Ingestion
Monitor data ingestion volumes, throughput, source and connector status, and the document flow entering the environment.
Data Plane
Turn enterprise information into usable AI context.
The Data Plane manages and monitors how enterprise data moves through, is processed by, and is used within T-Flux Ultra.
It provides operational visibility across the complete information lifecycle: ingestion, parsing, embedding, vectorisation, retrieval, and use by models and workflows.
Ingestion→Processing→Embedding→Vector index→Retrieval→AI context
One information lifecycle
Know what data T-Flux is processing, where it is, and how effectively it is being used.
The Data Plane is designed for data administrators, knowledge managers, AI operations teams, and application owners. It combines operational signals with data quality, lineage, and permission controls so enterprise information remains useful, governed, and observable.
Lifecycle visibility
Follow data from source to outcome.
Processing
Track parsing, OCR, extraction, file-type success rates, versioning events, failures, retries, and dead-letter queues.
Embedding
See embedding generation, embeddings per second, queue backlog, and the stages that prepare information for retrieval.
Vectorisation
Manage vector database utilisation, index size and growth, shard health, replica status, and knowledge-base activity.
Retrieval
Measure retrieval performance, p50 and p95 latency, cache performance, slow queries, and permission-filtered results.
Use in AI
Understand how governed information becomes usable context for models, RAG, agentic workflows, and customer applications.
Operational signals
Measure the health, quality, and control of enterprise information.
Sources and connectors
Documents ingested by source connector, connector health, sync boundaries, and the rate at which enterprise knowledge is entering the platform.
Document quality
OCR volume and success rate, average OCR time, parsing success by file type, re-index events, and the reasons work cannot progress.
Knowledge-base health
Object storage, processed text, metadata, vector index growth, embedding throughput, shard health, and retrieval cache performance.
Access correctness
The proportion of queries with ACL filters applied, denied snippets, source visibility, permission controls, and data-access boundaries.
Answer integrity
Citation coverage, grounding confidence, low-evidence refusals, missing or invalid citations, and answer revision before export.
Operational exceptions
Queue depth, oldest message age, retries, failed processing, data-processing exceptions, and patterns requiring intervention.
Customer benefits
Make data operations visible and reliable for every T-Flux Ultra environment.
Understand what data T-Flux is processing
A clear view of sources, documents, pipelines, indexes, and knowledge bases gives data administrators and application owners an accurate operating picture.
Know where information is in the lifecycle
From ingestion through retrieval, teams can identify how data is being transformed, where it is waiting, and what needs attention.
Protect governed access to enterprise knowledge
Permission-filtered retrieval, source visibility, access controls, and lineage make data use reviewable and aligned with enterprise requirements.
Improve reliable AI outcomes
Data quality, retrieval performance, citations, grounding, and low-evidence refusal signals help customers manage the context used by models and workflows.
Manage and govern
Keep data operations resilient, controlled, and ready for review.
Storage and retention
Track raw documents, processed text, exports, metadata, retention-policy compliance, deletion failures, backups, and restore readiness.
Quality and governance
Review grounding and citation measures, corpus changes, policy activity, source lineage, and audit-ready data controls.
API and integrations
Monitor API activity by client or key, authentication failures, rate limits, key rotation, and webhook or callback health.
Reporting and action
Investigate data operations with the context needed to act.
Use the Data Plane to drill into connector performance, document-processing failures, embedding queues, vector index health, retrieval latency, source permissions, and data-quality trends.
Data operations can then be reviewed alongside workload, governance, audit, and security reporting for the customer environment.