Production AI · Data Engineering · Edge Systems

Production AI for the physical economy.

DehazeLabs builds the telemetry, perception, and multimodal AI systems behind data centers, telecommunications networks, robotics fleets, smart cities, and industrial operations. From GPU rack onboarding to robot log pipelines to real-time SIEM — we ship the systems that make AI work in the real world.

90%+ manual review reduction in production multimodal pipelines
Real-time SIEM at telco scale — distributed, sub-second detection
GPU rack onboarding from weeks to hours
Edge perception pipelines processing real-world video at scale
Production RAG, vector search, agentic workflows on LangChain & LangGraph
90%+ manual review reduction in production multimodal pipelines
Real-time SIEM at telco scale — distributed, sub-second detection
GPU rack onboarding from weeks to hours
Edge perception pipelines processing real-world video at scale
Production RAG, vector search, agentic workflows on LangChain & LangGraph
Pillar 01

Data Center & Infrastructure AI

Real-time telemetry, observability, and agentic operations for GPU clusters, telecommunications networks, and critical infrastructure. We unify heterogeneous sensor and operational data, validate system readiness, and build the reasoning layers that turn telemetry into autonomous remediation.

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[ Featured Product ] EdgeTelemetry

From fragmented telemetry to autonomous infrastructure.

EdgeTelemetry ingests heterogeneous telemetry from GPUs, hosts, cooling, and infrastructure systems, normalizes it into a unified schema, and validates system readiness. Operators move from weeks of manual onboarding and fragmented dashboards to minutes of automated validation — and a foundation for autonomous diagnosis and remediation.

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— Trusted by teams shipping AI in production —

DehazeLabs operates with US-based partnership leadership and an engineering bench across South Asia. This footprint gives us cost-advantaged delivery for North American mid-market and enterprise customers, and direct access to the Indian manufacturing ecosystem, the emerging Indian space sector, and the global capability centers operating data center and infrastructure functions out of Bengaluru and Hyderabad.

We embed engineers directly into customer teams. Implementations are designed to evolve as the underlying models do — built to last beyond the launch, with ongoing platform operation and SLA support.

More about how we work →

→ 01

Data engineering at scale

Production data platforms processing high-volume telemetry, video, and operational data.

redshift · s3 · snowflake · kafka · airflow · nifi · airbyte · dbt
→ 02

Production AI systems

RAG and vector search, agentic workflows, multimodal pipelines, real-time ML inference, model evaluation and monitoring.

langchain · langgraph · vector_db · cv · transformers · bedrock · sagemaker
→ 03

Enterprise delivery

Embedded engineering teams, US partnership leadership, South Asia delivery bench, ongoing platform operation and SLA support.

us_leadership · south_asia_bench · embedded_teams · sla_ops

→ Industries & use cases

Industry
Data Center Operators
GPU telemetry, rack onboarding, agentic ops
Industry
Telecommunications
Real-time SIEM, network telemetry, T-Mobile
Use Case
GPU Rack Onboarding
Weeks to hours with EdgeTelemetry
Use Case
Enterprise RAG Platform
Production RAG, LangGraph, 90%+ review reduction
Industry
Smart Cities
Edge ML, perception AI, Hayden AI
Industry
Logistics & Transportation
ML routing, dock CV, Cargomatic
Industry
Manufacturing & Industrial
IoT telemetry, predictive maintenance, CV inspection
Use Case
Agentic Operations
Claude-powered autonomous infrastructure ops
Use Case
Physical AI Data Flywheel
Robot logs → labeled ML dataset in 90 days
Use Case
Manufacturing Quality AI
Manual inspection → defect detection + review queue
Compare
vs Big 4 Consulting
Engineering-led vs advisory-led AI
Compare
vs In-House AI Team
When to hire vs when to engage
Compare
vs Offshore AI Staffing
Embedded delivery vs staff augmentation
[ FAQ ]

Common questions about working with DehazeLabs.

What makes DehazeLabs different from a typical AI consulting firm?
We build production systems, not strategy decks. Our engagements are engineering-led: we embed a team, own the architecture, and operate the platform until it's stable. The work is anchored in deployments like T-Mobile's real-time SIEM at telco scale, Hayden AI's smart-city perception platform, and SponsorUnited's multimodal AI platform built from scratch. We're also a product company — EdgeTelemetry is our in-house GPU rack onboarding product, so we eat our own cooking on the infrastructure AI side.
What industries do you work with?
Our primary focus is the physical economy: data centers and GPU infrastructure operators, telecommunications networks, robotics and autonomous systems, smart cities and municipal systems, logistics and transportation, and industrial/manufacturing environments. We also work with enterprise SaaS companies building multimodal AI platforms. The common thread is systems that generate high-volume operational data and need AI that works in real-time, not just in demos.
Where are your teams located?
US-based partnership leadership and technical leads manage client relationships and architecture decisions. Our engineering bench is distributed across South Asia — primarily Hyderabad and Bengaluru. This structure gives North American clients cost-advantaged delivery without sacrificing engineering quality or communication. Most enterprise engagements have a US-based technical lead as the primary point of contact.
What does a typical engagement look like?
Engagements are typically $300K–$4M over 3–24 months depending on scope. We embed engineers directly into your team — you're not buying advisory, you're buying execution. Most engagements run in phases: architecture and data infrastructure first, then AI and reasoning layers, then ongoing platform operation. We're also honest if the scope doesn't fit what we do: we'll tell you in the first conversation.
Do you work with Claude specifically, or are you model-agnostic?
We have a strong Claude practice — it's our default for agentic operations, long-context document processing, and tool use workloads. We're also model-agnostic where the use case warrants it: some workloads run better on smaller fine-tuned models, some require specific latency or cost profiles. We'll give you an honest recommendation based on your actual requirements, not a model allegiance.
How do I know if my project is a fit for DehazeLabs?
Good fit: you're a data center, telecom, robotics, smart city, logistics, or industrial operator trying to build production AI on top of real operational data. You have a real engineering problem, not just an exploration. You want a team that builds and operates, not presents. The best way to know is a 30-minute conversation — we'll be direct about whether we're the right fit and what it would take to deliver.

Ready to ship AI that works in production?

We work with data center operators, telecommunications networks, manufacturers, and platform companies bringing AI into their core operations. Tell us about your roadmap.