About CogniSwitch

The reason we built CogniSwitch is quite simple, we want to fix reliability issues enterprises and businesses are facing with the adoption of GenAI and the use of LLMs in their daily operations. Our aim is to rid GenAI projects of hallucinations and bring enterprises closer to achieving 100% accuracy.

But to address the accuracy concerns, we needed to get to the root of the problem. Having played around with LLMs before the hype of ChatGPT and the mainstream use of LLMs the problem was evident to us. It’s almost impossible to expect accuracy out of LLMs despite the guardrails and training, purely due to their probabilistic nature. Which leads us to the underlying problem.

It isn’t the technology or the AI, they’re great for their own unique applications. At being creative, which is purely a byproduct of how they’re built. They’re great at generating summaries, extracting entities and manipulating text through prompts but sadly they lack the understanding to give enterprises the level of accuracy they seek.

Put simply, the problem lies in the data we use to train or guardrail the LLMs. This single dimensional format of data is inadequate for consumption by the LLMs and is why the LLM falls back on its probabilistic tendencies to fetch an answer based on what it thinks matches the query, contrary to understanding it.

CogniSwitch is an API based data platform that enhances enterprise data by extracting entities, concepts and their relationships thereby converting this data into a multidimensional format and storing it in a database that can accommodate these enhancements. In our case the data is stored in a knowledge graph. This enhanced data is now ready for consumption by LLMs and other GenAI applications ensuring the data is consumable and context can be maintained. Thereby eliminating hallucinations and delivering accuracy.