DEFECT_DET_v3 · INFERENCE_ACTIVE
▸ DETECT / COMPUTER VISION SYSTEMS

MACHINES
THAT SEE.
DEPLOYED
IN WEEKS.

We engineer production-grade vision pipelines for factory QA, warehouse operations, and satellite analysis — without the 18-month build timeline.

99.7%
avg confidence
Defect Detection
LIVE
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REC ● 14:54:361920×1080 · 30fps
Steel surface under industrial inspection with visible micro-fracture patterns and surface irregularities
MICRO_FRACTURE
99.7%
SURFACE_PIT
98.2%
EDGE_CRACK
97.4%
INCLUSION
96.1%
4 OBJECTS DETECTED▸ INFERENCE 4.2ms
MODEL:DEFECT_DET_v3|BACKBONE: YOLOv9-CUSTOM|DEVICE: EDGE_GPU
Scroll for benchmark data
▸ BENCHMARK_REPORT_2025 / METHODOLOGY: DIRECT_CLIENT_DATA

THE BUILD-VS-BUY
NUMBERS.

Eight dimensions. Three options. Data from 23 production deployments. Click any row to see the case study behind the number.

DIMENSION
IN-HOUSE BUILD
OFF-SHELF API
DETECT
Time to Deploy
TTD
14–18 months
3–6 weeks
4–8 weeks
▸ CASE_STUDY / TTD
Automotive Tier-1 supplier: stamping line defect detection
BEFORE
Manual QA team of 12 reviewing 4,200 parts/hour with 12.4% false rejection rate
AFTER
Vision pipeline deployed in 6 weeks — false rejection rate dropped to 0.8%
6 weeks from kickoff to production
Tier-1 Auto Supplier, Ohio
Accuracy Ceiling
ACC_MAX
85–92% (yr 2+)
78–88% (generic)
94–99.7% (custom)
▸ CASE_STUDY / ACC_MAX
PCB manufacturer: solder joint inspection
BEFORE
Off-the-shelf API hitting 81% accuracy on micro-solder defects — unacceptable for IPC-A-610 compliance
AFTER
Custom-trained model reached 97.3% accuracy on client's specific board variants
9 weeks to production-grade accuracy
PCB Manufacturer, Shenzhen
Edge Device Support
EDGE_COMPAT
Possible (costly)
Cloud-only
Native (NVIDIA/ARM)
▸ CASE_STUDY / EDGE_COMPAT
Cold-chain logistics: freezer inventory counting without cloud latency
BEFORE
Cloud API adding 340ms latency per frame — unusable for real-time pallet tracking in 6 warehouses
AFTER
Edge-deployed model running at 28fps on NVIDIA Jetson — zero cloud dependency
5 weeks including hardware validation
Cold-Chain Operator, Netherlands
Retraining Cost
RETRAIN_$
$180K–$340K/yr
Not available
$8K–$22K/cycle
Custom Model Ownership
MODEL_OWN
Full
None
Full (client-owned)
▸ CASE_STUDY / MODEL_OWN
Satellite imagery startup: building proprietary IP without a CV team
BEFORE
Series B startup with $0 CV IP — needed defensible proprietary models before Series C
AFTER
Full model ownership transferred — 3 patent applications filed on novel architecture adaptations
14 weeks, 4 model variants delivered
Geospatial Analytics Startup, SF
Integration Depth
INT_DEPTH
Full (18mo)
REST only
Full (SCADA/ERP/WMS)
Ongoing Support
SUPPORT_SLA
Internal team
Ticket only
Dedicated + SLA
Total 12-Month Cost
COST_12M
$680K–$1.2M
$48K–$120K
$72K–$180K
▸ CASE_STUDY / COST_12M
Full cost-of-ownership analysis across 23 engagements
BEFORE
Median in-house CV team: $847K year-one cost (salaries, tooling, cloud, delays)
AFTER
Median Detect engagement: $94K total — including model, integration, and 12mo support
Based on 23 completed engagements, 2022–2025
Internal Benchmark Data
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▸ PRODUCTION_ENGAGEMENTS / VERIFIED_RESULTS

HARDER PROBLEMS
ALREADY SOLVED.

Three engagements in ascending complexity. Each one shipped to production. Each one with numbers you can verify.

CS_001
MANUFACTURING / DEFECT_DETECTION
Tier-1 Auto Supplier, Ohio
Automotive Stamping

Manual QA team inspecting 4,200 stamped steel parts per hour. False rejection rate was destroying throughput — 12.4% of good parts being scrapped, $2.1M annual waste.

YOLOv9-customNVIDIA Jetson AGXSCADA bridgeOPC-UA

"The model sees things our best inspectors miss — and it never gets tired at the end of a 12-hour shift."

— Tier-1 Auto Supplier, Ohio

BEFORE
12.4%
False Rejection Rate
AFTER
0.8%
False Rejection Rate
6 weeks to production
CS_002
LOGISTICS / OBJECT_COUNTING
Cold-Chain Operator, Netherlands
Cold-Chain Warehousing

Six warehouses with no real-time pallet count. Inventory reconciliation took 3 hours per shift per facility. A cloud API trial added 340ms latency — unusable for live tracking.

Custom CNNNVIDIA Jetson NXWMS APIMQTT

"Six warehouses, zero manual counts. The system just knows."

— Cold-Chain Operator, Netherlands

BEFORE
3 hrs
Manual Inventory Time / Shift
AFTER
0 hrs
Manual Inventory Time / Shift
5 weeks to production
CS_003
SATELLITE / SCENE_PARSING
Series B Startup, San Francisco
Geospatial Analytics

Series B company selling satellite analytics to insurance underwriters. Their generic segmentation model hit 79% mIoU — not defensible for pricing commercial real estate risk.

Segment Anything (fine-tuned)U-Net variantAWS SageMakerQGIS pipeline

"We went into our Series C with proprietary model IP. That changed the entire valuation conversation."

— Series B Startup, San Francisco

BEFORE
79%
Segmentation mIoU
AFTER
94.3%
Segmentation mIoU
14 weeks to production
23 PRODUCTION ENGAGEMENTS · 2022–2025
Avg. time to production-grade accuracy: 7.4 weeksAvg. accuracy improvement: +18.6 percentage points
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WE'LL SPEC YOUR
PIPELINE IN A CALL.

No deck. No demo. A 45-minute technical conversation where we tell you exactly what architecture we'd build, what accuracy you'd get, and what it would cost. You leave with a spec sheet whether you engage us or not.

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