EU / euROBIN Pilot-Ready

Deploy site-specific robot skills in weeks.

We build a trainable digital twin from limited site inputs, train a robot skill inside the twin, and deliver a deployable Skill Pack with benchmarking, monitoring, and rollback.

Privacy-first
ROS 2 Native
Verifiable
Training Efficiency
+95%
Safety Score
Verified

Why AI Deployments Fail in the Physical World

The data + transfer bottleneck.

Unique Environments

Every site differs. Manual tuning and on-site data collection don’t scale.

Data Scarcity & Edge Cases

Rare failures and hazardous scenarios are expensive or unsafe to capture in the real world.

Sim-to-Real Gap

Policies trained in simulation break without domain randomization, benchmarking, and safety constraints.

How It Works

From site inputs to a deployable Skill Pack

1
Twin Generation

Twin Builder

Turn CAD/BIM, scans, and short videos into a trainable digital twin.

Output: Trainable Digital Twin
2
Specification

Skill Studio

Define the task, constraints, and acceptance KPIs.

Output: Task Spec + KPIs
3
Training

Training Engine

Generate synthetic scenarios and train in parallel (IL/RL/Hybrid).

Output: Synthetic Experience
4
Validation

Benchmark & Safety

Run benchmark suites, validate the safety envelope, generate evidence logs.

Output: Evidence Logs
5
Deployment

Deployment Pack

Integrate via ROS 2/API templates. Roll out with monitoring and rollback.

Output: Deployable Skill Pack

Typical pilot: ~4 weeks to first on-robot validation (scope-dependent).

Pilot Program

What you get in a pilot

Skill Pack

Trained policy/model for your specific robot, task, and environment.

  • Trained Policy
  • Model Weights
  • Inference Container

Benchmark Protocol

Comprehensive safety and performance validation.

  • Evidence Logs
  • Safety Envelope
  • Robustness Report

Deployment Pack

Everything needed for safe integration.

  • Integration Templates
  • Monitoring Dashboard
  • Rollback Plan

01. Pilot Timeline

Discovery & Needs

Week 1-2

We analyze your CAD/BIM data and define the robot task.

Digital Twin & Training

Week 3-6

Training the policy in our physics-accurate simulation environment.

Integration & Scaling

Month 2-3

Deploying the Skill Pack to physical robots and validating performance.

02. What We Need

1

CAD / BIM / Point Clouds

2

Robot URDF / Mesh

3

Defined Use Case

4

Safety Zones / Constraints

Use Cases

Industry Applications

Industrial Manipulation

Industrial Manipulation

Warehouse automation, material handling, and fulfilment.

Inspection & Maintenance

Inspection & Maintenance

Utilities, infrastructure, and facilities monitoring.

Disaster Response

Disaster Response

Civil security and hazardous environment operations.

Proven Results

Indicative Metrics

-55%
Reduction in policy training duration relative to traditional Sim2Real methods
Training Time
Indicative metric (pilot)
95%
Average precision on test set with novel distractors
Detection Accuracy
Indicative metric (pilot)
Up to -80%
Comparison against manual data collection and labeling costs
Data Cost
vs. real-world collection
Up to -90%
Accelerated deployment timeline from months to weeks
Time-to-Market
faster iteration cycles
*Indicative results based on prior pilot deployments. Outcomes vary by task, robot, and environment.

UAV Obstacle DetectionCASE STUDY

Challenge

Detecting thin wires in complex environments.

Approach

Synthetic training with domain randomization + targeted edge-case generation.

Outcome

Up to 95% detection accuracy with up to 55% less training time (pilot scope).

Safety & Trust

Enterprise-Grade Reliability

Rigorous Benchmarking

Task Performance
Success rate across standard test suite
High
Robustness
Performance stability under perturbation
Verified
Safety Envelope
Adherence to defined operational constraints
Compliant

* Example report view — final values depend on task and environment.

Data Governance

🛡️

Safety-by-Design

Constraint-aware training, runtime monitoring & rollback.

🔒

Data Governance

Data minimisation, access controls, retention by agreement (GDPR-aligned).

🇪🇺

EU Supported

euROBIN & Seeds of Bravery participation.

FAQ

About Syntetiq

We build a Digital Twin-to-Real Skill Factory that turns site inputs into deployable robot skills — with measurable benchmarks and safe rollout.

Our Mission

We enable organisations to deploy site-specific robot skills faster and with less operational risk. Our platform converts limited site inputs (CAD/BIM, scans, short videos, robot specs, safety constraints) into a trainable digital twin, trains the skill in simulation, and delivers a production-ready Skill Pack.

What makes our approach different is that we do not only deliver a model. Each pilot includes a benchmark protocol and evidence log (performance, robustness, safety envelope) plus a deployment pack with monitoring and rollback, so your team can run clear acceptance testing and scale safely.

Key principles

  • Privacy-first data handling and data minimisation
  • Verifiable benchmarking and repeatable acceptance criteria
  • Integration-ready delivery (ROS 2 / API patterns) and rollback planning

With years of experience in developing web applications, mobile solutions, and automation systems, we understand that every solution must be unique and tailored to your business needs.

Ready to deploy site-specific robot skills?

Start your pilot application today. We'll guide you through the process of creating a digital twin and training your robot.