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Businesses often use visual data from text, images, and videos for quality assurance. There are computer-based systems for the visual tasks known as Computer Vision (CV). They can increase productivity in enterprise operations, reducing the need for manual inspection.
But they often struggle to adapt without retraining. So, businesses are now shifting towards Vision AI Agents. And this intelligent system can perform visual tasks without the need for retraining.
In this blog, we will explore:
Computer Vision is a branch of AI. It helps computers interpret and understand visual information like humans do.
For example, Toyota uses it to inspect vehicle components. Hikvision applies this technology in security cameras. It can cut false alarms from animals or lighting changes.
While effective for specific, repetitive tasks, a traditional CV has some limitations:
Each task often requires a dedicated model and workflow
It needs retraining if there is a change in its operating condition or environment. For example, when there is a change in lighting or camera angle.
It struggles with unfamiliar or unstructured environments
There are systems powered by AI that can analyse images and take action. They can perform visual inspections and deliver insights in real time. For example, recognising objects, counting items, or spotting problems.
Amazon uses these intelligent vision systems to manage inventory in warehouses. Another company, Landing AI, utilises it to catch product defects.
Some of the major features of this system are the following:
Pre-trained and task-specific
It performs specialised jobs without the need for retraining. They can perform tasks like label validation, counting objects, or damage detection.
Composable logic
Agents can complete multi-step tasks in a single workflow. For example, counting objects and detecting any errors in their labels.
Plug-and-play integration
It is easy to include visual agents in your systems through APIs or a simple UI. This makes automation seamless without disrupting your processes.
They work in real time and scale on their own. They can deliver reliable performance in a single store or hundreds of stores.
Below are some major differences between the two visual automation systems:
Aspect
Traditional Computer Vision
Vision AI Agents
Setup
It takes a team of data scientists, large datasets, and training to build these systems.
These systems require low code to set up. Businesses or users can start using it without any special skills.
Flexibility
Computer vision-based systems have a rigid pipeline. They need retraining when there is a change in their operational environment, such as a change in lighting or angle.
Vision AI-powered systems are reusable across tasks and easily adjusted to changing conditions.
Speed to Deploy
Takes weeks or months to build, test, and roll out.
Can be deployed in minutes or hours, speeding up time-to-value.
Output
Provides raw data (e.g., bounding boxes, labels) needing further processing or manual review.
Delivers actionable outcomes ready for operational use (e.g., flagging expired items).
Scalability
Scaling requires manual setup and maintenance for new locations or systems.
Auto-scalable via platform; easy expansion across stores, warehouses, or facilities.
Error Handling
Sensitive to environmental changes, causing high error rates.
Robust under real-world noise and variability, maintaining high accuracy.
Performing visual tasks with better accuracy and speed is possible with Vision AI. They can automate workflows in industries.
In retail, AI-powered tools can recognise fresh products and verify pricing. They prevent fraudulent attempts in product return or checkout. For example, Tiliter’s Product Recognition API can recognize products even without a barcode. It can be very useful as a solution to many major challenges in retail.
In logistics, AI systems detect damaged goods, verify packages, and count items. They also perform shipment audits, cutting manual checks and errors. DHL leverages this technology to speed up parcel sorting and damage detection. Tiliter’s Damage Detector can also detect damaged or defective products with accuracy.
Manufacturers use AI vision for quality assurance, identifying defects or missing parts. They ensure product assembly is complete following essential standards. Toyota has applied AI-powered inspections for their manufactured vehicles. They have reduced distributing defective products by 32%.
AI-based visual systems can check surgical trays before medical procedures. They can check cleanliness and hygiene standards. This automation helps reduce errors and enhance patient safety. Hospitals are adopting these tools to improve safety and compliance.
AI-based systems often rely on stable edge or cloud infrastructure. So they may not always be available. Their ability to handle tasks beyond their training is still evolving.
Visual AI solutions are evolving into more advanced and integrated platforms. Key trends include:
Choosing an intelligent visual automation system should depend on your needs and resources. There is no complexity of setting up or retraining a system if you switch to Vision AI. It delivers actionable results and integrates into existing workflows.
Traditional CV works best for niche problems in controlled environments. It’s a good fit if you already have an in-house ML team.
For most other cases, Vision AI Agents are the better option. They deploy faster, cost less to maintain, and handle tasks with better accuracy. In about 80% of visual workflows, they’ll give you better results with less effort.
1. What is a Vision AI Agent?
A Vision AI Agent is an intelligent system that can capture, understand, and act on visual data. Unlike traditional CV models, agents are goal-driven, modular, and adaptable. They can handle multiple tasks without retraining.
2. Can I replace my current CV setup with agents?
In most cases, yes. Vision AI Agents can plug into existing workflows and perform the same tasks. They are usually faster and add more flexibility to your enterprise operation without the need for complex model development.
3. Do Vision AI Agents require training data?
Not usually. Many agents come pre-trained for specific tasks like label validation, object counting, or defect detection. You can deploy them out of the box and fine-tune if needed.
4. Are Vision AI Agents secure and private?
Yes. Tiliter’s Vision AI Agents follow strict data security protocols, with options for on-premise or cloud deployment to meet your compliance needs.
5. How do agents integrate into my workflow?
Agents can be connected via API or used through a no-code/low-code interface, making them easy to add to your current systems without disrupting operations.
Explore Tiliter’s Vision AI Agents and discover how they can transform your visual workflows.
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