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Goal
Unlocking the Full Potential of LLMs with Your Data
Fragmented and Inconsistent Data Sources
Applications often need to integrate data from various sources and extract only the relevant portions. Enterprises are generally resistant to changing how they store data, making it difficult to align these fragmented and inconsistent data systems with LLM requirements.
Garbage in, garbage out
The quality of data to LLM directly impacts the performance of LLMs. Low-quality, unstructured, or poorly labeled data can lead to inaccurate, irrelevant, or even harmful model outputs.
Data Exploration: A Specialized Expertise
Data exploration often becomes a specialized skill held by certain data scientists and programmers. This is because it requires a unique blend of data expertise and coding proficiency. Individuals with this combined skillset are often rare, leading to data exploration becoming the domain of a select few.
Solution
Table Augumented Generation + Autonomous Code Agent
Handle Multiple Data Sources
From your local csv/excel/parquet files to mysql/postgres/sqlserver databases and cloud data warehouses such as Snowflake
Enrich your data with LLM Friendly Metadata
Auto generate metadata for your data, for example, column names, data types, possible values, column descriptions, value distributions, etc.
Precisely track how LLM uses your data
Due to unpredictable nature of LLMs, observability is key to understanding how they are using your data, we build agent observability into pipelines, and track how LLMs are using your data
'Burn After Using' Apps
Write, debug, and run code, allowing user to build apps on the fly, instead of relying on the static, pre-built apps of the past. This opens up a whole new world of possibilities for dynamic, user-centric applications