CollectivIQ launches to aggregate AI model responses

Boston-based CollectivIQ has launched a platform that queries multiple large language models (LLMs) simultaneously to produce fused, more accurate answers. The tool aims to solve enterprise issues with AI hallucinations—defined as inaccurate or fabricated information generated by AI. The platform integrates with providers including OpenAI, Anthropic, Google, and xAI, querying up to 10 other models at once to find overlapping and differing information. By comparing responses across multiple systems, the tool is designed to deliver a single, more reliable answer than any individual model could produce on its own.

The startup was incubated at Buyers Edge Platform, a hospitality procurement enterprise. The tool was born from concerns raised by John Davie, the founder and CEO of Buyers Edge Platform and CollectivIQ, regarding data privacy and the risk of company information being used to train public AI models. “We had a bit of a wake-up call about a year ago when we learned that if our employees are just using any various AI tools, or even their own license, it could be training on our company information,” said Davie. He noted that such practices meant the company could essentially be aiding its competitors. While CollectivIQ encrypts prompt data, the company notes that it does not delete all prompt data, a distinction important for enterprise compliance.

The software was initially rolled out internally to employees at the beginning of 2026. After seeing a strong internal response and realizing that many of Buyers Edge Platform’s customers faced similar hesitation around adopting AI tools, the company decided to release the platform to the public. CollectivIQ operates on a usage-based pricing model where the startup pays for token costs and customers pay based on their usage. Davie hopes this approach will appeal to enterprises wary of long-term, restrictive contracts, offering an alternative to committing to a single provider. Davie, who has been building companies for nearly 28 years, plans to seek outside capital for CollectivIQ later this year.

Why it matters

CollectivIQ addresses enterprise-grade anxiety around AI reliability and data privacy by moving away from single-model dependency, offering a ‘fused’ approach that avoids vendor lock-in.