Ohmvia / Intelligence Platform
In early development
Building the intelligence layer for advanced electronics repair.
Complex electronics repair generates valuable evidence — measurements, observations, decisions and verified outcomes. Ohmvia is developing a platform to turn that fragmented information into structured diagnostic knowledge.
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Every repair leaves evidence. Too much of it gets lost.
A voltage reading on an instrument. A microscope photo on a phone. A decision remembered by one technician. When these details stay separate, the next difficult fault starts from scratch.
- Reproduce the diagnostic steps behind a decision.
- Find measurements and observations from earlier cases.
- Compare similar faults without losing board context.
- Train technicians with evidence, not just recollection.
- Understand which measurements helped isolate a fault.
- Learn from verified outcomes, including unsuccessful repairs.
From scattered evidence to connected knowledge.
The proposed platform connects repair workflows, technical evidence and diagnostic reasoning. These are development goals, not available software features.
Capture
Organize symptoms, board identity, observations, measurements and supporting evidence.
Connect
Link device and board context with diagnostic actions, interventions and outcomes.
Reason
Explore related-case retrieval and AI assistance grounded in recorded evidence.
Improve
Keep technician verification and documented outcomes at the centre of reusable repair knowledge.
A diagnostic cycle, led by the technician.
The technician chooses the next measurement and verifies the result. The proposed software supports this work; it does not control hardware autonomously.
Observe
Capture symptoms, board context and initial evidence.
Structure
Organize observations and measurements into a repair case.
Reason
Retrieve relevant knowledge and explore hypotheses as capabilities develop.
Measure
Gather further evidence with appropriate diagnostic tools.
Verify
Record interventions, test limits and the observed outcome.
Learn
Preserve validated case knowledge for future diagnostic work.
Evidence first. Traceability throughout.
A useful system must preserve context: what was observed, how it was measured, why an intervention was chosen and what was actually verified.
- Structured repair-case records
- Multimodal evidence: notes, microscope images and waveform captures
- Instrument data with units, conditions and board context
- Case retrieval and contextual knowledge
- Evidence-informed AI reasoning
- Human verification and documented test limits
- Traceable decisions and repair outcomes
A foundation to build on. A roadmap to test.
An internal repair-workflow prototype has been tested locally with synthetic records. It includes case records, notes, private attachments and workflow history. This is separate foundational work, not an available Repair Intelligence product or a deployed AI system.
Local prototype · synthetic records
Foundation
Internal repair-case records, notes, attachments and outcome history provide an initial software foundation. Dedicated diagnostic measurement records remain planned.
Planned capability
Intelligence
Related-case retrieval, evidence-based AI assistance and support for diagnostic hypotheses.
Planned capability
Connected diagnostics
Instrument integrations and richer measurement, waveform and image context.
Long-term research
Long-term research
Machine-readable diagnostic workflows that may eventually support advanced automation or robotics-assisted repair.
For people who work through difficult faults.
The intended audience extends beyond one repair bench. These are potential users, not a list of existing customers.
- Independent electronics repair laboratories
- Board-level technicians
- Refurbishment and service organizations
- Repair engineering teams
- Teams seeking reproducible diagnostics and reusable repair knowledge
Built around the realities of the repair bench.
A plausible explanation is not a verified repair. Physical measurements, technician judgment and testing against the reported fault matter. Ohmvia’s repair perspective informs software that aims to preserve those distinctions, including uncertainty and the limits of each test.
Explore the Repair LabInterested in the future of repair intelligence?
We welcome conversations with repair professionals, engineering teams and potential technical collaborators.
Start a conversationOpens your email app. You can also write to hello@ohmvia.com.