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High Performance Telemetry Pipeline

Collect once from every layer. The data foundation for full-stack AI infrastructure operations.

FFWD Pipeline collects natively from every layer of your stack — GPU, network, container, application — at AI scale, without gaps. It's the cross-domain telemetry foundation that FFWD's anomaly engine and agent assurance depend on to see what no single monitoring tool can. Built on Rust, deployed privately, pricing that doesn't scale with your data.

The case for a pipeline layer

Telemetry Pipeline is the new data foundation

Why AI infrastructure operations needs a telemetry layer, not another tool.

01 The Problem

AI scale breaks per-GB economics

AI infrastructure generates orders of magnitude more telemetry than traditional software — GPU metrics, model inference logs, agent traces. At TB/day, per-GB destination pricing turns observability into a CFO line item. And every tool wants its own feed — redundant collection, spiralling cost, vendor lock-in.

02 The Pipeline

Collect once. Ship to many.

FFWD sits at the aggregation point. Collect from any source, in any format. Route to any combination of destinations — Splunk, Datadog, Elastic, Kafka, S3, your data lake — now or in the future.

Reduce — 50%+ volume cut Transform in-flight Route by content / priority No destination lock-in
03 The Foundation

A pipeline is infrastructure, not a tool

FFWD's anomaly engine and Agent Assurance evaluate cross-domain state — GPU, network, container, application — because the pipeline already collects all of it. Add a new analytics tool, a new data lake, a new anomaly engine: instant. The pipeline isn't a companion to those systems — it's the data layer they run on.

Reshape Data In-Flight, Not at the Destination

The most expensive place to clean up telemetry is after it arrives. FFWD's transforms run inside the pipeline — between collection and destination — so what reaches your analytics is already parsed, enriched, normalised, and right-sized for whatever consumes it.

  • Parse Extract structured fields from any log format. FFWD's AI-assisted parser learns formats from samples, or configure parsing directly from your AI app via MCP. No regex skills required.
  • Enrich Attach context from lookup tables, live APIs, geo-IP, asset inventories, or other in-flight streams. Decorate raw events with the metadata your analytics actually need.
  • Cast field types Convert values to the types destinations expect — strings to integers, numbers to enums, JSON to columns. Get the schema right before it lands.
  • Normalise timestamps Mixed formats and timezones unified at ingest. ISO-8601 UTC, epoch, or your destination's flavour — picked once, applied everywhere.
  • Reshape per destination Flat key=value for Splunk, tags for Datadog, nested JSON for Elastic, Parquet for the lake — same source, different shapes, one pipeline.
  • Reduce without losing signal Aggregate high-cardinality counters, deduplicate, sample with statistical fidelity, drop fields nobody queries. 50%+ volume cuts that keep what matters.
  • Merge and correlate Join related streams in-flight: pair request logs with response traces, correlate auth events with downstream API calls.

These are common starting points. FFWD's transform vocabulary covers conditional logic, regex, schema validation, custom scripting, multi-stream joins — configured visually or through your AI agents via MCP. If you can describe the transformation in plain terms, it's a single config block.

Cost Control

Stop Paying SIEM Prices to Store Noise

Splunk, Sentinel, QRadar and Chronicle all bill by the gigabyte ingested. FFWD drops, routes, and right-sizes telemetry in-flight — so you pay to analyse signal, not to warehouse noise.

Typical Reduction
50–80 %
less volume reaching your SIEM
1 TB/day in → 200–500 GB billed
Ingested 1 TB Filter · Route To SIEM −50%
Drop the noise
Filter debug logs, health checks, heartbeats and duplicate events before they cross a metered boundary. The cheapest gigabyte is the one you never send.
Route by value
Security-relevant events go to the SIEM. Everything else lands in low-cost object storage — still parsed, still searchable — at a fraction of per-GB SIEM pricing.
Rehydrate on demand
Keep full-fidelity raw copies in Parquet on S3 for compliance and audit. Replay them into the SIEM only when an investigation actually needs them.

Same visibility. Same retention. A SIEM bill that reflects what you query — not what you happen to generate.

OT + INDUSTRIAL

Bring Agentic Intelligence to OT and Industrial

FFWD reads directly from the same industrial sources that already feed your existing SCADA. Apache PLC4X is integrated natively into FFWD’s adapter layer, providing direct support for Modbus, Siemens S7, EtherNet/IP, BACnet and more, alongside OPC UA, MQTT and the full 50+ connector set. The telemetry branches at the source: the existing PLC-to-SCADA control path continues untouched, while a separate, read-only stream feeds FFWD for collection, correlation and anomaly detection. FFWD can return scores and advisory tags as optional enrichment, but never enters the control loop. Nothing is replaced, and nothing needs to be re-certified.

Most telemetry products treat OT as an egress problem — get the data out to somewhere it can be processed. FFWD does the work in place.

Parallel agentic stackAir-gapped · On-site
FFWD runs as a parallel read-only intelligence path alongside SCADAExisting PLC and telemetry sources continue to feed the existing SCADA control path. A separate read-only tap feeds FFWD, which provides on-site agents and can return anomaly scores and advisory tags without touching the control path.INDUSTRIAL PERIMETEREXISTINGPLC + TelemetryPLC4X · OPC UA · MQTTCONTROL PATH — UNTOUCHEDEXISTINGSCADACONTROL + MANAGEMENTREAD-ONLY TAPFFWDCOLLECT · CORRELATE · DETECTDIRECT QUERYREAD-ONLYOn-site agentsNEXT TO THE DATASCORES + ADVISORY TAGS
A parallel intelligence stream. Existing controls remain exactly where they are.
01

Air-gapped by design

Collection, correlation and anomaly detection all run inside the perimeter, with no cloud dependency and no outbound control-plane connection. Sites that cannot reach the internet at all still get the full capability.

02

Data sovereignty

Operational data never leaves the site. Only query results cross the boundary, so operators under strict residency or export constraints keep their data exactly where it is.

03

The agentic layer, on-site

An agent reasoning over plant telemetry belongs next to the data, not a round-trip away. With on-premises inference, the entire loop closes inside the fence — which is what makes a read-only agentic layer approvable in critical infrastructure.

Architecture

Built in Rust. Engineered for AI-Scale Throughput.

A two-tier architecture purpose-built for the data volumes AI infrastructure generates — without the compute footprint of legacy stacks.

Universal Collector — Edge
100K+
events / sec per instance
  • Rust-native, multi-threaded, GC-free
  • Configure with your AI app via MCP — no regex required
  • Deploy as Docker container or Kubernetes pod
Data Cluster — Central
Millions/s
events / sec — proven throughput
  • Parquet / Arrow columnar storage, efficient compression
  • Minimal compute footprint — runs on a fraction of competitors’ hardware
  • Built-in search, dashboards, alerts — no forwarding required
50+ Native Source Formats

Ingest from anywhere, in any format. No format wrangling, no bespoke collectors.

Cloud
AWS S3 SQS SNS Kinesis CloudWatch GCP PubSub GCP Storage Azure Blob Azure Monitor
Messaging
Kafka / MSK AMQP MQTT NATS Redis Pulsar Splunk HEC
Infrastructure
Kubernetes Docker logs Host metrics JournalD Syslog Exec
Monitoring
OpenTelemetry Prometheus StatsD SNMP NGINX Apache Postgres Datadog MongoDB Okta
Data Sources
File Socket Stdin HTTP Client HTTP Server Fluent Logstash dnstap GNMI WebSocket
GPU Telemetry
NVIDIA Intel AMD Huawei Broadcom Apple Qualcomm
AI Apps via MCP
Parse and configure collectors using AI agents natively — describe the source, FFWD writes the config.
Auto-Parse
~100 Grok templates ready out of the box, plus full support for custom formats.
AI-native observability

Headless Observability direct from the Pipeline

FFWD Pipeline is MCP-first — AI agents query and configure it natively. But humans still operate the stack. Live dashboards, full-text log search, analytics, and alerting are all built into the pipeline. Same data layer, two ways to query.

AI
> query pipeline — service health, last 1h
Events / sec
1.24M
▲3.4% vs yesterday
Latency p99 — trend
182ms  ·  peak 14:32
Top services by errors
api-gw42 payment30 auth20 search11
  • Live Dashboards
  • Full-Text Log Search
  • Analytics & Aggregation
  • Native Alerting

Private Deployment

FFWD Pipeline runs entirely within your environment. On-premises, private cloud, or air-gapped — your telemetry never leaves your security perimeter. No SaaS dependencies. No data sovereignty concerns.

Multi-tenant architecture lets you run FFWD as private SaaS — serving multiple business units, sites, or customers from a single deployment with full data isolation. Edge collectors deploy at every site; the central cluster aggregates wherever you choose.

Collect everything. Detect across it all. Gate every agent action against what's real. That's the full-stack loop — pipeline, anomaly engine, and agent assurance, unified.