Why do you need
an Entity Search Engine?

Reliable, fresh, and granular data on entities, both businesses and individuals, is critical for agile operations in financial institutions. From identifying and qualifying potential customers, to verifying and onboarding them, and monitoring changes in their risk profiles, high quality data is the essence of streamlined and optimized operations.

Insights from our survey of more than 100 risk officers and operational specialists show that financial institutions face two primary challenges with entity data in their operations: inaccuracy and high costs. Inaccurate data leads to elevated risk and compliance issues, while high costs limit the automation of high-volume use cases, such as lead screening and portfolio monitoring. Financial institutions often need to navigate multiple vendors to gain comprehensive insights, increasing setup and maintenance costs due to vendor fragmentation.

Founded by seasoned fintech entrepreneurs and data enthusiasts, we built Livesight from the ground up to address these challenges. Think of it as Google for entity search—a search engine that matches any entity with high precision and is optimized to uncover hidden and nuanced relationships between entities. Our engine is continually fed by a diverse mix of publicly available data on business entities, enriched with proprietary data on consumers, income, employment, and fraud through a peer-shared consortium.


One-stop entity insights for streamlined operations


Accurate matching powered by an AI-native entity resolution engine


Precision insights with top-of-funnel cost-efficiency

Operational Impact

Without Livesight

With Livesight

Manual and inefficient processes
Automated processes for timely decision-making
Higher risk due to partial and innacurate data
One-stop solution for precision-critical use-cases
Cost-prohibitive for high-volume use-cases
Cost-effective solutions for top-of-funnel use-cases

How Averisana Boosted Ad Spend ROI by 25% with Livesight Data Solutions

Averisana aimed to predict a particular type of outcome within insurance distribution that partly relied on some of their domain specific attributes, but had elements of other income or financial economic related attributes that they really didn't have any good way to acquire.
Clients Feedback
"Livesight has unique income-related data elements that, in conjunction with our domain-specific elements, were used to develop a first-of-its-kind predictive model in insurance distribution. The Livesight team worked collaboratively with us throughout the model development and testing process; and crafted ingenious and flexible commercial terms geared towards growth and customer value. We rely on Livesight and their data science and product team’s enormous knowledge and resourcefulness in all our new use cases as they surface."
Sudhama Gopalan
Chief Executive Officer

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