Be it the high-profile celebrity assassination cases, political massacres, or the controversial Vikas Dubey case, one company at the centre of it all is Staqu Technologies and its AI-powered video analytics model Jarvis. “We have identified more than 30,000 criminals in the country; we help identify around 100 criminals on a daily basis,“ said Atul Rai, CEO and co-founder of Staqu, in an exclusive interaction with AIM.  Their solution has also garnered attention across sectors, from retail and manufacturing to infrastructure, and hospitality.

Jarvis, developed by Staqu, finds application in law enforcement across various states. It is referred to as PAIS in Punjab, Trinetra in UP, Chakra in Bihar, and Eagle in Haryana. Staqu collaborates with a total of eight states, including Rajasthan, Uttarakhand, and Telangana. 

Moreover, Staqu’s Jarvis incorporates a Video Wall technology and aggregates CCTV footage from 70 prisons. It handles 700 cameras, aiding law enforcement with tasks like access monitoring, violence detection, and intrusion alerts in prisons across UP. This marks the first use of such AI-driven analytics in Indian prisons. 

Historically, criminals and illicit networks have operated from within the prison walls. With its solid support, JARVIS significantly empowers police forces in their efforts to combat crime. Additionally, it is also efficient in tasks like frisking, crowd analysis, and facial recognition. Beyond these applications, Staqu is actively developing JARVIS for diverse security scenarios as well.

The company’s image-based search and analytics platform serves business and security needs for other establishments. It uses deep learning and machine learning technologies to offer video analytics, big data tools, and auditing solutions for security and data analytics domains. In the realm of big data analytics, Staqu provides tools such as a data aggregating tool and a data analysis tool.

More than meets the eye

This is just one side of Staqu. It operates majorly in the B2B and SaaS sectors, specifically targeting the high-tech and fintech market segments. Founded in 2015 by Atul Rai, Anurag Saini and Pankaj Kumar Sharma, the company works across several sectors from retail, manufacturing, infrastructure, and hospitality to the public sector and has conglomerates such as IBM, GMR and Adani Power as clients. On the retail front, it caters to Starbucks, Piramal Glass, Marico, Tata Consumer Products Ltd, Cafe Coffee Day, Chaayos, Crocs, Khadims, Croma, and Metro.

Its business models are centred around offering a loss prevention module and a revenue enhancement module. “If you want to secure it, you will go for a loss prevention module. If you want to monetise it, you will go for the revenue enhancement module,” Rai explained.

The revenue enhancement module primarily targets the retail and real-estate sectors. It aims to bridge the data gap in the retail industry, offering insights similar to e-commerce data. Leveraging existing camera infrastructure, JARVIS provides valuable insights to retailers. It tracks footfall, customer journey, gender demographics, product engagement, and queue analysis. By utilising a unique tracking algorithm, JARVIS accurately measures footfall, excluding employees. This technology addresses the lack of critical data in the retail sector, offering valuable metrics for business optimisation. Now, it also finds its use case in catching criminals. 

The working mechanism involves connecting observers from cameras to JARVIS through the internet, eliminating the need for extra hardware and associated costs. Data analysis occurs in the cloud via custom JARVIS, ensuring end-to-end encryption. This approach ensures data ownership and GDPR compliance. The camera’s features provide insights which, when combined with sales data, generate analyses like customer qualification percentages. The outcomes are tailored for clients like retail stores, enabling informed decision-making based on comprehensive insights.

Video management ensures centralised control for businesses with multiple locations. JARVIS, facilitates plug-and-play insights from various stores, covering occupancy analysis and beyond. Additionally, it offers security features like intrusion detection and identifying blacklisted individuals. JARVIS operates as a unified solution, eliminating the need for additional hardware—just an internet connection is required for implementation.

Questions about enterprise adoption and the possibility of integrating additional features were addressed by Rai when he explained the approach taken, he stated, “Imagine an enterprise adopting our system and wanting to expand its capabilities. We’ve structured it as a platform rather than a prescriptive solution, akin to Google’s modular approach with services like Gmail.”

“In our industry, lacking AI expertise is common, even when APIs are available. Major companies like Google and Microsoft provide APIs, but accessing them requires technical proficiency. By producing our platform, we provide solutions to real business challenges faced by industry giants like Starbucks, Raymond, and CCD. Our aim is to bridge the gap between AI potential and practical implementation.”

Funding-wise, Staqu has secured $2.11 million in total funding through four seed rounds and achieved a valuation of $8.57 million as of April 27, 2022. In the last undisclosed seed round on May 25, 2022, Security and Intelligence Services led the investment. Staqu has 64 investors, including five institutional investors like Security and Intelligence Services and 59 Angel investors, including Bikky Khosla. Previous Seed rounds saw participation from Mount Judi Ventures, Ayushman Khanna, Ajay Gupta, and Neeraj Kumar Singal.

Innovations Powering Staqu

Staqu’s proprietary technology consists of two main components: the streamer algorithm and an IP portfolio with 25 research papers and four patents. The streamer algorithm handles diverse camera sources by unifying recommendations and analyses, compatible with existing systems like DVR NVR setups.

The IP portfolio focuses on advanced identification tech and big data search methods. It precisely pinpoints individuals and handles large-scale processing, aided by a specialised search patent using a pyramidal algorithmic approach, alongside leveraging machine learning techniques, including transformers, CNNs, and LSTMs. The foundational model, Vision Transformer (VIT), is optimised for real-time learning and adapted to handle a large number of diverse camera streams.

The technology excels in bimodal data analysis, combining text and visual info for comprehensive understanding. It supports audio-visual integration for enhanced insights and interactions. “When you talk about the textual model forensic analysis then, of course, you are training on bimodal data so you are training with the text and images. For instance, if I’m searching for a green shirt and white t-shirt that means that need to be trained with the textual data and the image as well.”

In essence, the proprietary technology incorporates a sophisticated streamer algorithm and IP portfolio backed by advanced models. Customising the vision transformer ensures accurate, diverse, and real-time data processing, enabling efficient camera data management and actionable insights for better decision-making.