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.
Modern data centers, 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.
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:
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.
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.
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.
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' team's expertise in building, deploying, and managing AI agents revolutionized our network optimization and elevated customer service efficiency.
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.