Trellis AI

Trellis AI is widely used for structuring and streamlining complex datasets. It can convert unstructured data into SQL-ready formats, significantly benefiting data and operation teams. Its core features bring great value to end users - namely, summarizing company descriptions, extracting provider names, classifying support queries, analyzing customer sentiments, summarizing customer requests, and classifying loan applications. Being able to support various data formats and direct integration wit

Trellis AI uses its advanced artificial intelligence engine to convert unstructured data into structured, SQL-ready formats. It takes complex forms of information from various sources, processes them, and then transforms them into a format that can be readily used by SQL. The transformation is achieved through its skills to extract, classify or summarize assets. This AI-powered transformation eliminates the need for custom data pipelines, allowing teams to define their schema in natural language

Trellis AI supports multiple data formats. These include PDF, HTML, images, and audio files. Its state-of-the-art parsing engine combines with a finely tuned vision AI model to effectively understand and process data in these many formats.

Trellis AI features robust validation and monitoring mechanisms. These mechanisms improve the quality of data by quickly building custom logic to validate outputs. Any outliers and anomalies in the data are identified and flagged, allowing teams to rectify these discrepancies before the data is fed into production. This helps maintain the accuracy and reliability of the data.

Trellis AI works with a wide range of unstructured data. This includes but is not limited to information from financial documents, emails, and voice calls. Given its intelligent AI engine, it can convert this complex and unstructured data into SQL-ready formats, streamlining the data for easier processing and analysis.

Yes, Trellis AI can extract names from data sources. This extends to various categories such as extracting insurance provider names and customer first names from provided content.

Trellis AI classifies support queries intelligently using varied parameters. This can range from categorizing queries into buckets like pricing or customer service, improving how teams can respond to and manage these queries. The actual classification processes and criteria are determined by the custom logic developed, based on the unique needs and requirements of each use case.

The analysis of customer sentiments by Trellis AI is driven by the advanced machine learning algorithms it employs. These algorithms interpret and analyze the content of customer messages to determine sentiments. The specifics of this feature are determined by the specific needs of each application and use case. However, at its core, this functionality allows companies to interpret the emotional tone of their customers' communications, allowing them to respond in more informed and empathetic way

Trellis AI processes a vast array of complex documents. Specifically, it is used to process complex documents such as bonds, credit ratings, and underwriting documents in the financial services industries.

In Trellis AI, teams define their schema using natural language. This is part of the platform's AI-powered transformation process. Once the schema is defined in natural language, Trellis AI takes care of the rest. It processes and structures the data based on this schema, eliminating the need for manual data workflows or the construction of custom data pipelines.