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with Nischal Harohalli Padmanabha,
Scoutbee’s Vice President of Data Engineering and Data Science
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Scoutbee drives better business outcomes by giving companies the actionable insights they need to perfect the supply base and advance strategic initiatives, such as risk management, ESG and innovation. The Scoutbee Intelligence Platform (SIP) uses graph technology and predictive and prescriptive analytics to deliver holistic supplier visibility that helps procurement professionals make confident supplier decisions, drive cross-functional efficiency, and optimize their existing technology investments. Scoutbee’s AI-powered data foundation connects teams to any data point – internal, external, third-party, and more – and any data combination necessary to orchestrate a resilient, competitive, and sustainable supply base.
In order to deliver these solutions, Scoutbee uses large-scale information extraction using deep learning models on unstructured data found on the web. They then use machine learning to rank and score each item for relevance to make it easy and accurate to search for supplier information across their knowledge graph.
Scoutbee has a deep understanding of the personas in the supply chain industry and how they interact with data associated with the supply chain.
That knowledge presented Scoutbee with a major opportunity to innovate and deliver significant value to customers at scale. The big idea? Using domain-specific proprietary machine learning models to automate supply chain information collection, cleaning and enhancement (a task that would normally take a customer hundreds of hours to do by themselves), and then make that information easily available and searchable for Scoutbee customers.
However, in order to train their models, Scoutbee needed to create very specific labeled datasets using their own proprietary data to ensure that they would have a strategic advantage over other publicly available search engines. And they needed to do so in a cost-effective manner.
To develop targeted, highly-accurate datasets, Nischal Harohalli Padmanabha, Scoutbee’s Vice President of Data Engineering and Data Science, needed a flexible labeling platform that could do rich HTML annotation, along with the ability to support active learning and large scale inferences. He was particularly interested in finding a platform to support human-in-the-loop reviews to ensure model quality and Service-Level Agreements (SLAs).
Discussing their criteria for a labeling platform, Nischal states “As part of choosing a technology partner at Scoutbee, we always evaluate several options, so that we can make a strategic decision. Our first thought process is to always look into the open source world to see if there are tools - we could host and contribute to - that we can work with.” This led him to take a look at Label Studio, the most popular open source labeling platform.
After evaluating both Label Studio Community and Enterprise editions, Nischal determined that Label Studio Enterprise was the right solution for Scoutbee.
Scoutbee has seen significant success with their ML-driven products while using Label Studio Enterprise to both train large-scale models and provide adjustments to their models currently in production. This includes significant results like:
Speaking about his experience with Label Studio, Nischal says “We consider each cost as an investment, and getting a good return on investment is very important. Label Studio has definitely had a positive impact on our return on investment, thereby making it a viable option for Scoutbee.”
“We consider each cost as an investment, and getting a good return on investment is very important. Label Studio has definitely had a positive impact on our return on investment, thereby making it a viable option for Scoutbee.”
Using Label Studio in conjunction with their proprietary data to build their knowledge graphs along with all the ontology work that they have put in place, Scoutbee is in a great position to reap the benefits of working with large language models (LLMs). At present, the Scoutbee data team is putting in significant efforts to cater to their customers' requirements in this area. Their primary focus is to empower customers with the capability to intuitively communicate with supply chain data by using natural language. This will open up new opportunities to enhance the product user experience, as well as new use cases and capabilities for their customers.
Like everyone who is working in data science and tech at the moment, Nischal is excited about the future. “We also have plans to support not only text-based but also image-based information extraction in the future to further enhance our knowledge graph. AI at Scoutbee is quite exciting at the moment.”
