Critical evidence lives everywhere.
- Inconsistent capture
- Missing measurement context
- Repeated review questions
- Manual quote translation
Engineer-supervised DCIA intelligence
Evidence in.
Engineering decision out.
ARIA starts with the equipment tag, guides mobile photo and measurement capture, and records what the engineer sees—then turns confirmed evidence into a traceable assessment report for authorized engineer review and estimating.
- Evidence-linked output
- Human approval required
- Built to scale across shops
INSPECTION / P-2048HOUSTON SHOP · BAY 04
OUTSIDE Ø214.98 mmRUNOUT0.05 mmEVIDENCE12 / 12COMPONENTImpeller
DRAFTErosion at vane tips
STATUSReview required
The assessment gap
DCIA knowledge is valuable.
The process shouldn’t be fragile.
One defensible inspection record.
- Guided required evidence
- Calibration-aware measurements
- Cited AI-assisted draft
- Approved downstream handoff
One closed loop
From teardown bench
to estimating.
Every output stays connected to the evidence, criteria, version, and person responsible for the decision.
- 01CAPTURETag · photo · audio · dimension
Identify before you inspect
Capture the equipment tag and model, confirm detected pump parts, then collect required views, measurements, and time-coded narration—even when the connection is unreliable.
- 02ANALYZEGrounded · versioned · abstains
Draft from confirmed evidence
Structure observations, severity, disposition, confidence, and follow-up with direct citations to photographs, measurements, and approved transcript timestamps.
- 03REVIEWHuman authority · immutable history
Keep engineers in control
Compare evidence and criteria side by side. Correct, request recapture, place on hold, or approve with full attribution.
- 04REPORTAssessment · photo appendix · Epicor-ready
Move approved work forward
Publish a versioned assessment with component findings, recommended work, approved field-note summaries, and a numbered photo appendix—then reconcile the estimating handoff.
FINDING 04 / IMPELLER92% evidence complete
AB
A decision, not a black box
Every statement earns its place.
When evidence is missing, contradictory, low-quality, or outside validated coverage, ARIA requests review instead of forcing an answer.
DRAFT DISPOSITIONRepair · reviewer confirmation required
Multi-shop by design
Standardize the system.
Respect the shop.
One organization can govern many shops without flattening local expertise. Configuration inherits deliberately, data stays scoped, and rollout happens by measured cohort.
Policy floor + portfolio view
Identity, security, data-use controls, entitlements, and privacy-safe aggregate metrics.
Explicit shop membership Project-scoped evidence Versioned local rules Fair-use compute quotas
A precise product boundary
Built for field-to-bench DCIA.
Condition-monitoring platforms help teams understand operating assets. ARIA’s proposed focus begins with confirmed equipment identity and follows disassembly, cleaning, inspection evidence, engineer narration, assessment approval, and estimating handoff.
Not generic visual AI
Tag-first identity, technician-confirmed part detection, component capture plans, measurements, audio timestamps, and provenance define the record.
Not autonomous assessment
OCR, object detection, transcripts, and assessment output remain candidates or drafts. AI output is always a draft; named, authorized reviewers own the final technical assessment and disposition.
Not another ERP
ARIA produces an approved assessment package and reconciles the handoff to the system already creating and releasing the quote.
Trust architecture
Confidential evidence.
Controlled intelligence.
Designed around tenant isolation, least privilege, immutable provenance, and a default policy of no shared-model training on customer evidence.
Discuss your requirements ↗Shop-scoped access
Organization roles never imply unrestricted access to raw shop evidence.
Traceable provenance
Evidence hashes, rule, taxonomy, prompt, and model versions remain tied to the draft.
Human attestation
Approvals record identity, authority, timestamp, version, disposition, and reason.
Safe degradation
Capture and manual findings continue if AI or ERP integrations are unavailable.
Standardize before scale
Plan a measured
one-shop pilot.
Begin with a 4–6 week baseline and approved standard work. Then validate identity, controlled photography, voice capture, calibrated measurements, completeness, and human approval across 12–20 real jobs—before advanced scanners or defect AI.
8–12 weeksControlled pilotFirst 5 jobsShadow mode0 autonomyEngineer approval required