Data and AI for electronics manufacturing
Electronics generates enormous volumes of process, test and field data. The difficulty is rarely collection — it is joining process history to eventual outcomes across systems built at different times for different purposes.
Clients in this sector: Samsung
What usually gets in the way
- Manufacturing execution, test and quality systems with no shared unit-level key
- High-volume time-series data expensive to retain at full fidelity
- Yield and failure signals visible only when process history is joined to field returns
- Supply chain data spanning many suppliers with varying quality
- Engineering teams needing answers in hours, not after a quarterly analysis
What we build
- Unit-level traceability across process, test and field data
- Yield analytics and root-cause investigation tooling
- Predictive maintenance and equipment health monitoring
- Test-data reduction that keeps the signal and drops the storage cost
- Self-serve analytics so process engineers stop queuing for reports
How we handle the constraints
Process and yield data is commercially sensitive and often subject to supplier confidentiality. We work inside your environment and scope access to the specific lines and periods an engagement needs.
Ways to work with us
Fixed-scope build
Defined deliverable · weeks to months
The problem is well understood and you want it solved without opening a hiring req.
Embedded senior team
Ongoing · alongside your people
You have a capable team that is short on senior capacity, not on direction.
Advisory and roadmap
Assessment · sequenced plan
There are several plausible directions and the cost of picking the wrong one is high.
Build and run
Delivery · then ongoing operation
The system matters but does not justify hiring a permanent team to maintain it.
Common questions
Can you work with our existing MES and test infrastructure?
Yes. These systems are rarely worth replacing, and the value usually comes from joining them rather than consolidating them.
How do you handle the data volumes involved?
By deciding retention and aggregation deliberately — full fidelity where the analysis needs it, reduced representations elsewhere. Keeping everything at full resolution is usually the expensive wrong answer.
Working on something in electronics?
Describe the problem in a couple of sentences. If we're not the right people for it, we'll tell you who is.