Your team.
Our engineers.
Shared ambition.
Forward deployed engineers who work inside your environment to get production AI live fast — and build lasting capability in your team. Not a remote vendor. A dedicated deployment pod embedded with your engineers, led from the US, with a bench across Hyderabad and Bengaluru.
The operating handoff
starts with the first build.
Discover
Agree on the workflow, source access, and acceptance criteria.
Build
Work with your team to connect data, models, and controls.
Operate
Observe real usage, handle exceptions, and improve reliability.
Transfer
Deliver documentation, runbooks, and clear ownership.
Why AI gets stuck in pilot mode.
Most operators have already run the pilots — knowledge search, operations, productivity. The demos worked. Few reached production at scale. The models were never the problem. Implementation is: integrations across systems that were never designed to talk to each other, security and compliance constraints no brief fully captures, and production failures in environments nobody fully controls.
Getting AI to hold up in the real world is the hard problem. Someone has to own it from the inside. That is the job of a forward deployed engineer.
Not the deliverable. The outcome.
The engineer who scopes it builds it, ships it, and operates it — and stays accountable until the system holds up in production. No handoff to a different team when it gets hard.
Systems that were never meant to talk.
ERP, CRM, telemetry, document stores, control systems — integrated on your live data, inside your security and compliance constraints, in environments we don't fully control. That's the work.
Technical and organizational ownership, in one seat.
Managing the relationships, aligning the stakeholders, and earning the operational trust that makes deployment possible — because integrations don't ship without it.
Each deployment makes the next one easier.
First use case to production, then the knowledge, the reusable integrations, and the runbooks go to your team — so the next deployment is faster, and increasingly yours to build.
| 01 · first_use_case | We pick the use case that matters and take it to production before anything scales. A direct 30-minute first call; a concrete technical plan against your real systems within 1–2 weeks. |
| 02 · solve_inside | Data access, governance, and performance issues get solved during implementation, embedded in your environment — the pod is there to work through them, not to file tickets about them. |
| 03 · production | Phased, milestone-driven delivery. A working system in your hands, running on your data — not a demo on ours. |
| 04 · operate | We run it while your team learns it. SLA-backed operation until it's stable. |
| 05 · expand | Reusable integrations and accumulated context make the next deployment faster. Expand across departments, properties, projects, or fleets. |
| 06 · transfer | Structured handoff — documented, with runbooks. Your team owns it and builds on it. |
Infrastructure, robotics, and enterprise platforms.
US-based partnership leadership accountable for delivery, embedded with the teams that run physical infrastructure — where the data is noisy, high-volume, and the tolerance for a system that doesn't hold up is zero.
| data_center_operators | GPU cluster and colocation operators — rack onboarding, unified telemetry, agentic operations. EdgeTelemetry |
| telecommunications | Real-time SIEM and network telemetry at carrier scale. T-Mobile. |
| robotics_autonomy | Robot fleet replay, failure triage, and physical-AI data flywheels. Robot Ops |
| smart_cities | Edge perception and automated enforcement platforms across US cities. Hayden AI. |
| logistics | ML routing and dock/container computer vision. Cargomatic. |
| manufacturing | Industrial IoT telemetry, predictive maintenance, quality inspection. |
| enterprise_platforms | Multimodal AI, production RAG, and agentic workflows at scale. SponsorUnited. |
| buyers | CTO · VP Engineering · Head of Infrastructure or Operations |
Owner-operators, manufacturers, and capability centers.
India is one of the most AI-aware markets in the world — and most operators have run pilots that never reached production. Our team is on the ground in Hyderabad and Bengaluru, accountable for getting AI into production in weeks, not quarters, and for transferring capability to your own team.
| owner_operators | Real estate developers, hospitality operators, family offices — Company Brain as a three-week pilot on the customer's own Anthropic account. GHR Infra · The Cascades; a Best Western-franchised property. |
| manufacturing | Quality AI and operational apps for factory networks. Maryadha. |
| financial_services | NBFCs and financial-services operators — Company Brain with compliance and audit posture. Adjacent capacity. |
| capability_centers | Global capability centers in Bengaluru and Hyderabad running data center, infrastructure, and data functions. |
| industrial_ecosystem | Direct relationships across Indian manufacturing and the emerging space sector. |
| buyers | Owner-operators & CEOs · CTO · COO |
EdgeTelemetry
Unified GPU, host, cooling, power, and network telemetry with automated rack readiness — and a Claude reasoning layer for autonomous diagnosis and remediation.
Company Brain
A Claude reasoning layer over fragmented ERP, CRM, document, planning, and HR systems — every answer sourced. Three weeks to a working pilot, on your own Anthropic account.
Robot Ops
Replay, incident detection, and AI-assisted failure triage for robot fleets — raw MCAP logs become a labeled ML dataset. Built on Foxglove.
Plus custom builds across Data Center & Infrastructure AI, Edge & Perception AI, and Multimodal Enterprise AI Platforms.
We deploy on your cloud — AWS, or on-prem edge where the data lives — and where you want it, on your own Anthropic account with customer-direct billing. Claude is the reasoning layer across every deployment; DehazeLabs is a member of the Anthropic Claude Partner Network, and 10+ of our engineers hold Claude Certified Architect: Foundations (CCAF). Nothing we build locks you into us.
DehazeLabs' team's expertise in building, deploying, and managing AI agents revolutionized our network optimization and elevated customer service efficiency.
→ In production with
T-Mobile · Hayden AI · SponsorUnited · Cargomatic · GHR Infra · Maryadha · a US robotics operator (under NDA) See our work →
Forward deployed engineering — common questions.
What is a forward deployed engineer?
How fast does a first use case reach production?
Is the deployment pod on site?
Do you build on our own Anthropic account and cloud?
How do US and India deployments differ?
How is this different from offshore staffing?
What does a forward deployed engagement cost?
Get your first use case into production.
Tell us what's stuck in pilot mode. In 30 minutes we'll be direct about whether we're the right pod — and exactly what it takes to ship.