What On-Prem, No-Code AI Vision
Actually Pays Back

r.o.i.

Manufacturers ask us the same question in almost every first call: "What's the ROI?" This page gives you a framework to answer that for your own line — plus real numbers from Detect-It deployments and industry benchmarks you can hold us to.

run your numbers
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$67k

Typical Year 1 investment, single line

$100.5k

3-year total cost of ownership

hours

Not weeks, to deploy — no code required

Two Industries. Two Failure Modes. Both Paid Back Fast.

the short answer

A single-line Detect-It deployment typically runs $67,000 in Year 1 and $16,750/year in renewals after that. Here's what that bought two real customers.

Anonymized · Automotive

A Tier 1 Automotive Supplier

Inspections in 30-minute pilot820
Failed parts identified65
Cycle time54s → 35s
Documented savings$600K+

That line now runs 40,000+ inspections a day across two shifts – the pilot was the baseline, not the ceiling.

Anonymized · Food Manufacturing

A Multi-Plant Food Manufacturer

Initial investment$100K
Savings, first 6 months$1.1M
Return11x
Expansion since2 more plants

Every dollar came from one place: defective product stopped before it reached a package headed to a store shelf.

build your ownn roi case in 4 inputs

the framework

You already know these numbers for your line. Plug them in.

1

Labor Reallocation

What are you paying, per shift, for manual visual inspection? Most customers redeploy inspectors to higher-value QA work rather than cut headcount.

2

scrap & rework

Your current scrap/rework rate and fully loaded cost per bad part. The visible number is often only 20–30% of the real cost.

3

cost of an escape

What happens when a defect reaches your customer instead of your line? Usually the largest number in the equation, and the hardest to see coming.

4

cycle time

Does faster inspection speed up the line itself? One deployment saw cycle time drop 35% — independent of defect-catch savings.

Annual Savings = Labor Reallocation + Scrap/Rework Reduction + Escape Cost Avoidance + Cycle Time Gain

Payback Period (months) = Year 1 Investment ÷ (Annual Savings ÷ 12)

detect-it vs. traditional machine vision

cost of ownership

A 3-year total cost of ownership comparison — because neither Cognex nor Keyence publishes theirs.

Detect-It
Cognex VisionPro
Keyence CV-X / XG-X
Pricing model
Detect-ItOne-time license + modest annual renewal
Cognex VisionProPer-seat, $10K–$50K+
Keyence CV-X / XG-XQuote-only, $8K–$25K+
Hardware
Detect-ItAny off-the-shelf camera + any NVIDIA GPU PC
Cognex VisionProProprietary cameras required
Keyence CV-X / XG-XProprietary system required
Coding required
Detect-ItNone – trains from video
Cognex VisionProYes
Keyence CV-X / XG-XYes
Deployment time
Detect-ItHours
Cognex VisionProWeeks
Keyence CV-X / XG-XWeeks
Cloud subscription
Detect-ItNone – fully on-premises
Cognex VisionProOften required
Keyence CV-X / XG-XOften required
IP ownership
Detect-ItCustomer owns 100% of models
Cognex VisionProVaries
Keyence CV-X / XG-XVaries
Est. 3-year TCO
Detect-It~$100,500 (single line)
Cognex VisionProOften $30K–$150K+ before hardware & integration
Keyence CV-X / XG-XOften $25K–$75K+ before hardware & integration

Detect-It figures reflect a typical single-Runner-license deployment. Competitor figures reflect publicly available pricing ranges; actual costs typically run higher once proprietary hardware, integration labor, and coding resources are included.

The deployment-speed factor – every week a coded, proprietary-hardware system isn't live is a week your line keeps paying for manual inspection or living with escapes. Hours-not-weeks deployment means savings start accruing immediately, not after a multi-week integration project.

what poor quality actually costs

industry benchmarks

You don't have to take our word for the stakes — here's what's independently published.

5–30%
Cost of Poor Quality (COPQ) consumes 5–30% of annual revenue for most manufacturers, per the American Society for Quality – world-class facilities hold it under 5%.
3–5x
Scrap and rework run 0.6–2.2% of revenue on the books – but the real cost is often 3–5x higher once machine time, labor, and lost capacity are counted.
1-10-100
The 1-10-100 Rule (Labovitz & Chang, 1992): a defect costs ~$1 to prevent, ~$10 to correct in-process, $100+ once it reaches your customer – 1,000x+ in aerospace and pharma.
$11.75B
Automotive warranty claims run ~2.5% of vehicle sales revenue industry-wide – three major automakers alone paid this in claims in 2024.
The takeaway: an AI vision system that catches defects on the line – the "$1–$10" stage – is displacing cost that would otherwise land at the "$100+" stage. That's the ROI case in one sentence.

where detect-it roi shows up fastest

where it works best

The common thread isn't the industry — it's high volume combined with a real cost when a defect gets missed.

1

automotive manufacturing

High volume, high warranty/recall exposure, tight cycle-time margins.

2

food & cpg manufacturing

High volume, thin margins, direct line from caught defect to avoided waste.

3

heavy equipment

High per-unit cost of a defective or missing component.

4

general manufacturing

High-mix, high-touch inspection points expensive to staff manually.