement units
Contextualize
Add domain context and relationships
Ready
Deliver consistent, contextualized data
Quality + consistency + context → optimized for AI
AI Data Engine
Generate purpose-built data from one foundation
RAG Ready
Data optimized for retrieval and evidence
CAG Ready
Context optimized for repeated reference
TAG Ready
Structured data for exploration and aggregation
AI-Ready Data Outputs
Generate purpose-built data from one foundation
Knowledge Data
Evidence-oriented retrieval data
Context Data
Domain context · policies · manuals · rules
Structured AI Data
Tables · semantic schemas · features
Training Data
Instructions · Q&A · fine-tuning datasets
AI Applications
Connect search, recommendation, learning, and operations
LLM · sLLM
Commercial APIs and on-premise small models
AI Agents
Answer with evidence and take action
Predictive Models
Anomaly, demand, and quality prediction
Cubeon
Connect decisions to approval and execution
Features
What turns data into an asset
Every step from collected data to findable knowledge, managed in one place.
Processing Status
Track every document from initial analysis through search availability.
- Stage Status
- Track analysis, chunking, and semantic conversion by document
- Change Detection
- Automatically reflect additions, updates, and removals
- Privacy Flags
- Mark documents containing sensitive information
Search Unit Optimization
Document splitting changes retrieval quality. Evaluate each result with measurable indicators.
- Quality Metrics
- Check length, symbols, duplication, and semantic completeness
- Document Diagnostics
- Classify threshold violations as caution or warning
- Chunking Rules
- Manage size, overlap, and splitting methods globally
Vector Quality Validation
After turning text into searchable vectors, continuously monitor throughput, latency, and indexing.
- Model Status
- Compare vector dimensions, throughput, and latency
- Index Status
- Review collections, disk usage, and index types
- Reranking Review
- Compare candidates for improved retrieval quality
Failed Document Recovery
Find documents halted by encryption or size limits and process them again automatically or manually.
- Failure Stage
- Record whether analysis, chunking, or conversion stopped
- Automatic Retry
- Manage the queue by retry count and priority
- Manual Branch
- Notify an owner when automatic recovery is unavailable
Role-Based Access Control
Limit the documents AI can reference by user role and safely mask personal information.
- Folder Permissions
- Set organization-wide, departmental, or limited visibility
- Privacy Masking
- Identify and mask sensitive fields
- Processing Status
- Review chunk counts and status by document
Standards & Governance
Built on the standards the industry already uses
Designed with reference to industrial interoperability standards and established data management practice.
Interoperability Standards
Use OPC UA and ISA-95 to integrate heterogeneous equipment data
Data Governance
Track source and trust through catalogs, lineage, and quality rules
Granular Access
Apply data- and query-level permissions with audit logs
Industrial Interoperability
Align equipment semantics with international standard models
Quality Rules
Automate missing, anomalous, and duplicate data validation
Open Catalog
Catalog data assets for reuse across the organization
Ideal Use Cases
It suits organisations like these
For organisations whose data is too scattered to start with AI at all.
Manufacturers blocked by data cleanup
Teams that repeat cleansing because tags and units differ between lines, leaving little time to improve models.Operations with separated networks
Environments where equipment and business data are disconnected, making root-cause analysis difficult.Organizations with scattered technical documents
Teams unable to quickly find evidence across standards, inspection logs, and equipment manuals.