BEFORE YOU SIGN ANY CONTRACT
Most Vision AI pilots succeed. The problem is what happens at month 6, month 12, and when you try to scale to site 3. The 12 capabilities below determine whether your deployment compounds in value — or quietly stalls.
1) Hardware-agnostic camera compatibility [Critical]
If a platform requires you to buy their cameras, you're not buying software — you're buying a hardware bundle with software attached. Hardware-agnostic means the platform works with any RTSP or ONVIF-compatible camera you already have. Your existing CCTV investment should be the starting point, not something to replace. Platforms that bundle hardware create vendor lock-in, inflate upfront cost, and limit your flexibility as technology evolves.
ASK THE VENDOR
Will this platform work with the IP cameras we already have installed? What's your compatibility list, and what happens if we need to add a different brand at a new site?
2) Sub-10ms real-time inference at the edge [Critical]
Cloud-based AI inference carries 600–800ms of round-trip latency. On a conveyor running at 2 meters per second, that means the product has travelled 1.6 meters past the detection point before a reject decision is made. For inline rejection, you need sub-10ms inference, which requires edge processing. This is not a performance preference — it is a physics constraint that cloud-only platforms cannot solve.
●Edge AI delivers <12ms inference vs 800ms for cloud round-trip. On a 2m/s conveyor, that is the difference between catching a defect inline and losing it downstream.
ASK THE VENDOR
What is your measured inference latency at the edge — not in a lab, but on a production-speed line? Can you demonstrate inline rejection at our line speed?
3) 98%+ detection accuracy — with deployment context [Critical]
Every vendor claims high accuracy. The number is meaningless without context: what defect types, what line speed, what camera placement, what lighting conditions, and what baseline were they comparing against? Human inspection accuracy hovers at around 80% under ideal conditions — less during night shifts and high-volume periods. AI catching 37% more defects than a human inspector is a meaningful benchmark.
●ASQ (2024) reports 99.8% surface defect accuracy for optimized Vision AI deployments. Human inspection peaks at ~80% under ideal conditions.
ASK THE VENDOR
Can you share accuracy benchmarks from a production deployment comparable to ours — same defect types, similar line speeds, similar environment? What were the false positive and false negative rates?
4) Self-learning and adaptive models [Critical]
Static models are trained once on a fixed dataset and degrade as the production environment changes. New product variants, changed lighting, supplier material variation, equipment wear — any of these shift what "normal" looks like, and a static model cannot adapt. Self-learning models continuously update from live production data, maintaining and improving accuracy over time. The difference between a system that gets better the longer it runs and one that slowly erodes is the difference between a compounding asset and a maintenance burden.
ASK THE VENDOR
What happens to model accuracy when we introduce a new product variant? Does retraining require your team's involvement, and if so, what does that process and cost look like?
5) Closed-loop workflow: detect → assign → escalate → close [Critical]
A platform that detects defects but does not close the response loop is an expensive alert system. Closed-loop means every detection event automatically routes to an assigned owner, escalates if unaddressed within a defined timeframe, and closes only when a documented resolution is recorded. Without this, you get alert fatigue — inspectors start ignoring notifications, confidence in the system drops, and the business case collapses.
ASK THE VENDOR
Show me what happens from the moment a defect is detected to the moment the incident is closed. Who gets notified, how, and what does the evidence trail look like in your system?
6) Timestamped, audit-ready evidence packs [Important]
Compliance audits, customer disputes, and regulatory inspections all require documented evidence. An AI visual inspection system should automatically generate a structured evidence pack for every detection event: camera ID, timestamp, confidence score, classification, assigned response, escalation history, and close-out documentation. Manual assembly of audit evidence from multiple systems is expensive and error-prone.
ASK THE VENDOR
If we needed to pull every quality event from a specific production line over the past 90 days for a customer audit, how long would that take and what format would the data be in?
7) Flexible deployment: On-Premises, Cloud, and Hybrid [Important]
A platform that forces one deployment model creates problems. Regulated industries often require on-premises for data sovereignty. Multi-site operations benefit from cloud centralization. High-speed production lines need edge processing. Hybrid deployment — combining edge inference for real-time decisions with cloud analytics for dashboards and multi-site learning — is increasingly the enterprise standard. Data compliance must be guaranteed across all three models.
ASK THE VENDOR
Can we run on-premises at our regulated facility and cloud at our other sites — from the same platform? What does data compliance look like in each model?
8) Multi-model orchestration per camera stream [Important]
A single production camera should be able to run multiple AI models simultaneously — surface defect detection, OCR for label verification, PPE compliance checking, and count verification — without deploying additional cameras or hardware. Platforms that run one model per stream force you to multiply cameras and costs with every new use case. Multi-model orchestration is what separates a platform from a point solution.
ASK THE VENDOR
Can one camera stream run defect detection and OCR label verification simultaneously? How many models can run on a single stream, and does that affect inference speed?
9) Model flexibility: pre-built, custom, and bring-your-own [Important]
Pre-built models should deliver value from day one — no waiting weeks for custom development before you see your first detection. But your operation also has unique requirements that off-the-shelf models will not address. The platform must support custom model development, fine-tuning, and bring-your-own-model (BYOM) for organisations that have already invested in proprietary AI. Vendor lock-in on model choice is a significant long-term risk.
ASK THE VENDOR
We already have some AI models our team developed. Can we bring those into your platform and run them alongside your pre-built models? What is the process?
10) Native MES / ERP / PLC integration [Important]
Vision AI that does not close the loop into your existing automation creates a parallel data silo. Detection events should trigger actions in your MES — pausing lines, triggering rework queues, updating batch records. Quality data should flow into your ERP without manual re-entry. PLC integration enables automated physical responses — rejection actuators, line stops, diverter gates — triggered directly by Vision AI decisions.
ASK THE VENDOR
Show me how a defect detection event triggers an action in our MES. Do you have pre-built connectors for our specific system, or does this require custom integration work?
11) Multi-site scaling with central governance [Enterprise-Grade]
Single-site performance is table stakes. The enterprise question is what happens at site 5, site 10, and site 20. Linear scaling economics mean the incremental cost of adding a new site should be significantly lower than the first — because the platform shares model intelligence, integration templates, and governance frameworks across deployments. Central governance means quality standards and compliance protocols are consistent across every facility.
●Multi-site learning propagates best practices across facilities in days rather than months — compounding ROI across every deployment rather than restarting it at each site.
ASK THE VENDOR
What does it cost and how long does it take to add a fifth site, compared to the first? What is the specific mechanism by which learnings from one site improve detection at another?
12) Role-based dashboards for every stakeholder [Enterprise-Grade]
One dashboard for everyone is a dashboard that works for no one. HQ leadership needs estate-wide KPIs. Plant managers need real-time line performance. Quality teams need defect trend data. Auditors need compliance evidence. Security teams need access violation logs. Role-appropriate data visibility is what converts platform capability into daily operational use — and daily use is what delivers the compounding ROI.
ASK THE VENDOR
Show me the dashboard view for our quality director at HQ, our plant manager, and our external auditor — separately. Are these different views or just different filters on the same screen?
Frequently Asked Questions
What accuracy should AI visual inspection achieve in manufacturing?
Enterprise-grade AI visual inspection should achieve 98%+ detection accuracy under production conditions — not just in controlled pilots. Research shows AI catches 37% more defects than human inspectors (2024), and surface defect accuracy can reach 99.8% in optimized deployments (ASQ, 2024). More importantly, accuracy should improve over time as the model learns your specific environment.
What is the difference between cloud and edge AI for visual inspection?
Cloud AI sends image data to a remote server for processing, adding 600–800ms of round-trip latency. Edge AI processes data locally, delivering inference in under 10ms. On a conveyor at 2 meters per second, 800ms means the product has travelled 1.6 meters past the rejection point. For inline inspection and real-time rejection, edge AI is not optional.
What does closed-loop workflow mean in AI visual inspection?
Closed-loop means every detection event automatically routes to an assigned owner, escalates if unaddressed within a defined window, and closes only when a documented resolution is recorded. Systems that only alert without workflow closure create fatigue and leave defects unaddressed.
How long does AI visual inspection take to deliver ROI?
Industry benchmarks show ROI payback typically within 6–12 months. Drivers include 50–90% reduction in inspection time, 30–80% reduction in inspection labor costs, and up to 40% reduction in defect rates. Deloitte's 2024 automotive analysis found 83% fewer defect escapes in Vision AI-enabled facilities.
See How AegisVision Scores on All 12
Hardware-agnostic. Sub-10ms edge inference. Self-learning models. Closed-loop workflow. Multi-site governance.