Author name: Arpit Agrawal

Unlocking RAG’s Full Potential: Integrating Text and Non-Textual Information

Insights

Are you missing crucial insights because your RAG can’t read a chart or understand a table?

Text-based RAG solutions, limited to text analysis, leave a wealth of information untapped in your visual and audio data. Elastiq Discover bridges this gap, offering a holistic view of your entire data ecosystem.

From decoding complex graphs to extracting insights from video content, it empowers comprehensive multimodal analysis

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AI for Legal Contracts Management – Beyond Traditional and Out-of-the-Box RAG

Case Study

Struggling to extract valuable insights from your legal contracts?
A leading global law firm faced the challenge of efficiently extracting valuable insights from a vast repository of legal documents. After trying their luck and failing with Out-of-the-box (OOTB) RAG, they realized that what they wanted couldn’t be done by traditional RAG, even after trying all the hacks!
How did they overcome this challenge?
By leveraging Elastiq Discover’s advanced RAG platform, they were able to:
Accelerate research
Enhance contract analysis
Streamline document retrieval

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Breaking the RAG Barrier: Generate Responses that require Retrieval of 1000s of Chunks

Insights

the “RAG trap” – the limitations of traditional RAG systems – can pose significant challenges for enterprises that rely on document analysis. We’ve seen how the Top-k approach can limit accuracy and completeness. Elastiq Discover’s Knowledge Layer offers a more comprehensive solution, enabling in-depth analysis and insights from vast datasets. By adopting Elastiq Discover, organizations can unlock the full potential of their document data. Contact us to know more or schedule a demo with us.

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Measuring Social Media Sentiment at 5 Bn+ Message/Day Scale

Case Study

With billions of social media posts generated daily, identifying trends, understanding sentiment, and connecting the dots can be extremely difficult.
We helped a leading social media insights company overcome these challenges by developing a scalable platform that can:
Process billions of messages daily
Identify and connect users across platforms
Analyze sentiment and extract key insights
By leveraging advanced machine learning and data engineering techniques, we were able to provide valuable insights into customer sentiment and content performance. We also developed powerful audience segmentation models to predict content preferences.

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Machine Learning with SQL

Insights

Machine Learning with SQL Today we’re going to look at how to create a Machine Learning model. Not with Python or Tensorflow or Spark, but we’re going to create a Machine Learning model using SQL. What is Machine Learning? Machine Learning (ML), a subset of Artificial Intelligence (AI), is the study of computer algorithms that

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