Digital twin pre-deployment
Scan the facility, recreate the task environment, and validate policies before hardware blocks production flow.
Physical AI deployment company
Making robots work in production.
Advaitic deploys model-powered robotics into messy real-world workflows where foundation models almost work, but still fail too often for production.
Deployment surface
High-variance manipulation workflows
Product wedge
Digital twin, edge runtime, observability, learning loop
Starting point
Book logistics and electronics test benches
We are the independent deployment layer between fast-moving robot foundation models and the operators who need reliable physical work done on site.
§ 01 Product
A deployment stack for physical AI systems.
Customers pay for outcomes: robots that can handle useful work under real site variation. We stitch together best-in-class models, simulation, hardware, and monitoring instead of asking customers to bet on one vendor.
Model-agnostic policy serving
Evaluate and route across foundation models, classical controls, and task-specific policies without customer lock-in.
On-device inference
Run low-latency perception and control on edge compute where cloud-only inference is too slow or sensitive.
Operations observability
Track failures, interventions, recovery paths, and production metrics so reliability improves after deployment.
§ 02 Why now
Reliability at scale is the bottleneck.
Robot foundation models, cameras, manipulators, and simulation are improving quickly. The remaining gap is production reliability across every customer, workflow, object variation, edge case, and recovery path.
Task chains
95%
per-step success can collapse across a chained physical task.
Operator trust
99%+
per-step reliability is what operators need before automation becomes trusted infrastructure.
Runtime
24/7
production cells need monitoring, rollback, human review, and continuous post-deployment learning.
§ 03 Deployment playbook
From messy workflow to operating cell.
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Map
Find the repeatable pain
Document materials, throughput targets, exception rates, safety boundaries, and human handoffs.
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Simulate
Build the site twin
Use scans, procedural variants, and task data to test policies before they touch the real workflow.
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Deploy
Run a bounded pilot
Integrate manipulators, sensors, edge compute, classical controls, and learned policies on site.
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Improve
Compound failure data
Capture interventions and outcomes, then improve models and procedures with human-approved updates.
§ 04 Use cases
Starting where classical automation is too rigid.
We start with high-variance workflows where the environment shifts, recovery paths matter, and useful automation still depends on adapting to the messiness of the real world.
Chaotic operating environments
Workcells with changing objects, layouts, lighting, and handoffs where fixed programs break under day-to-day variation.
High-precision physical tasks
Manipulation and inspection steps where small errors compound quickly and reliability matters more than a polished demo.
Lab and industrial automation
Structured but evolving workflows that still require flexible perception, recovery logic, and human-aware deployment in production.
§ 05 Work with us
Bring us the workflow your current automation cannot handle.
We are looking for design partners with real operational pain, measurable throughput goals, and teams ready to pilot physical AI in the field.
hello@advaitic.ai