Prakash Prabhu – Chief Business Officer & Co-Founder, VisionBot
In the past few articles, we have discussed about Computer Vision and how it can address the challenges faced by many diverse processes across multiple Industries. With the staggering amount of video content generated by billions of video sensors across the globe, the capabilities of CCTV video surveillance systems are now being transformed by fundamental shifts in how video data is gathered, stored, accessed, and analysed. VIDEO Cloud is enabling a massive transformation in the ease of monitoring and analysis for data driven Digital Enterprises
Visionbot™ – Enabling the Convergence of the VIDEO Cloud and AI/ ML for Computer Vision applications
VisionBot was incorporated, with a focus to address the demand for VIDEO driven digital enterprise. VisionBot AI enabled Computer Vision, helps augment performance of workflow processes and enables improvement of overall Operational efficiency of an Enterprise. It cost-effectively manages distributed visual sensors at scale, analyses the massive volumes of visual data with artificial intelligence and distributes personalized, actionable information to decision makers.
The AI enabled VisionBot VIDEO cloud computer vision platform was built for accessibility, affordability and availability.
Bringing the benefit of cognitive Video AI for ALL is the most important part of our journey to address the Video driven roadmap for any organization.
Generative AI will probably be the singularly adopted tool for generating adaptive video content to boost engagement and drive collaboration across enterprises and social engagement. What excites us is the reality of VIDEO becoming the primary driver of the creator economy. It is inevitable that video will remain an important tool whether for work, entertainment, and learning.
VisionBot will be synonymous for all things related to the AI driven VIDEO cloud.
This journey of VIDEO based transformation, has opened a lot of conversations around the technology, its challenges, and the benefits for the early adopters of the AI driven VIDEO cloud.
What are the industry problems that are being addressed?
Among all the workflow processes within any enterprise today – visual inspection and monitoring is still heavily dependent on human reporting. Human assessment and reporting are however subjective and is prone to errors due to fatigue and inconsistency/ non repeatability. It also slows down the overall process throughput which are otherwise completely automated.
VisionBot AI driven computer vision helps companies to reduce operational costs and mitigate safety and operational gaps through custom object and event detection.
- Automates visual monitoring.
- Workflow automation and reporting.
- Improved accuracy of actionable object data.
Our Generative AI models will be assistive for attainable goals to drive long-term video adoption. Recruiting early adopters, setting content strategy, and making sure enterprises have the right technology to support goals for a more effective use of video.
- Increase employee-created content.
- Drive content diversity.
- Choose the right generative AI Tools.
- Ready to scale.
A. How does this compare with traditional technique/s?
1.Traditional Rule-based automation systems are rigid
Traditional rule based video content analysis has been incorporated in cameras (Edge) and server-based analytics server on the premise.
Rule-based automation is a system that applies manmade rules to store, sort, and manipulate data. To work, rulebased systems need a set of facts or sources of data and a set of rules for manipulating. We sometimes refer this rule to as an ‘If-else statement’ because it must follow instructions.
2. Rule-based automation need humans to identify all scenarios in advance
If you want to create a rule-based system capable of handling 100 different actions, we must write 100 different ‘if-else’ rules. Any need to update the system and add more event alerts, then we need to write a new rule.
The system does not work on its own or make intelligent decisions. Rules-based systems will also not change or update, and will not ‘learn’ from mistakes, so accuracy levels do not improve.
3. Cognitive analysis based intelligent automation can accommodate wide scopes
In comparison with rule based systems, VisionBot AI driven computer vision which is intelligent automation that with the help of artificial intelligence, helps to automate more activities.
Artificial Intelligence includes three broad areas of computer-vision/ image-recognition, (NLP) natural-language processing and (ML) machine learning (to predict outcomes). With a combination of these capabilities, we can build automation that is more human-like with model training.
Companies today face several challenges: increasing efficiency, improving decision-making, staying competitive, ensuring customer loyalty and compliance are just some of many obstacles that businesses face, cognitive automation is effective in overcoming these key challenges by supporting companies in optimizing their day-to-day activities and their entire business.
The benefits that result from cognitive automation also include improved compliance and overall business quality, greater operational scalability, reduced turnover, and lower error rates. All of this has a positive impact on business flexibility and employee efficiency.
Based on our experience, we believe that a company can expect savings of over 50% on its production activities and a reduction in the relevant costs.
B. This is relevant for large corporates, not for MSMEs?
While large-scale enterprises have long begun using new technologies like machine learning and artificial intelligence (AI) to streamline their business processes, SMEs (Small and Medium Enterprises) are still lagging behind in their effort to embrace the digital data driven enterprise.
Primary Reasons for slow adoption: How VisionBot mitigates this for MSME
- High cost of deployment: CAPEX: VisionBot subscription model allows all companies to adopt Video based AI in to their organization processes at near zero costs. On the contrary, their existing investment in Video surveillance devices are used more effectively for organizational effectiveness apart from pure surveillance and safety.
- Fast changing technology landscape: VisionBot AI driven video cloud which is hosted on AWS/ AZURE, ensures companies get the best of computing hardware, data security and scalability without the need to hire a technical expert for cloud or AI technologies.
- Fear of getting locked in: VisionBot AI driven video cloud is ready with a wide model set of object detection behaviours and user configured event alerts. Users can flexibly select and change between different AI behaviours at any point of time in their subscription. Custom based model trainings are also available to cater to specific needs at very nominal charges.
- Unclear RoI: VisionBot AI driven video cloud is a subscription-based service and needs no CAPEX investments in hardware or additional manpower. Users can register for the service and start benefitting from day one with real time actionable data for their enterprise. It however lowers RoI on existing CCTV hardware by adding AI datasets to real time or recorded video
C. Can this technology guarantee greater benefits & higher profits?
Organizations can enjoy the benefits of Artificial Intelligence software adoption in various extends of daily performance and enhance their competitive advantages and therefore profitability. Reduced risks and losses also mean better bottom lines for companies.
Gartner had predicted that AI will recover 6.2 billion productivity hours by 2021 and generate $2.9 trillion in the value of the business when combined with human intelligence.
According to the study called State of Artificial Intelligence For Enterprises, nearly 80% of the companies have already invested in some form of AI today, and 30% of companies are planning to increase their AI investment over the next 3 years. The invested companies expect a great RoI for each dollar invested today. The following graph shows the average RoI in US dollars that enterprises could receive for their investment in AI software in the next two years, five year and ten years.
D. Will it require constant upgradation, which means (regular) extra costs?
Being a subscription-based service, the application is hosted on the cloud – AWS & AZURE. Users are assured of the best and latest enterprise grade CPU and GPU computing. Apart from the subscription there are no additional operational or upgradation costs for the user for the duration of their subscription.
E. Will it replace qualified personnel?
Gartner had predicted that AI will recover 6.2 billion productivity hours by 2021 and generate $2.9 trillion in the value of the business when combined with human intelligence.
VisionBot AI driven computer vision automates the entire visual inspection and monitoring process, reducing the load on the human operator and building better accuracy. In fact it frees up the time of qualified personnel to focus in to more important tasks rather than on repetitive tasks which can be more effectively done through intelligent automation.
F. What is the USP?
- Deep learning and neural AI.
- Truly self service.
- Works with any video device.
- Zero Capex.
- Fastest go-live.
- Highly scalable.
- Simple to deploy.
- Flexible to customize.
- Centralized dashboard.
- Access from anywhere.
G. Can we have a free trial before buying?
We offer a time bound free trial with limited features. You can register for the same on our website. https://app.visionbot.io/signup.aspx
Connect with our experts to understand how companies are using Augmented Computer Vision to strengthen security, safety and streamline operations.
https://visionbot.com/contactus
Become a VisionBot™ channel partner, and discover the opportunity to offer a cutting-edge AI-powered computer vision solution to your customers at ZERO CAPEX, generate new revenue streams, and benefit from the expertise and support of the VisionBot™ team.
https://visionbot.com/partnering
We will cover VisionBot™ Partner Program and its advantages soon.
*Views expressed in the article are solely of the Author
