From fragmented telemetry to autonomous infrastructure.
We build the real-time telemetry, observability, and agentic operations layers that turn raw infrastructure data into autonomous decisions. Anchored in production deployments at T-Mobile and our own EdgeTelemetry product.
Build the path from
telemetry to a decision.
Ingest
Collect host, network, power, and cooling signals with source lineage.
Normalize
Unify schemas and expose missing or stale inputs.
Interpret
Correlate operational context with checks and bounded reasoning.
Resolve
Deliver reviewable recommendations to the responsible operator.
Modern data center operators, telecom networks, and infrastructure environments generate enormous volumes of telemetry — GPUs, hosts, cooling systems, power, network fabric, security events. The data exists, but it's fragmented across vendors, inconsistent in schema, and slow to become trusted and actionable.
The result: operators run blind during deployment, debug reactively rather than proactively, and can't move toward the autonomous operations that AI workloads demand. Capital sits idle. Incidents take hours instead of minutes. SREs burn out maintaining glue code between dashboards.
What we do
We design and build the unified telemetry, validation, and reasoning layers that turn this fragmented data into operational ground truth. Our work spans three phases, depending on where the customer is:
1. Unified telemetry & observability
Real-time ingestion from heterogeneous sources, schema normalization, validation logic, and the data infrastructure to make telemetry queryable at scale. Built on production-tested stacks: Kafka, Spark, Airflow, Flink, dbt, and modern data warehouses.
2. Real-time SIEM & threat detection
Distributed pipelines processing high-volume security and operational events with sub-second latency. Detection logic that scales horizontally. Improved mean-time-to-detect and reduced false positive rates through scalable analytics.
3. Agentic operations & autonomous remediation
Reasoning layers built on Claude that interpret telemetry, follow operational playbooks, execute remediation through tool use, and escalate appropriately to humans. The architecture pattern enterprises actually trust because it's defensible in front of safety, compliance, and reliability reviews.
Where Claude fits
Claude is the reasoning layer at the top of the infrastructure stack. For telemetry interpretation, Claude reads normalized operational state and explains anomalies in plain language — correlating signals across GPU, cooling, power, and network that rule-based systems miss. For autonomous remediation, Claude follows runbooks via tool use: restarting services, updating configs, filing tickets, and escalating with full incident context. The autonomous scope is explicit and auditable, which is what makes it deployable in regulated and high-reliability environments. We are an Anthropic partner.
Telemetry → unified layer → reasoning → action.
The pattern we deploy across customers: heterogeneous source ingestion into a unified schema, validation and enrichment, a reasoning layer (typically Claude) that interprets state and executes playbooks via tool use, and clear human escalation paths.
Designed to evolve as the underlying models improve, not be rebuilt with each generation.
// pattern
01 · Source ingestion — GPU, host, cooling, power, network, security events. Ingested via Kafka, NiFi, or vendor APIs. No transformation at the edge.
02 · Unified schema — Normalization to a consistent operational schema. Validation, enrichment, lineage tracking. Stored in a real-time-queryable layer.
03 · Reasoning layer — Claude reasons over telemetry state, runbooks, and historical incidents. Anomaly explanation, root-cause hypothesis, remediation planning.
04 · Action & escalation — Tool-use execution of remediation playbooks. Audit trail. Human escalation with full context. Continuous evaluation.
DehazeLabs brought our SIEM pipeline to telco scale — real-time threat detection across our entire network infrastructure with detection latency we hadn't thought possible.
Common questions about data center & infrastructure AI.
How long does it take to go from fragmented telemetry to a unified observability layer?
What telemetry sources can you ingest — do you support heterogeneous GPU and cooling vendors?
How does Claude fit into infrastructure agentic operations — what can it actually do autonomously?
What does GPU rack onboarding automation actually look like in production?
Can you build on top of our existing SIEM, or do we need to replace it?
What's a typical engagement size and team shape for data center AI work?
Building something operationally critical?
Tell us about your infrastructure roadmap. Initial conversations are free and frank — we'll tell you whether we're the right fit, and what we'd need to deliver if we are.