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Insurance

Enhance policy management, fraud detection, CX and more

Banking & Finance

Enhance risk management, regulatory compliance and data governance

Asset Management

Rapidly assess insights across an entire portfolio

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Search & Retrieval

Data discovery tools to retrieve all types of unstructured data

Data Standardization

Deliver your data in a structured format, for further enriching.

Relationships and Enriching

Enrich data with key rules, categories, ID’s needed by your business.

Data Migration

Optimized integration of data into existing intelligence platforms.

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How Gen AI and SLMs are transforming data governance

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Deep-dives into Gen AI and Data Governance

Get in touch

Meet the Founder: Neeve Parker of EmergeGen

In the world of artificial intelligence, it takes a unique blend of vision, resilience, and empathy to lead innovation. 
Case Study

EmergeGen and Cententia Partner to Revolutionize Document Processing

EmergeGen and Cententia - a provider of tailored enterprise automation solutions - have joined forces to tackle one of the biggest challenges in business today: automating document processing at scale. With EmergeGen's Data Central as the foundation, Centennial delivers cutting-edge solutions that simplify data chaos and enhance operational efficiency.
Blog

Data Central: Simplifying Data Chaos for Enterprise Data Governance

Companies are overwhelmed by a tidal wave of raw, unstructured data in today's financial, insurance, and asset management industries. The volume is vast and chaotic, ranging from customer interactions to transaction records and market analyses. 
Blog

From Data Overload to Data Goldmine: Utilizing AI for Smarter Business Decisions

In our last article, we explored the AI tech trio doing the heavy lifting of finding, organizing and governing the tsunami of unstructured data in our ever-more digitized world. ‍
Blog

AI Precision: Understanding the Power of SLMs in EmergeGen's Data Governance Revolution

As organizations grapple with an ever-growing influx of data, traditional data management methods often fall short.
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