Finding the right computer vision platform for deployment is notoriously difficult. Most platforms can identify a dog in a static image, but very few can trigger a reject gate on a high-speed production line, maintain SOC 2 compliance, and manage a fleet of edge devices across ten global factories.
To evaluate true production readiness, we’ve compiled a computer vision system RFP template. This guide outlines the 20 critical questions your team must ask to ensure a platform can bridge the deployment gap. It covers everything from on-premise hardware orchestration and industrial protocol integration (PLC/OPC-UA) to long-term model monitoring and enterprise-grade security.
Computer Vision System RFP Template
When evaluating a computer vision platform, key considerations include the lifecycle tools, infrastructure, connectivity, and reliability. The lifecycle is all about end-to-end orchestration; if a solution doesn't cover everything from labeling to monitoring, your team might waste months putting fragmented tools together. Infrastructure requirements, such as On-Prem or VPC support, are the non-negotiable foundations for security-conscious industries where data simply cannot leave the building. Finally, connectivity and reliability ensure the system actually works on the floor, using industrial protocols like MQTT to talk to your machinery while using active learning loops to prevent accuracy from drifting as factory conditions change.
Technical Requirements Covered
- Platform Architecture
- Deployment Flexibility
- Security & Compliance
- Access Control & Identity
- Camera & Hardware
- Manufacturing & QA
- Real-Time Processing
- Monitoring & Audit
- Integration & APIs
- Support & SLAs
What does the platform actually do?
Before anything else, make sure you understand what the platform covers end to end.
1. Does the platform cover the full computer vision lifecycle, from data collection and labeling through training and production deployment?
You want one platform that handles everything, collecting and labeling your images, training your model, building the logic around it, deploying it, and monitoring it once it's live. Ask them to walk you through a project from start to finish and see if there are any gaps.
2. Is your solution turnkey, requiring minimal setup and training for non-ML teams?
The labeling interface, workflow builder, and operator dashboard should all be designed with non-technical users in mind so your quality supervisors and line operators can handle day-to-day use without needing a developer. That said, you'll still need someone technical involved at key stages, like initial setup, connecting to existing systems, and deciding when to retrain a model. A good platform reduces how much of that you need significantly, but it doesn't eliminate it entirely.
Where can you run it?
Your infrastructure has specific requirements. Make sure the platform can work within them.
3. Can your system operate fully on-premises without any cloud dependency?
Some industries defense, healthcare, certain manufacturing environments simply cannot send images or video to an outside cloud service. If that's you, the platform needs to run entirely on your own servers, with no data leaving your building. Once the system is set up and the model is loaded, it should work even if the internet goes down.
4. Does the platform support deployment within our own Virtual Private Cloud (VPC)?
If you use AWS, Azure, or Google Cloud, you may want the platform to run inside your own cloud account rather than the vendor's. This keeps your data within your own environment while still using managed infrastructure.
5. What edge hardware is supported natively, and how is it managed at scale?
If you're running the system on physical devices on-site like small computers attached to cameras on a production line you need to know exactly which hardware is supported. Ask for a specific list. Beyond that, ask how you push updates to those devices when you train a new model. Going to each device manually is not realistic if you have dozens or hundreds of sites. There should be a way to push updates remotely from a central dashboard.
Is it secure and does it meet compliance rules?
If you're in a regulated industry, these are non-negotiable.
6. What compliance certifications does the platform hold?
Ask for proof, not promises. SOC 2 Type II is the standard certification for software security, it means an independent auditor has checked that their security controls actually work over time, not just on paper. If you're in healthcare, you need HIPAA compliance and a signed Business Associate Agreement. Ask for the actual certificates.
7. Does your solution integrate with third-party security solutions?
The platform should support SSO integration with your existing enterprise identity providers, so your team logs in through the same system they use for everything else. It should also maintain full audit logs of every action taken logins, model changes, deployments which your security team can export and review.
Who gets access to what?
When many people use the same system, you need clear controls over who can do what.
8. Does the application support custom granular roles and permissions (RBAC)?
A basic admin or viewer setup isn't enough for large teams. The platform should let you define exactly what each person can and can't do, whether that's labeling images, reviewing and approving annotations, or deploying models.
9. Does the platform support Single Sign-On (SSO) with enterprise identity providers?
SSO means your team logs into the vision platform using the same company login they use for everything else - no separate username and password to manage. When someone leaves the company and their account is deactivated in your main system, their access to the vision platform is automatically removed too.
What cameras and hardware does it work with?
Make sure the platform fits your existing setup before buying new equipment.
10. What camera types and resolutions are supported?
You probably already have cameras installed and the platform should work with them. Ask the vendor for a specific list of supported camera types and connection interfaces such as USB, IP, and industrial cameras are standard expectations. Also confirm whether there's a maximum resolution limit and whether the system can handle multiple camera streams running at the same time without slowing down. The last thing you want is to buy a platform and then find out it doesn't work with your existing hardware.
11. Is there a purpose-built industrial hardware option for plug-and-play deployment?
If your team doesn't have engineers who specialize in hardware setup, ask whether the vendor sells a ready-to-go device, a small computer that arrives pre-configured, plugs into your camera, and just works out of the box. This removes a lot of the technical burden from your side and gets you up and running faster. Ask about durability too, factory floors are dusty, hot, and rough on equipment.
Can it handle factory and quality inspection work?
Generic vision platforms often fall short on the specific things manufacturing teams need.
12. Can your system verify part presence, quantity, and correct orientation?
This is one of the most common things manufacturers need, the system looking at an assembled product and checking: is every part there, are there the right number of them, and are they facing the right way? Ask the vendor for a live demo of this specific scenario with your type of product or something similar.
13. Can your system track cycle time and takt time for workstations?
If you want to use the system to measure how long each task takes at a workstation and flag when a task is taking longer than it should, the platform needs to track events over time, not just look at single frames.
14. Does the platform integrate with industrial protocols for closed-loop automation (PLC, OPC-UA, MQTT)?
In a factory, you often need the vision system to trigger something physical when it spots a problem - opening a reject gate, stopping a conveyor, or sounding an alarm. This requires the vision platform to talk directly to your existing machinery using industrial communication standards. Ask whether this is built in or whether it requires custom engineering work from your side. Built-in is much better.
15. Does the platform include a Human-Machine Interface (HMI) for floor operators?
The people standing on the factory floor aren't looking at a laptop, they need a simple screen that shows them what the AI is seeing and alerts them when something is wrong. Ask whether the platform includes a visual display designed for operators, not engineers. It should show live camera feeds with the AI's findings highlighted, surface alerts in plain language, and keep working even if the internet connection drops.
Can it process video in real time?
If the system is too slow, it can't catch problems before they pass down the line.
16. Does your system support live video processing with real-time feedback?
Ask for performance numbers on hardware similar to yours, at the resolution your cameras run at. A platform might claim to be real-time, but struggle when you add a second or third camera stream. You also want to know the worst-case speed, not just the average.
Can you track changes and see a history of activity?
Once a model is live, you need to know what's happening and be able to go back if something goes wrong.
17. Does the platform provide version history and event logs for model and data changes?
Every change to your data or your model should be saved as a snapshot, so you can always go back to an earlier version if something breaks. Think of it like a save history. If you deploy a new model and it starts making more mistakes than the old one, you want to be able to roll back in minutes, not rebuild from scratch. The platform should also keep a log of every action taken, who did what and when so nothing happens without a trace.
18. Is there active monitoring for model drift and automated mechanisms to retrain on hard examples?
A model that's 97% accurate when you launch it might drop to 85% accuracy six months later, because lighting changed, products were updated, or new defect types appeared. The platform should automatically spot when the model starts struggling and flag those tricky images for your team to review and relabel. Once reviewed, they feed back into the training pipeline to sharpen the model over time. It's not fully hands-off, someone still needs to approve the labels, but it removes the hard work of manually hunting through production data to find the problem cases.
Does it connect with your existing tools and systems?
A vision system that sits in isolation from everything else creates more work, not less.
19. Can the platform chain multiple models together in a single pipeline without custom code?
Most real inspection systems need more than one AI model working together. You might detect a component, then classify its condition, then use a second model to check a specific detail on it, all in one pass. The platform should let you connect multiple models in a sequence visually, without writing custom code to pass data between them.
What kind of support do you get?
Good support can make the difference between a successful rollout and a project that stalls.
20. Do you provide 24 x 7 support availability for production issues?
Factories run around the clock and problems don't wait for business hours. On enterprise plans, you'll want all sorts of channels such as email and phone support, in-application live chat. And you'll need a dedicated ML Field Engineer assigned to your account - someone who understands computer vision in-depth.
Cite this Post
Use the following entry to cite this post in your research:
Contributing Writer. (Jan 1, 2026). What to Include in a Computer Vision System RFP. Roboflow Blog: https://blog.roboflow.com/computer-vision-system-rfp/