From problem definition to development and operational improvement. Consulting connects two development paths: AI agents and physical AI.
Management transformation / Organizational transformation / New business
We plan development around each company’s objectives, covering challenge analysis, proposed initiatives, technology selection, proof of concept (PoC), team formation, and requirements definition.
Development / In-house development support / FDE
We connect workflows, knowledge, and applications through AI agents tailored to each company’s challenges. Beyond development, we work with your team to create the tools and processes that enable non-engineers to build in-house, or join your organization as a development team lead to build together.
Development / Data preparation / Simulation
We develop software, build and deploy AI, prepare data, and run simulations to connect AI with equipment, sensors, and robots. Alongside initiatives aimed at delivering immediate results in the field, we offer co-creation through joint research and development for the future.
DEVELOPMENT PROCESS
Make each next step informed by evidence. Scope and deliverables are agreed for each project.
Review workflows, team skills, and development environments to define in-house development goals.
Select tools and AI that non-engineers can use, and validate feasibility with a small prototype.
Co-develop business applications and AI agents while sharing design and implementation skills.
Validate quality, permissions, and approvals, then prepare documentation and training for internal operation.
Support the team’s ongoing changes and improvements through reviews and technical guidance.
Understand operations, existing systems, and team challenges to agree on shared priorities.
Align technology, requirements, and responsibilities in a practical validation and development plan.
Join the company’s team as a development lead to drive AI and application design and implementation.
Validate quality and usability in real workflows, integrate with existing systems, and deploy.
Improve from operational feedback and share design decisions and development practices with the team.
Review devices, sensors, connectivity, latency, and operating conditions to define AI’s role.
Collect and prepare field data, compare models and runtimes, and evaluate accuracy and processing load.
Build or tune models for the task and develop on-device inference and business-system integration.
Test accuracy, latency, and stability on site, and define exception handling and operating procedures.
Continuously improve models, settings, and applications using operational feedback and new data.
Review the robot, tasks, and surroundings to define motions and evaluation criteria.
Recreate robots and workspaces in simulation with sensor and physics configurations.
Validate cognition and motion, prepare training and evaluation data, and tune AI for the robot.
Review simulation findings and, where needed, assess differences through hardware tests under defined safety conditions.
Refine environments, data, models, and motion based on evaluation findings to guide further development and research.
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