Shuveb Hussain on Bridging Unstructured and Structured Data with AI-Powered ETL.
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Shuveb Hussain is co-founder of Unstract, a no-code platform that uses large language models to extract structured data from unstructured documents, allowing users to build API endpoints and ETL pipelines to automate document processing workflows. Unstract allows users to build ETL pipelines and APIs to process documents like forms, contracts, and financial statements, outputting the extracted information as JSON. Key features include OCR optimization, prompt engineering capabilities, and the use of multiple LLMs to improve accuracy, with applications in industries like insurance, finance, and healthcare.
Interview highlights – key sections from the video version:
- Inspiration for Unstract
- Emerging Role of Prompt Engineers
- Reimagining Data Engineering for Unstructured Data
- Challenges with Unstructured Data
- Ensuring Accuracy with LLMEval
- Balancing Cost and Performance
- Development and Testing Phases
- Prompt Engineering and Domain Expertise
- Integration with Existing Tools
- Open Source Features and Limitations
- Handling Diverse Document Formats
- Fine-tuning and Customization
- Popular Use Cases
- Future Developments in Multimodality
- Usability of Open Source Version
Related content:
- A video version of this conversation is available on our YouTube channel.
- Is Your Data Strategy Ready for Generative AI?
- Generative AI: Navigating the Challenges of Enterprise Adoption
- LLM Routers Unpacked
- Brian Raymond → ETL for LLMs
- Jerry Liu → An Open Source Data Framework for LLMs
- Joao Moura → Unleashing the Power of AI Agents
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