DeepInspect® · Industries & Use Cases
Vision AI Across
Every Production Line
From spark plugs to syringe vials, DeepInspect® deep-learning models adapt to the part variability, surface complexity, and SKU mix that rule-based machine vision struggles with — deployed in 2 weeks, trained on fewer than 200 good-part images, running on your existing cameras.
Automotive
Pharma & Biopharma
Electronics
Consumer Goods & FMCG
Food, Beverage & Glass
01
Coverage Map
Four Industries. One Inspection Platform.
Every line is different. Every defect is different. DeepInspect® doesn't ship industry-specific firmware — the same edge platform learns your parts, your defects, and your line speed. Below: where it's already in production.
01 · Automotive
Body, Powertrain & Components
75%
QA Labor Reduction
Stamping, welds, fasteners, brake pads, pistons, bearings, spark plugs, sealant beads. Surface defects on reflective metal where rule-based vision fails.
See use cases →
02 · Consumer Goods
FMCG, Packaging & Closures
1,000+
PPM Throughput
Cap placement, seal integrity, label OCR, fill level, contamination. 360° cap inspection without part orientation. Glass bottle bore inspection.
See use cases →
03 · Pharma & Biopharma
Sterile Vials, Capsules & Closures
21 CFR
Part 11 Compliant
Particulate inspection in sterile vials, gelatin capsule defects, syrup cap 360° inspection, audit trail and electronic-signature ready.
See use cases →
04 · Electronics
PCB, SMT & Battery Pack
150µm
Min Defect Size
PCB soldering defects, missing or misaligned components, battery pack weld inspection, connector verification. Area + line-scan.
See use cases →
02
Automotive Manufacturing
From Spark Plug to Powertrain — Surface Inspection on Reflective Metal
Automotive is where deep learning earns its keep. Stamped, painted, cast, welded — surfaces that rule-based vision can't handle because every part orientation is different and every lighting condition shifts the rejection threshold. DeepInspect® learns the part once and runs at line speed.
Powertrain
Piston & Connecting Rod
Robotic + camera cell inspecting forged conrods and pistons — surface scratches, casting porosity, machining inclusions. Train on 200 good parts, deploy in 2 weeks. Read the vendor case study for the cell layout.
Components
Bearings, Fasteners & Sprockets
High-volume precision-metal parts where dimensional gauging passes but surface quality is rejectable. Detects pitting, plating defects, missing chamfers, OD scratches across reflective metal.
Safety-Critical
Brake Pad & Friction Surface
Friction-material distribution, backplate corrosion, edge chips, glue-line voids. Replaces manual visual QC where escape rate matters more than throughput — vendor case study reports sustained accuracy on a Tier 1 line.
Tier 1
Spark Plug 360° Inspection
Rotational camera arrangement around the plug — insulator cracks, electrode-gap deviation, thread surface defects, ground-electrode bend. 4–8 cameras per station, no orientation rig.
Body-in-White
Sealant Bead & Weld Verification
Continuous bead presence, width, break detection on automotive subframes; spot-weld presence and quality across stamped assemblies. Where templating fails on part-position variability, deep learning generalizes.
Aftermarket
Tire Sidewall & Tread
Sidewall lettering OCR, tread defect detection, sidewall bulge and undercure indicators. Adapts across SKU sizes without retraining from scratch — vendor case study covers the multi-SKU model loadout.
03
Consumer Goods & FMCG
Caps, Bottles, Closures & Packaging — at 1,000+ PPM
High-speed packaging lines where a 0.3% defect escape is too many. Deep learning handles label variability, reflective transparent surfaces, and cap-orientation differences that template matching can't.
Beverage / FMCG
HDPE Bottle Inspection
Seal integrity, cap placement, label position, fill level, surface contamination across 40+ SKUs. Replaces legacy machine vision on a global FMCG line — defect rate dropped 0.3% → 0.05% on an Ariel detergent install.
Glass Container
Bore & Bottle Inspection
40+ SKUs (50–350mm). Trained on 200 images per SKU, detects defects >500 microns. Neck-diameter variations, choked necks, foreign particles. Eliminated 2 manual inspectors on a high-speed glass line.
Closures
360° Cap & Closure Inspection
Rotation-invariant cap inspection — print quality, tamper-evident band integrity, cap height, color verification — without orienting the bottle to a fixed angle. Pouches, crown caps, screw caps, snap caps in one model.
04
Pharma & Biopharma
Sterile Vials, Capsules & Closures — 21 CFR Part 11 Audit-Ready
Regulated manufacturing where every inspection event needs an electronic signature, every model change needs a traceable record, and every false-negative is a recall. DeepInspect® analytics is FDA 21 CFR Part 11 compliant out of the box.
Biopharma
Sterile Vial Particulate Inspection
Dual-camera optical assembly with diffuse backlight + ring light, vacuum-gripper handler, PLC integration. POC at Rev1 Auburn Hills validated 0% FP / 0% FN across six particle classes for a global biopharma manufacturer.
Solid Dose
Gelatin Capsule Defect Inspection
Capsule body / cap mismatches, dimples, dents, splits, foreign particles trapped in transparent gelatin, color drift across batches. Compatible with high-speed encapsulation lines and 21 CFR Part 11 audit trail.
Liquid Dose
Syrup Cap 360° Inspection
Cap seal, fill-level, label OCR, tamper-evident band, shoulder-area particulate — all in one station for syrup and oral-liquid bottles. SOC 2 Type II cloud analytics for cross-facility QA aggregation.
05
Electronics Manufacturing
PCB, SMT Assembly & Battery Pack
Board-level inspection where component density, color similarity (green PCB / green solder mask / green capacitors), and reflective solder make rule-based vision brittle. Area-scan + line-scan supported in the same station.
PCB / SMT
SMT Component & Soldering Inspection
Missing components, misaligned connectors, solder bridges, insufficient solder, tombstoning. Replaces or augments AOI for boards where geometry varies SKU-to-SKU. Area + line-scan supported in one station.
EV / Battery
Battery Pack Weld & Cell Inspection
Tab-weld presence, weld-bead quality, cell-to-busbar alignment, cell-can dent / scratch. Critical for EV-pack throughput — a single bad weld means thermal-runaway risk downstream.
06
Where Deep Learning Wins
Three QA Approaches. Three Different Cost Curves.
Most factories have run the same machine-vision setup for a decade — rule-based systems from Cognex or Keyence that work great on rigid parts in fixed orientations. Deep learning shows up when the part varies, the SKU mix grows, or the defect itself is hard to spec. Here's the honest tradeoff.
Approach 01
Manual Visual QC
Where it still works. Low-volume, high-mix, defects that a human spots instantly because context matters. Aerospace finish, custom assemblies.
No capital cost upfront
Operators drift across shifts — 60–80% sustained accuracy is the realistic ceiling
No audit trail, no Pareto, no analytics
Scales with headcount, not throughput
Approach 02
Rule-Based Machine Vision
Where it still works. Rigid parts, fixed orientation, defects you can spec with geometry — measurement, presence/absence, barcode read. Mature, proven, well-supported on the shop floor.
12–16 weeks integration typical with a custom engineering scope
Re-tuning required for every SKU change, lighting drift, part orientation shift
False-positive rates climb on reflective metals, transparent surfaces, complex geometry
Excellent for what it's spec'd for; brittle outside that spec
Approach 03
Deep-Learning Vision AI
Where DeepInspect® fits. Variable part orientation, reflective / transparent / textured surfaces, growing SKU mix, defects you can't fully spec but can show examples of. Trained on fewer than 200 good parts.
2 weeks kickoff to live production (Rev1 managed)
No-code SKU training in ~45 min — operators do it, not engineers
99.95%+ production accuracy across 1,000+ SKUs globally
Hardware-agnostic: works with your existing GigE / USB3 Vision cameras
FDA 21 CFR Part 11 / SOC 2 Type II / on-premise edge inference
07
Frequently Asked
Industry-Specific Questions We Hear
We already run rule-based machine vision on this line — do we have to rip it out?
No. DeepInspect® often runs alongside an existing system as the deep-learning stage downstream of templating. Templating catches the high-confidence pass/fail, the AI stage catches what templating couldn't define. Most teams keep their existing cameras and PLC and just add the edge controller.
What does deployment look like for an automotive Tier 1?
2 weeks from kickoff. Week 1: fixture design, camera mount, PLC wiring, model training on your good parts. Week 2: factory acceptance test, shadow-run against existing QA, sign-off. Rev1 (Auburn Hills, MI) handles the full engineering scope — you don't need a vision-AI hire.
For pharma, what does FDA 21 CFR Part 11 actually cover here?
Electronic audit trail of every inspection event, electronic signatures on model changes, role-based access control, SSO integration, full traceability of model retraining and PLC interactions. The platform is audit-ready out of the box; Rev1 supports IQ/OQ/PQ documentation for your validation team.
How many SKUs can a single station handle?
50+ trained models per station, simultaneously. The PLC sends a changeover signal and the correct model auto-loads — no manual intervention, no line stoppage. This is the use case that breaks most rule-based setups: every SKU change becomes a re-tuning project.
Can it run air-gapped? We don't allow cloud connections on the production network.
Yes. All inference runs on the on-premise DeepInspect® edge controller. Cloud connectivity is optional and only used for analytics aggregation. Production lines can run fully air-gapped.
What's the relative investment vs. a Cognex or Keyence quote we already have?
Hardware + integration is competitive (we use the same industrial cameras — Basler, Baumer, Allied Vision, FLIR). Software is an annual subscription starting around $10,000/station/year (up to 6 cameras). Most teams come out lower total cost over 3 years because deployment time is 2 weeks instead of 12–16 and SKU changeovers don't bill new engineering hours.
Who actually deploys it — you or someone we hire?
Rev1 deploys it. We're the US integration partner for DeepInspect®. Fixture design, camera mounting, PLC wiring, model training, factory acceptance test, operator training, post-deployment hypercare — one Michigan engineering team, start to production sign-off.
Can I see it on my own line before we commit?
Yes. Send us 50–200 sample parts (good + bad). Rev1 builds a proof-of-concept rig at our Auburn Hills facility, demos it remotely or on-site, and only then do you scope the production deployment. Most POCs land in 2 weeks.
Talk to a Vision AI Engineer
Have a Defect Type
That Doesn't Fit a Template?
If you've already got a Cognex or Keyence quote that doesn't quite cover the problem, or if you've been quoted 12+ weeks on integration, send us a few sample parts. We'll run the POC at Rev1 Auburn Hills, share the model output, and give you an honest assessment — even if the answer is "stick with what you have."