Trellis is an AI-driven platform that transforms unstructured data into SQL-compliant tables using schemas defined in natural language. Trellis enables organizations to query data from complex sources—such as financial documents, contracts, and emails—directly with SQL, making previously inaccessible information actionable and structured.
Their platform is designed to bridge the gap between unstructured data and traditional analytics workflows. By leveraging advanced AI models, Trellis ensures accurate schema generation and reliable extraction results, allowing business teams to:
- Unlock hidden revenue streams by extracting key features from transaction data (e.g., underwriting teams building better risk models)
- Enhance retrieval-augmented generation (RAG) applications, enabling users to ask analytical questions not possible with traditional vector databases
- Enrich data warehouses by retrieving business-critical information, such as competitor pricing and product details from external documents
Trellis stands out by offering natural language schema definition and guaranteeing accurate, structured outputs from varied and complex data sources, reducing the technical barrier for teams needing to operationalize unstructured information.
What Technology Enables Trellis?
Trellis employs proprietary AI models specialized in data extraction and schema mapping. Users define the desired schema in plain language, and Trellis’s engine reliably parses and restructures unstructured content—ranging from emails to PDFs—into SQL-ready tables. This approach eliminates the need for extensive manual labeling or custom coding, expediting the data onboarding process for analytics and business intelligence.
Who Uses Trellis?
Trellis primarily serves enterprise customers and data-driven teams within sectors such as finance, insurance, and e-commerce. Typical users include underwriting teams, analytics professionals, and product managers who need to extract granular insights from large volumes of documents, communications, or third-party data sources to inform business decisions and automate workflows.
Who Are Trellis's Competitors?
Trellis operates within the AI data extraction and unstructured data processing space. Notable competitors and alternative solutions include:
- Unstructured: Focuses on transforming data into formats suitable for large language models and AI systems.
- MindsDB: Enables detail extraction from text using SQL commands for rapid data mining.
- LlamaIndex: Offers LlamaExtract, a managed service for structured extraction from unstructured documents.
- Shinydocs: Provides enterprise AI solutions for unstructured data discovery and extraction.
- NLTK, Spacy, Stanford CoreNLP: Widely used open-source toolkits for text mining and information extraction.
- Snowflake: Offers document AI for extracting insights from unstructured reports within the Snowflake Data Cloud.
These platforms vary in their focus, from developer toolkits for natural language processing to end-to-end enterprise extraction solutions. Trellis differentiates itself through its natural language schema definition and focus on SQL-ready outputs for business teams.
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