GyanAI Introduces Hallucination-Free AI Model for Enterprises

May 6, 2025
GyanAI has launched a new AI model designed to eliminate hallucinations, ensuring reliability and data privacy for enterprises, as announced in a press release.

GyanAI has introduced a new AI model that promises to eliminate hallucinations, a common issue in AI systems, particularly in regulated industries such as healthcare, finance, and legal sectors. This announcement was made in a press release, highlighting the model's neuro-symbolic architecture, which differs from traditional transformer-based models.

The model is designed to provide enterprises with a reliable and transparent AI solution, ensuring that every inference is traceable and verifiable. This is particularly crucial in industries where accuracy and data privacy are paramount. GyanAI's model has demonstrated state-of-the-art performance in life sciences benchmarks like PubMedQA and MMLU, proving its efficacy.

GyanAI's approach allows businesses to maintain full control over their data, ensuring privacy and security. The model's energy efficiency is also a significant advantage, requiring significantly less compute power compared to current large language models. This makes it a suitable choice for enterprises with zero tolerance for errors and privacy risks.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief, AI Policy Brief or Daily AI Brief.

Also, consider following us on social media:

Enterprise AI AI Safety & Regulation AI Brief AI Brief (X)
Free newsletter

AI Policy Brief

Weekly report on AI regulations, safety standards, government policies, and compliance requirements worldwide.

Guide
AI Legal Assistants

AI Legal Assistants

Research, drafting, review and knowledge tools for corporate legal teams and transactional firms

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.