By Gwylym Owen — 20–28 min read
Picture a mechanic on an automotive assembly line, eyes glued to a monitor as cameras scan for scratches on a freshly painted hood. The conveyor can’t stop—latency must be razor-sharp, security ironclad, and quality provable with hard data. That’s the edge AI challenge, and AethergenPlatform delivers camera ingest, calibration, inference, policy actions, and signed evidence logs—all air-gapped and ready for the toughest stations. This guide lays out the architecture, timing budgets, acceptance gates, and evidence bundles that let QA and procurement sign off with confidence. Every detail is now live and operational as of September 1, 2025.
Assembly lines demand deterministic latency, zero-trust packaging, and auditable quality evidence. AethergenPlatform ships edge bundles for air-gapped stations: camera ingest, calibration, inference, policy actions, and signed evidence logs. This guide details station architecture, timing budgets, acceptance gates, and the evidence bundle that lets QA and procurement sign with confidence. All features are
Every station faces unique pressures. Imagine a conductor syncing an orchestra—every millisecond counts. Latency: 5–50 ms/frame is typical, ensuring actions don’t stall conveyors. Uptime: operate through network outages with local watchdogs and restart policies keeping the beat. Traceability: link decisions to lot, shift, operator, and configuration state for full accountability. Change-control: versioned policies, thresholds, and binaries with checksums maintain stability. These constraints shape a system built for the chaos of the line.
Each station tackles a specific challenge. Surface defects catch paint flaws, scratches, and dents with eagle-eyed precision. Assembly checks verify fasteners, gaps, and alignment to ensure a tight fit. Electrical tests confirm connector presence and indicator functionality. Interior scans for stitching issues, wrinkles, and fit problems. AethergenPlatform tailors solutions for every role on the line.
Defects fall into clear categories. Critical issues stop the line, major ones trigger rework, and minor ones tally for trends—think of it as a triage system for quality. Per-class thresholds and escalation routes are baked into the policy pack, ensuring every flaw gets the right response.
This is your station’s health check. Capture golden images after calibration to set the baseline. Run model with logging at full verbosity to catch every detail. Freeze operating points and store signed evidence to lock in quality. It’s like a pre-race tune-up, fully operational as of September 1, 2025.
Lighting can make or break vision systems. Day, night, mixed; seasonal presets adapt to the environment. Histogram alarms; auto switch profiles within bounds keep detection steady, like adjusting stage lights for a live show
Security is non-negotiable. Offline signed packages; QR hash verification ensures trust without a network. Role-based access; local audit logs track every move. Rollback artifacts stored locally with checksums provide a safety net. It’s a fortress built for the edge
Maintenance keeps the system humming. Enter maintenance mode; snapshot current state to preserve the baseline. Recalibrate camera/lighting; record deltas to track changes. Re-run golden set; compare drift and accept/reject to validate performance. It’s a pit stop done right
Policy triggers a fallback model or raises thresholds temporarily; evidence logs the event and recovery.
Yes—ensemble or shadow modes are supported. Shadow output is retained in evidence for future promotions.
Manifest + SBOM hashes must match the release notes; QR hash scan is the quick check.
Before you launch, ensure: Calibration signed off by the team. Golden run stored; hashes match for consistency. Operating points documented with effect sizes for transparency. Rollback and maintenance SOP rehearsed for readiness. All items are
This is the backbone of the system. Capture & preprocess handles camera sync, lens distortion correction, and illumination normalization. On-device inference runs defect detection and classification with model variants per station. Policy layer manages accept/reject/flag routing, rework loop integration, and safety interlocks. Evidence logger creates signed summaries and retains sample frames under configurable policies. All components are
Calibration keeps it accurate. Golden images and alignment targets handle camera reposition events. Lighting profiles with seasonal/shift presets and drift alarms on histogram shifts adapt to conditions. Tooling changes tracked in metadata de-risk false alarms after maintenance. All features are
Numbers drive decisions. Per-class sensitivity/specificity at chosen operating points tracks accuracy. False-call cost and rework minutes per 1k units by shift measure efficiency. Throughput impact (units/hour) at current thresholds gauges speed. Stability across lighting/tooling/seasonal shifts ensures reliability. All metrics are
This is your proof package. Model cards detail training data specs, limits, and known failure modes. Operating points per class include ROC/PR curves and confidence intervals. Traceability links lot/shift decisions, policy thresholds, and rework triggers. SBOM and signed manifests (hashes) cover all binaries and configs. All components are
Device-specific builds (INT8/Q4/FP16) and policy packs (thresholds, logging) ship as a signed tarball. Optional QR-encoded manifest hashes enable handheld verification at the line—no network required. All features are
Here’s the deployment playbook. Pilot: one station, one defect class; freeze acceptance criteria and rework policy. Gates: select operating points; document effect sizes; capture golden runs. Rollback: define drift alarms and reversion triggers; rehearse the procedure. Scale-out: replicate station bundles; keep gates identical to preserve evidence. All steps are
With AethergenPlatform you don’t just deploy models—you deploy proof. QA signs off on numbers that map to throughput and rework, not vibes.
Every step is timed like a race. Capture: 1–3 ms grabs the frame. Preprocess: 1–5 ms cleans it up. Inference: 5–25 ms (model-dependent) spots defects. Policy & I/O: 1–5 ms decides the action. All within 5–26ms total latency
Failures happen—here’s the safety net. Camera dropout → failover to redundant sensor; alert operator. Lighting shift → alarm and auto-adjust profile; hold thresholds if needed. Model error spike → revert to last good operating point; log evidence. All safeguards are
The policy layer ties into rework cells. Defects above severity S route automatically, and evidence links each rework to the original frame and decision context. All features are
Before signing off, confirm: SBOM + signed manifests are in hand. Per-class operating points with CIs are documented. Golden image sets and calibration procedures are ready. Rollback scripts and rehearsal records are tested. All items are