AI systems fail differently from traditional software. A rogue agent 
makes an innocent-looking database query that chokes the DB, congests 
the network, and triggers a cascade of downstream failures — none of 
which appear in the agent's own logs. Meanwhile, your telemetry bill 
keeps climbing while your team still can't find root cause fast enough.

FFWD is the AIOps platform built for this environment.

We give engineering teams four interlocking capabilities on one stack:

High-performance telemetry pipeline — ingest, filter, enrich, and 
route 500,000+ events per second, 20+ TB per day, at a fraction of 
Splunk or Datadog cost. Built in Rust. No vendor lock-in.

Full-stack anomaly detection — our Holistic Log Model applies six 
distinct ML model categories simultaneously across logs, metrics, 
traces, GPU telemetry, and container events. No rules to write. No 
topology map required. Operational from day one.

Cross-domain correlation — when anomalies appear in unrelated systems 
at the same time, FFWD's marker correlation engine finds the 
statistical link. Root cause hidden in a GPU thermal trend, a sequence 
anomaly in a Python service, and a network rate spike? FFWD connects 
them.

Agent Assurance — the only platform that runs a pre-task risk score 
on autonomous AI agents before they execute consequential actions, and 
a confidence score after. A Go/No-Go gate for every agent, grounded in 
live full-stack anomaly intelligence — not static rules.

These aren't four products. The pipeline feeds the anomaly model. The 
anomaly model powers the risk score. The risk score is only 
trustworthy because the data underneath it is complete.

Used by engineering teams who are deploying AI agents at scale and 
need to trust what they're doing.

MCP-native. eBPF zero-instrumentation. Air-gap and private cloud ready.

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