Workforce shortages and skill transfer
We identify person-dependent decisions and convert them into records and support procedures that experts and less-experienced staff can review together.
MANUFACTURING AI / SKILL SUCCESSION
Carry manufacturing knowledge
Carry manufacturing knowledge
into the next generation with field AI.
Capture expert judgment, equipment conditions and tacit knowledge through cameras, sensors and Edge AI. The goal is not to replace people, but to create a system that helps engineers continue to decide, teach and improve.
THE OPERATIONAL GAP
01 / PEOPLE
02 / EQUIPMENT
03 / DATA
Preserve not only the video, but the reasoning behind each decision.
As Japan faces an aging population, shortages of engineers and a lack of successors, manufacturers of every size need a way to pass field knowledge forward. Video alone does not preserve what an expert noticed, which change was judged abnormal or why the next action was selected. We align equipment data, video, sound and work records on a common timeline so the next generation can understand and reuse the context behind each decision.
Missed changes in equipment condition
We use the inputs required by the problem, including cameras, sound, vibration and environmental sensors, to identify signs of abnormal conditions.
Fragmented field data
We verify connection conditions for existing equipment and business systems, then share only the information that is needed.
WHAT WE IMPLEMENT
VISION
CONDITION
KNOWLEDGE
SAFETY
EDGE AI
INTEGRATION
Connect only the layers the site needs, from cameras to data infrastructure.
We do not assume a large-scale replacement from the outset. We determine whether the right starting point is input design, Edge AI, integration with existing equipment, or records and learning.
Quality and visual inspection
We design imaging around lighting conditions and the target object, then connect it to vision analytics whose decision evidence can be reviewed.
Equipment monitoring and predictive maintenance
We combine video and sensor data to surface potential anomalies and support maintenance staff review.
Transfer of skills and judgment
We connect work records with expert explanations and structure the knowledge for training, review and standardization.
Safety confirmation and work support
With human judgment retained as the final authority, we design alerts, records and interfaces that reduce missed checks and oversights.
Closed-network and on-premises processing
Depending on confidentiality, connectivity and latency requirements, we process data on site and share only what is necessary.
Existing equipment and business-system integration
We verify the integration scope for existing PCs, cameras, sensors, APIs and databases case by case.
POC TO OPERATION
Break down the challenge, validate on a small scale and build for real operations.
The purpose of a PoC is not simply to demonstrate AI. It is to assemble the evidence needed for a deployment decision. We define evaluation criteria, users, data handling and connections to existing equipment at the outset.
- 01Map the operational challenge and decision process
- 02Select the required cameras, sensors and data
- 03Validate accuracy and operating conditions in a PoC
- 04Integrate with existing equipment and workflows
- 05Improve with operational data and expand across sites
PUBLIC IMPLEMENTATION STATUS
We distinguish what is currently available, under integration validation and part of the future roadmap.
We do not make blanket claims about project results, performance, time frames or costs based only on public information. Recommendations follow review of the target, equipment, data and operating conditions.
| Currently available | Contract AI development, vision analytics, camera and input evaluation, Edge AI architecture assessment and PoC design. |
|---|---|
| Under integration validation | Multi-sensor integration, coordination between field Edge AI and data infrastructure, and operating design that includes continuous learning. |
| Future roadmap | Distributed AI infrastructure that securely circulates knowledge and models across sites and accumulates improvements across industries. |
EVIDENCE & GOVERNANCE
DATA
IP
SOURCES
When field knowledge is involved, evidence and safeguards come first.
Before a PoC, we define the purpose of collection, retention scope, access rights, use for training and IP ownership. Pre-filing inventions, customer-specific specifications and non-public data are never placed on public pages.
Use only the data that is needed
Information that can be processed in the field is handled at the edge, with the sharing scope designed around the objective.
Treat IP and contracts as one design issue
We address public IP, licensing, joint development and ownership of deliverables at the same stage as the business design.
Ground social challenges in public evidence
IPA skill-transfer AI case study, JILPT survey on AI in manufacturing and other public primary sources inform the page.
START FROM THE FIELD
Tell us which decisions must be preserved and which process should improve.
Even before a complete dataset exists, we can define a PoC entry point from the field challenge, users, existing equipment and confidentiality requirements.