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Technologies émergentes
Automatisation avancée
Une automatisation qui combine robotique, perception artificielle, agents d’IA et orchestration pour prendre en charge des tâches que les scripts à base de règles et la RPA classique ne savent pas gérer de manière fiable.
Vue d’ensemble
Rules-based automation handles predictable, structured work well. It struggles with variation: unstructured inputs, physical environments that change, exceptions that call for judgment. Machine perception, AI agents and collaborative robotics extend automation into those areas, but they bring different failure modes that have to be engineered for.
We design advanced automation with human oversight built in. That means clear boundaries on what the system may decide, confidence thresholds that route uncertain cases to people, and logs that explain every action. The goal is dependable operation, not autonomy for its own sake.
Applications métier
- 01
Intelligent process orchestration
Workflows that combine APIs, document understanding and AI agents to handle cases end to end, escalating to people at defined decision points and recording every step for later audit.
- 02
Vision-guided physical automation
Inspection, picking and sorting tasks in which cameras and machine learning models guide robots or actuators, with fallback paths for when lighting, orientation or object variety exceeds the model's competence.
- 03
Mobile robot integration
Integration of autonomous mobile robots and collaborative arms with warehouse, manufacturing execution and maintenance systems through standards such as VDA 5050, so fleets act on real operational data.
- 04
Exception handling at scale
Systems that triage the long tail of exceptions left by existing automation, such as mismatched records, unusual requests and ambiguous documents, proposing resolutions for human approval rather than acting blindly.
Approche d’ingénierie
- 01
Work analysis
We study the work as it is actually performed, measuring variation and exception rates to determine which parts suit automation and which should remain with people.
- 02
Autonomy boundaries
For each task, we define what the system may do alone, what requires approval and what it must never do, then encode those limits in the architecture rather than in policy alone.
- 03
Incremental build
Automation is introduced in stages, often starting in shadow mode where the system recommends and people decide, building evidence before any autonomy is extended.
- 04
Safety and reliability engineering
Physical systems follow applicable safety standards such as ISO 10218 and ISO/TS 15066, while software agents receive guardrails, evaluation suites and rollback paths for every action they take.
- 05
Monitoring and improvement
Dashboards track throughput, exception rates, overrides and drift, and feedback from human reviewers is used to retrain models and refine rules over time.
Technologies et plateformes
Orchestration and agents
- Temporal
- Apache Airflow
- Camunda
- LangGraph
- n8n
Perception
- OpenCV
- PyTorch
- ONNX Runtime
- NVIDIA Jetson
- NVIDIA DeepStream
Robotics
- ROS 2
- MoveIt
- NVIDIA Isaac ROS
- VDA 5050
- OPC UA
Enterprise integration
- REST and GraphQL APIs
- MQTT
- Apache Kafka
- UiPath
- Microsoft Power Automate
Les noms de technologies sont des marques de leurs propriétaires respectifs et n’impliquent aucun partenariat ni aucune approbation.
Ce que vous obtenez
- Work analysis and automation opportunity assessment
- Autonomy boundary and human-oversight design
- Automation workflows, models or robotic integrations
- Shadow-mode evaluation results
- Safety and guardrail documentation
- Operational dashboards and a continuous improvement process
Les questions des dirigeants
How is this different from RPA?
Traditional RPA replays fixed steps through user interfaces and tends to break when inputs vary. Advanced automation combines APIs, machine perception and AI reasoning to handle variation, with explicit confidence checks. Existing RPA often remains useful, and we typically integrate with it rather than replace it.
How do you keep people in control?
Through design rather than policy alone: defined autonomy boundaries, approval steps for consequential actions, confidence thresholds that escalate uncertain cases, complete action logs, and the ability to pause or roll back. Autonomy is extended only when evidence from shadow operation supports it.
What happens when the automation makes a mistake?
It will, occasionally, just as people do. We design for detection and recovery: validation checks after each action, reversible operations where possible, alerting on anomalies and clear ownership for remediation. Error rates are measured openly, so decisions about scope rest on evidence.
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