Physical AI Infrastructure
Infrastructure forPhysical AIsystems.
Data, models and hardware integration for humanoid robots, companion robotics, industrial machines and smart devices.
Supported By
The problem
A chatbot inside a robot is not Physical AI.
Physical AI must perceive its environment, understand what is happening, make decisions and act in the real world. It must do this within the limits of its hardware and safety requirements. That takes four connected layers working as one.
- 01
Perception & real-world data
To understand the environment and learn from real-world experience.
- 02
Models built for the device
Domain-specific models optimized for the hardware and its requirements.
- 03
A cognitive decision layer
With memory, context, reasoning and goal-oriented planning.
- 04
Hardware integration & control
To translate decisions into physical actions within defined safety limits.
We bring all four together.
The stack
Six modules, from raw sensor data to action.
01 · Capture
Data
Human demonstrations and sensor recordings, structured into datasets for training, evaluation and improvement.
02 · Sense
Perception
Camera, audio and sensor signals processed into information about the environment that the system can use.
03 · Think
Cognition
Memory, context, domain knowledge, reasoning and goals that guide what the system should do next.
04 · Learn
Models
Small language and multimodal models, trained or fine-tuned for your domain and optimized for the target hardware.
05 · Act
Control
Decisions translated into speech, movement and actuator commands through the device's control systems.
06 · Own
Your AI
Compact, private intelligence inside the robot or on your servers, linked to the wider Recapi network.
Evaluation runs across every layer, measuring perception, model performance, decision quality and physical execution.
Use the complete stack, or engage us for the modules your product needs.
Use cases
What you can build with XOIDLABS.
You don't need to be a robotics company. You need a physical product that should sense, understand and act.
- 01
Companion robotics
Example: A companion robot that recognises a user's voice, remembers yesterday's conversation and picks it up in today's interaction.
- 02
Humanoid & service robots
Example: A reception robot that recognises a returning visitor, recalls their last visit and walks them to their meeting.
- 03
Industrial systems
Example: A robot arm that learns a packing task from operator demonstrations and stops when an item is placed wrong.
- 04
Smart devices
Example: A kitchen appliance that understands “make it less spicy”, remembers the household's taste and adjusts the next recipe.
- 05
Autonomous machines
Example: A warehouse cart that sees a blocked aisle, reroutes within its safety limits and learns which aisles crowd at peak hours.
Examples are illustrative. Shipped products are under Our work.
Why XOIDLABS
Most teams connect AI to hardware. We build the system around it.
One team, sensor to action
From data collection and model training to cognition, hardware integration and deployment. One engineering team across the entire system.
Built for your hardware
Models and intelligence systems configured for your device, compute capacity and operating environment.
Built on reusable intelligence
Recapi provides the foundation for domain understanding, memory, context, reasoning and goal-oriented execution. Your project starts from reusable infrastructure.
Forward-deployed engineering
Our engineers work alongside your team, from understanding the product and building its intelligence to deployment and ongoing improvement.
Our technology
Recapi: the cognitive layer behind every intelligent product we build.
Recapi learns your product's domain, goals, workflows and operating rules. It configures the right models, memory, context and reasoning around them, giving every product a cognitive foundation without starting from zero.
Recapi · Cognitive infrastructure
Memory
Retains relevant information and experience.
Context
Understands the current situation and history.
Reasoning
Interprets information and evaluates options.
Goal planning
Plans actions around desired outcomes.
Guardrails
Enforces rules, permissions and constraints.
Learning
Improves through feedback and evaluation.
30-day MVP
A working Physical AI prototype in 30 days.
You bring the product idea. We bring the AI and engineering team.
- 3 technical specialists
- 1 project lead
- Defined 30-day scope
- A working prototype
The 30-day engagement is designed around a defined MVP scope. Hardware availability, data access, integration dependencies and acceptance criteria are agreed before work begins. Production readiness may require further engineering.
Week 1
Discovery & architecture
Understand your product, hardware, operating environment and data. Define the system architecture and MVP scope.
Week 2
Intelligence & integration
Configure the models, Recapi cognitive layer and required hardware interfaces.
Week 3
Prototype & validation
Build the core experience, integrate the components and test the agreed use cases.
Week 4
Delivery & roadmap
Deliver the agreed working prototype, technical documentation, evaluation findings and roadmap towards production.
If it works for both sides, we continue as your Physical AI engineering team.
What you walk away with
Depth varies by project. Deliverables are agreed before we start.
- A working prototype against agreed requirements
- Configured AI models and cognitive infrastructure
- Hardware and software integration within the agreed scope
- Initial performance and evaluation results
- Technical architecture and documentation
- A roadmap for production development
The XOIDLABS engagement model
- 01 · You start here
Explore
30-day MVP to establish technical feasibility and build the first working prototype.
- 02
Build
Model training, data infrastructure, hardware integration and system development.
- 03
Deploy
Prepare the system for production, edge or on-premise deployment.
- 04
Evolve
Your extended engineering team for model updates, evaluation and product improvements.
Our work
Intelligence built for real-world applications.
Companion robots that listen, talk and remember
We bring cognitive intelligence into physical companion devices through conversational AI, memory, contextual interactions and secure, age-appropriate behaviour.
- Physical product integration
- Conversational intelligence
- Domain-specific model configuration
- Memory and contextual interactions
- Gamified, interactive experiences
- Secure, age-appropriate AI behaviour
- Perception
- Cognition
- Models
- Control
Manufacturing
Manufacturing intelligence for sales teams
We build domain-specific language models that evaluate sales interactions, score performance and support personalised follow-ups.
Pharmaceuticals
Pharmaceutical intelligence with controlled responses
We build domain-trained AI for pharmaceutical medicine knowledge, designed to provide contextual information and consistent, rule-governed responses.
Cinema
Domain-specific AI for film production
We build specialised cinema intelligence that supports pre-production planning, scheduling, budgeting and production coordination.
Proprietary model development
Hybrid video intelligence
XOIDLABS
Our own video intelligence model, developed for multimodal and domain-specific applications.
Let's build together
Have a physical product in mind?
Let's make it intelligent.
You bring the product idea. We bring the intelligence, data and engineering to make it work.