Business Review

Language AI

Natural Language Processing (NLP) and Large Language Models (LLMs) are revolutionizing the way businesses operate by providing powerful tools to analyze and understand human language. By leveraging GPT-4 and other similar technologies, we build enterprise virtual analysts.

Intelligent Tagging & Intelligent Agent

Building applications with state-of-the-art Language AI

At Effixis, we support companies to leverage AI Language Models. We have developed our proprietary technologies Intelligent Tagging, and Intelligent Agent, NLP/LLM platforms enabling to swiftly and robust train and deploy custom NLP/LLM models to tackle your most advanced challenges.

Dashboards, as we know them, have been a staple of data visualization and analysis for years. They allow organizations to access important data, make decisions based on that data, and track progress over time. However, with the advent of Large Language models (LLM), it’s possible that dashboards, as we know them, will soon become a thing of the past.

Imagine if you could build a ChatGPT-like platform, while respecting all your internal compliance rules, to interact and conversationally inspect external data, as well as your company data – asking for a KPI, a plot, or qualitative analysis without any programming! Is that too good to be true?

By applying chains of Large Language Models (LLMs) on external data, together with your internal data, we build prompt interfaces that:

  • Plot data you have asked, for a seamless visualization and interpretation.
  •  Query database extracts, so you can further analyze and get advanced isnights.
  • Reply in natural language, so you can have a conversation with your database.

A combination of a reduced number of dashboards, together with a prompt interface can be setup for, bringing you loads of benefits

Data accessibility

User engagement

Agility & reactivity

Data engineering workload

Number of applications

Agility & reactivity

Our platform and proprietary technologies, Intelligent Tagging and Intelligent Agent enable a reliable and cost-efficient integration of LLMs

Another advantage of this approach is empowering data analysis on top of data retrieval. Indeed, it is now possible for individuals with no programming knowledge to perform exploratory data analysis and statistical modelling. For example, instead of writing code to perform a regression analysis, a user can ask the language model: “Can you perform a linear regression on my data and show me the results?” The language model then generates the appropriate code and formats the output for the user, giving them more time to study the results and extract insights. For people with some programming skills, such an approach would remain interpretable and useful as they could access and inspect the code and further modify it if need be.

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Intelligent Tagging & Intelligent Agent

Data Privacy

A question you may have is regarding data privacy – When working with publicly available data, there is no problem. However, when working with sensitive internal data, should I be sharing my data with LLMs, such as those provided by OpenAI? This is an important question to consider if you work in a data-sensitive field. There are two ways to address this problem.

  1. Isolation: Separate your database contents from the LLM itself and only share your database metadata (e.g., table and column names). This is what we have done for our proof-of-concept demonstration, except for the final step, where the LLM formulates the output in natural language. If data privacy concerns you, the final step can just be skipped, and the query results returned directly.
  2. Self-hosted LLM: Use an open-source LLM (e.g. Google’s FLAN-T5) hosted on your secure premises. This solution allows you to use the LLM to its full potential without worrying about data privacy issues, as the model will be hosted entirely on-site.

We develop either solution – or any alternative – to suit your specific needs!

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Use cases

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