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.
Telemetry Pipeline is the new data foundation
Why AI infrastructure operations needs a telemetry layer, not another tool.
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.
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.
Ingest from anywhere, in any format. No format wrangling, no bespoke collectors.
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.
- 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.