We started our journey in Data analytics through Phrazor in 2015. Our vision was to empower businesses to make data driven decisions by converting their data to text insights which are easily readable and actionable for business users using our inhouse LLM engine.
The launch of ChatGPT was successful and it made non technical users understand the value of magic behind LLMs. After its successful launch we noticed an extraordinary surge in inquiries regarding the utilization of AI to extract insights directly from their datasets.
While the prospect of such an endeavor may seem thrilling and attainable, I'm here to assure you that it's a formidable challenge.
The primary reason for this challenge lies in the fact that ChatGPT, much like other Large Language Models (LLMs), is not designed for data analysis. Tools that generate output based on their own interpretations are akin to a double-edged sword, exhibiting inconsistency in output, accuracy, and audit-ability.
Phrazor provides the control needed to prevent AI Hallucination
What we observed with generative AI and LLMs is that they function as black boxes, much like our brains. However, our understanding of these models sometimes lags behind our comprehension of how our own brains operate. Discerning what gets triggered and suppressed among the millions of connections within these models is virtually impossible.
This can create a situation where you're left with only two options: either unquestioningly believe what the model analyses (which can change with slight alterations to the input data or updates to the language model's learned weights) or remain skeptical of every assumption it makes.
This inconsistency by GenAI while handling sensitive data makes it difficult for businesses to make decisions.
What do we mean by Consistency in AI?
If you were to input a dataset into popular LLMs on the market, such as ChatGPT, Claude, or Bard, and request a summary, you'd receive varying outputs. These models haven't evolved to analyze or mine data (and, in most cases, they can't due to context limitations), rendering them susceptible to interpretations that yield inconsistent results.
This inconsistency can lead to the omission, modification, or misrepresentation of information, posing a risk to the decisions based on these outputs.
How do Phrazor solve this problem?
Phrazor SDK acts as a middle layer between data and generative AI.
Our inhouse LLM model generates insights from the data as unstructured text. This unstructured text is then sent to Generative AI for users to customize based on persona, verbosity or any custom AI prompts.
Due to this layer, there is no loss of data due to self interpretation and generative AI only beautifies the unstructured text to structured text for final consumption.
Plus Phrazor SDK is developer friendly and easy to run!
Just install and import our python library, upload your data and define column metas, and simply summarize your custom insights.
Your support is important for us!
Phrazor SDK python library was specifically created for developers like you.