Telemetry Data Platforms
Unified ingestion and normalization of logs, events, alerts, metrics, identities, assets, and infrastructure data into one operational intelligence layer — on-premises, air-gap capable, policy-routed.
Fragmented signals, one intelligence layer
Missions produce telemetry faster than teams can make sense of it — and every source speaks a different language.
Logs from one vendor, metrics from another, alerts from a third, asset data in a spreadsheet. The result is an analyst switching between consoles, manually joining context, and missing the correlation that matters. The data is all there. The intelligence is not.
Neural Data Fabric designs and modernizes telemetry platforms that transform fragmented signals into usable operational intelligence — unified ingestion, stream processing, normalization, and identity-aware correlation across every source the mission runs.
The telemetry pipeline
Multi-Source Ingestion
Splunk, Cribl, Elastic, Wazuh, syslog, OpenTelemetry, custom APIs — every source into one governed layer.
Stream Processing
Real-time filtering, routing, shaping, and enrichment pipelines with store-and-forward during degraded connectivity.
Normalization Engine
Field extraction, timestamp alignment, schema enforcement, and CIM mapping for consistent, comparable data.
Cross-Source Correlation
Identity-aware correlation across logs, alerts, metrics, and asset data — one question, every source at once.
Policy-native routing
Telemetry moves through the platform on the basis of policy, not connectivity. Every data movement is evaluated against mission context, sensitivity, destination authority, and threat posture before any packet moves. A log that should stay local stays local. A threat indicator authorized to federate federates. An unauthorized request gets denied, logged, and reported.
The policy is the pipeline. Raw telemetry stays local — only what the mission needs to know crosses the boundary.
Frequently asked
What is a telemetry data platform?
A telemetry data platform is the layer that ingests, normalizes, and routes every signal a mission produces — logs, events, alerts, metrics, identities, assets, and infrastructure data — into one governed operational intelligence layer.
Which sources do you ingest?
Splunk Universal Forwarder, Cribl Stream, syslog (RFC 5424), Windows Event Forwarding, HTTP Event Collector, Linux auditd, NetFlow/IPFIX, DNS/DHCP/proxy/VPN, EDR, OT and mission-system telemetry, cloud event streams, and custom collectors.
What is CIM normalization?
CIM — the Splunk Common Information Model — is a set of field names and schemas that make data from different sources comparable. Normalizing to CIM enables cross-source correlation and consistent dashboards across your entire stack.
Why normalize telemetry?
Because fragmented, source-specific schemas make correlation slow and investigations brittle. Normalized telemetry means one question can be asked across logs, alerts, metrics, and asset data at once.
Is this on-premises or cloud?
On-premises, air-gap capable. The telemetry path is designed for environments where the network is not always there — store-and-forward during degraded connectivity, edge filtering before transmission.
Explore the fabric
Request a telemetry platform assessment
Source inventory, normalization design, and a routing architecture for your existing telemetry estate.
Splunk · Cribl · OpenTelemetry
On-Premises · Air-Gap Capable