The Problem with Single-Model Enterprise AI

Enterprises adopted large language models fast. Many are now realizing that speed came with trade-offs: hallucinations showing up in client decks, biased outputs slipping through reviews, and sensitive company data potentially leaking into model training pipelines.

John Davie, founder and CEO of Buyers Edge Platform, a hospitality procurement company, ran into all of these problems firsthand. When his employees started experimenting with consumer AI tools, leadership discovered a security gap: employee usage of personal AI licenses risked exposing proprietary company data to model training processes.

Locking down to a single enterprise LLM contract meant high costs, long commitments, and outputs that still weren't reliable. Employees reported that AI-generated content, sometimes containing outright fabrications, was making its way into presentations shared with clients and partners.

What CollectivIQ Built

Davie's response was to build a separate company around the fix. CollectivIQ, a Boston-based spinout incubated at Buyers Edge Platform, takes a different approach to enterprise AI.

Instead of routing queries to a single model, the platform sends each prompt to multiple LLMs simultaneously, including models from OpenAI, Anthropic, Google, and xAI, among others. It then analyzes where the responses overlap and where they diverge, producing a fused answer designed to reduce the inaccuracies that plague single-model outputs.

The product queries up to 14 models at once and encrypts all prompt data.

How the Business Model Works

CollectivIQ covers the underlying token costs through enterprise API agreements with each model provider. Customers pay based on usage rather than committing to fixed contracts.

This is a deliberate contrast to the subscription and commitment structures common across enterprise AI vendors. Davie told TechCrunch he sees the pay-for-what-you-use approach as a way to stand out in a crowded market.

The company rolled the tool out internally at the start of 2026. After learning that Buyers Edge Platform's own customers faced the same uncertainty about which AI tools to adopt, Davie decided to release CollectivIQ publicly. He has self-funded the company so far and plans to raise outside capital later this year.

Why Multi-Model Aggregation Matters

No single LLM excels at everything. Models differ in their training data, reasoning strengths, and failure modes. Querying several at once and cross-referencing outputs is a logical step toward reducing the risk of any one model's blind spots contaminating business decisions.

For mid-market companies that lack the budget for six-figure annual AI contracts but still need reliable outputs, a usage-based aggregation platform lowers the barrier to entry significantly.

Open Questions

Fusing answers from models with different architectures and training sets is nontrivial. The quality of the fusion layer, how conflicts between models get resolved, and how confidence gets scored, will determine whether CollectivIQ's outputs are meaningfully better than picking the best single model for a given task.

Enterprise adoption of AI tools stalled in many organizations specifically because of data governance concerns. A platform that aggregates across providers needs to demonstrate airtight data isolation at every integration point.

The approach is sound in principle. Cross-referencing multiple models addresses a real and growing frustration among enterprise buyers who have been burned by single-model inaccuracies.

We'll be watching how CollectivIQ's fusion accuracy holds up as LLM capabilities shift and new models enter the market. Execution, particularly in the fusion logic and data handling layers, will determine whether multi-model aggregation becomes a standard enterprise pattern or remains a niche offering.