Define the task
Agree on the part, defect, throughput target, and intervention rules.
A task specification and baseline.DEHAZE LABS / PHYSICAL AI & ROBOTICS ENGINEERINGFROM MODELS TO OPERATIONS
Engineering intelligence into the physical world.
From foundation models to real-world robotics.
We integrate existing robots, computer vision, foundation models, simulation, and production software to develop intelligent automation systems.
PHYSICAL AI / FROM ENVIRONMENT TO OPERATION
Intelligence, in the context of the real world.
Simulation. Workcell. Production.
Three independently sourced scenes illustrate the engineering context. Simulation is shown first, followed by real factory footage; these are not Dehaze deployments or one sim-to-real experiment.
See the operation.
Engineer the right intervention.
THE FLOOR
Layout, movement, and the people around the task.
THE WORKCELL
Robots, perception, tooling, and adapted intelligence.
THE OPERATION
Systems integration, acceptance, and continuous improvement.
01 / EXISTING HARDWARE02 / ADAPTED INTELLIGENCE03 / OPERATIONAL RELIABILITYEXPLORE OUR ENGINEERING ↓
One application.
The engineering to make it work.
Physical AI & Robotics leads our work. Infrastructure and applied AI engineering connect it to the rest of your operation.
↗From robot hardware
to intelligent automation.
Robotics integration, task adaptation, simulation, perception, and operational reliability—with qualified partners for physical deployment.
↗The foundation for
production intelligence.
GPU infrastructure, data platforms, distributed systems, telemetry, observability, and production MLOps.
VisionLanguageSignals
Models & tools
Operational applications ↗
Intelligence built
around the work.
Computer vision, multimodal applications, agents, edge inference, model adaptation, and custom enterprise software.
02 / APPLICATIONSSee the whole floor.
See the whole floor.
Start with one useful task.
A robot works inside an operation: production lines, material flow, people, and site systems. Simulation helps explore that context. Physical trials show what holds up on the floor.
01 / MODEL THE ENVIRONMENT
Understand the operating context.
IndustryEQA / Research simulation
An existing simulated warehouse illustrates how layout and spatial context can be represented before physical testing.
02 / REAL PRODUCTION
Build for the whole operation.
Engin Altundağ / Industrial footage
A real automotive production cell brings robots, tooling, and material handling into view. Illustrative footage, separate from the simulation.
Two independent industry examples: a warehouse simulation and a real factory demonstration. Neither is a DehazeLabs deployment.
WHERE DEHAZE FITS
Your floor. Your task. A practical path to automation.
We help you select a useful starting point, connect existing hardware and AI, evaluate the application, and support deployment with qualified partners.
Explore our robotics engineering ↗Follow an inspection-and-handling pilot, step by step
ILLUSTRATIVE WORKCELL / INSPECTION & HANDLINGHardware, intelligence, and exception handling
Present partInspect with visionRoute with robotReview exceptions
Model the workcell
Represent the robot, camera, tooling, and working envelope. Check the assumptions that need physical testing.
A workcell model and interface plan.Adapt the intelligence
Connect perception and task software. Evaluate established models and demonstration data where they fit the workflow.
A task adaptation and evaluation plan.Validate the application
Test defect detection, handling, exceptions, and the limits of the simulated environment.
Results against agreed acceptance criteria.Commission with partners
Integrate site systems and support a bounded pilot with qualified hardware and deployment partners.
An agreed commissioning and handoff plan.Review and improve
Record outcomes and interventions. Investigate failures and prioritize the next engineering change.
An evidence record and improvement backlog.Illustrative application path. Hardware suitability, performance, site requirements, and partner responsibilities are evaluated for each engagement.
Explore the industries and applications we work in
Deep in the industries
that keep the world running.
Data centers ↗Telecommunications ↗Smart cities ↗Logistics ↗Manufacturing ↗Retail & consumer brands ↗Healthcare & life sciences ↗Financial services ↗
One connected approach
01
Sense.
Connect signals from the physical world.
02
Understand.
Turn operational data into context.
03
Act.
Build reliable decisions into everyday operations.
Explore robotics industry references
Explore the wider robotics ecosystem through these publisher films. These are industry references, separate from DehazeLabs work.
03 / EVIDENCEEngineering depth,
Engineering depth,
already at work.
Customer work across perception, data, infrastructure, and applied AI supports our robotics practice. Each case retains its own scope and delivery stage.
Selected customer work
Inside Robot Ops: inspect a saved simulation incident
BUILT BY DEHAZE / ROBOT OPSA robot incident.
Reference run · simulated robotA robot incident.
Evidence an engineer can inspect.
Exported scan comparisons · approximately 10× playback
Simulation telemetry, not camera footage or a product UI recording.
SAVED INCIDENT / INC-1778618747
- Saved incident window
- 88.99–191.69 seconds
- Peak pose uncertainty
- 7.76× baseline
What the output tells us
Localization needs investigation.
The cached triage report flags a localization-degradation hypothesis. An engineer still needs to compare the signals, fill the evidence gaps, and validate the cause.
See the review boundary
The model reported medium confidence. That is not a performance score. The saved artifacts do not establish a confirmed cause, human approval, or an improvement in robot performance.
The source report asks for additional scan/map, odometry, and costmap context. The plots shown here are separate exported reference artifacts.
Explore our products and their distinct operational roles
DehazeLabs productsFocused products.
Focused products.
Distinct operational needs.
Explore the tools we build for infrastructure, business knowledge, and robot operations.
See the work take shapeWhat does AI look like
What does AI look like
inside your operation?
01 / Business intelligenceA question becomes
A rack becomes
An incident becomes
A question becomes
a sourced answer.
Explore how Company Brain connects a business question to the records behind it.
02 / InfrastructureA rack becomes
ready to operate.
Follow a readiness review across compute, network, power, and cooling.
03 / Physical AIAn incident becomes
a learning signal.
See how raw robot logs become evidence a reviewer can turn into a curated dataset.
04 / DELIVERYA defined task.
A defined task.
A deliberate path to operation.
Start with an automation opportunity. We assess feasibility, integrate commercially available hardware and tooling, adapt AI to the task, and validate performance with qualified deployment partners.
AssessIntegrateAdaptValidateDeployImprove
DEHAZE LABS
AI adaptation, application software, evaluation, and operational intelligence.
QUALIFIED PARTNERS
Hardware integration, installation, site engineering, and commissioning within the agreed scope.
YOUR TEAM
Workflow knowledge, site access, operational ownership, and acceptance decisions.
A team. Not a handoff.We build it
We build it
with you.
Then make it yours.
Our engineers embed directly into your team. From architecture and data infrastructure to deployment and ongoing operation, we own the work with you.
US-based partnership leadership. Engineering depth across Hyderabad and Bengaluru.
How we work ↗Explore the architecture and engineering behind the application
The DehazeLabs AI operating layerConnect the intelligence.
Connect the intelligence.
Transform the operation.
Connect knowledge, infrastructure, and workflows around a defined application. Our engineering teams connect the application. Robot Ops, EdgeTelemetry, and Company Brain provide purpose-built tools for robot operations, infrastructure, and business knowledge.
Complementary products and engineering capabilities
Explore the AI operating layer ↗01KnowCompany Brain
02ObserveEdgeTelemetry
03LearnRobot Ops
04ActAgents & workflows
Source context · Human oversight · Measurable outcomes
A complete path to productionThe architecture.
The architecture.
The team. The next move.
Engineering with the leading AI and cloud platforms.
Anthropic Claude Partner Network ↗AWS Partner Network
05 / THE FIRST ENGAGEMENTMake the first decision
Make the first decision
a useful one.
An automation assessment turns an open-ended idea into a task, a set of feasibility questions, and a practical pilot decision.
- 01
A defined task
The workflow, operating constraints, and baseline.
- 02
A hardware & interface shortlist
Existing platforms, tooling, sensors, and integration needs.
- 03
A feasibility and risk review
The technical unknowns and evidence needed to resolve them.
- 04
Pilot acceptance criteria
What to measure and how to make the go/no-go decision.
Scope is agreed before work begins. An assessment can recommend a pilot, further investigation, or a decision not to automate.