Service
Network & telemetry AI
We build AI that reads machine data the way senior engineers do — fault diagnosis and anomaly detection over device logs and network telemetry, at fleet scale.
What this covers
- Log-based fault diagnosis — systems that ingest raw device and network logs and produce ranked, human-readable diagnoses with the evidence lines that support them.
- Anomaly detection — baselines per device model and firmware, drift and outage detection across fleets of thousands to millions of endpoints.
- Diagnostic assistants — RAG over device manuals, past incidents, and vendor documentation so support engineers resolve faults without escalating.
- Telemetry pipelines — the unglamorous plumbing done right: parsing zoo-like log formats, sessionizing, and feature stores that keep models honest.
Where it works well
Network equipment vendors, ISPs and carriers, smart-home and IoT platforms, and operations teams drowning in device logs. Our founding team has shipped AI diagnostics in telecom-grade WiFi environments, so we know what carrier reliability expectations feel like.
Common questions
- Our logs are messy and undocumented. Is that a blocker?
- No — it is the normal starting condition. Parsing and normalizing heterogeneous log formats is treated as first-class engineering work in the engagement, not an assumption we make and skip.
- Does this require sending device data to a cloud LLM?
- Not necessarily. Diagnosis pipelines can run on-premise or in your VPC, and we design data flows with carrier-grade privacy expectations in mind.
- Can it work in real time?
- Yes, within honest limits. We typically split the system into a fast streaming layer for detection and a slower reasoning layer for diagnosis, so alerts are timely and explanations are thorough.