Trellis launches: Make your unstructured data SQL ready. Try it live w/ your data⚡️

Trellis recently launched!

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^{"LLM-powered Snowflake for unstructured data."}

_{Trellis extracts and transforms your unstructured data to SQL-compliant tables with schema you define with natural language. With Trellis, you can run SQL queries on your unstructured data sources like financial documents, contracts, customers, emails, etc.
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Founded by Mac Klinkachorn & Jacky Lin
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✅ Try out the product live at https://demo.usetrellis.co

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❌ Problem — Modern data warehouses are not designed for unstructured data
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• For complex business and aggregation queries (i.e. what are the most common customer complaints or how do transaction categories change over time? ), RAG and traditional search techniques fall short.

• 80% of enterprise data is unstructured and arrives in many different formats. This makes it hard for applications and business decisions to be built reliably on top of that data.

• Building dedicated ML pipelines to extract features from unstructured data and performing inferences are time-consuming and hard to maintain. Teams are bogged down by handling edge cases.

🎉 Solution
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Trellis' AI engine combines LLMs, multimodal models, and database query engines to guarantee correct schema and accurate results across unstructured data sources.
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• Define your transformation and tasks with natural language. Trellis takes care of the rest.

• First-class support for PDF, HTML, and images with table transformation, context-aware chunking, and data normalization. Trellis robustly finds and aggregates all the data in situations where the smallest details matter.

• Real-time compute and feature store for all your analytics needs
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Trellis in-action: Processing hundreds of pages for all common health insurance plans) ⤵️

🏃🏽‍♂️ Leading Enterprises Use Trellis to:
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1. Supercharge RAG Applications: Enrich RAG pipelines and data warehouse by including business-critical information from unstructured data.
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2. Unlock Hidden Insights from Customer Calls and Emails: Process and query Terabytes of calls and emails to identify revenue opportunities, compliance risks, and user behavior over time
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3. Automate Contract Reviews and Underwriting: Extract key provisions from thousands of contracts, experiment with new features from financial data sources, and build better ML models from the newly transformed structured

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Learn More

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_{🌐 Visit www.usetrellis.co to learn more}

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⭐️ Try out the live demo and join the waitlist for API access here.}

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‍❓ Know someone working with a high volume of unstructured data (PDFs, calls, Log files)? Make the intro!}

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🗓️ Have a use case in mind? Do a deep dive with the team here}

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👥 Follow Trellis on LinkedIn and X}