Cooper Launches Free Index Tracking B2B Software Recommendations Across Four AI Platforms

Cooper has introduced a free B2B software recommendation index that tracks how ChatGPT, Google Gemini, Anthropic Claude, and Perplexity recommend business software across 80 categories. The platform, available at CooperAIQ.com, publishes weekly product rankings, AIQ scores, cited-source evidence, and historical recommendation data, offering software buyers and vendors a way to monitor how AI assistants influence software discovery.

As businesses increasingly use generative AI tools to research technology purchases, software vendors face a new visibility challenge: understanding which products AI assistants recommend when potential customers ask for buying advice.

Traditional software discovery relies heavily on search engine rankings, review platforms, analyst reports, and vendor websites. AI-powered assistants introduce another discovery channel, where recommendations are generated through responses that may draw on multiple sources and change between queries or platforms.

Cooper is entering this emerging market with a free index designed to track those recommendations. Its platform aggregates responses from ChatGPT, Google Gemini, Claude, and Perplexity across 80 business software categories, publishing weekly rankings that buyers and software companies can examine without creating an account or purchasing a subscription.

The index covers eight broad technology areas: Sales, Marketing, Support, HR, Finance, Productivity, IT and Security, and Engineering. Individual categories include products such as CRM, accounting, and project management software.

Rather than producing a single overall leaderboard, Cooper separates recommendations into four rankings: Best, Most Affordable, Easiest to Set Up, and Rising. Each ranking reflects responses to different software-buying questions, allowing users to examine how AI assistants recommend products based on specific purchasing priorities.

The platform also provides AIQ scores, individual assistant rankings, cited-source evidence, and eight weeks of completed historical product data. Dated archives allow users to compare previous editions and identify changes in product visibility over time.

For software buyers, the index offers a consolidated starting point for examining which platforms appear in AI-generated recommendations. Someone evaluating CRM software, for example, can compare recommendations across four assistants and inspect the sources cited in their responses before conducting further product research.

The potential value for software vendors is different. Marketing teams and founders can use the rankings to monitor how their products appear against competitors in AI-generated buying conversations. This could provide an additional perspective on brand visibility beyond conventional search engine optimization and software review websites.

Cooper says its rankings are calculated using observed AI recommendations and a published methodology. It also states that advertising and vendor product claims do not influence rankings, scores, or source evidence. All listed products receive the same public access, regardless of whether their vendors claim profiles or advertise on the platform.

That separation is relevant in a market where software discovery platforms often combine editorial listings, sponsored placements, vendor subscriptions, and commercial lead-generation models. A publicly accessible index based on observed AI responses offers a different approach, although its usefulness will ultimately depend on the transparency and consistency of its measurement methodology.

There are also limitations to consider. AI-generated recommendations can vary according to prompt wording, model updates, available sources, and other contextual factors. A product's position in one weekly edition does not necessarily represent its overall market standing or suitability for a particular enterprise.

Cooper's index should therefore be understood as a record of observed AI recommendations rather than an independent assessment of software quality.

The launch reflects a developing category of AI search visibility monitoring, often associated with Generative Engine Optimization (GEO). As enterprises increasingly consult AI assistants during technology research, tracking how products appear in these conversations could become another component of software marketing intelligence.

Market Landscape

Cooper enters an emerging segment of marketing technology focused on monitoring brand visibility across generative AI platforms. The category is gaining relevance as businesses explore how ChatGPT, Gemini, Claude, and Perplexity influence product research and purchasing decisions.

Its approach differs from conventional software comparison platforms such as G2 and Capterra. These services primarily organize software discovery around customer reviews, product information, ratings, and comparison tools. Cooper instead focuses on recording recommendations generated by AI assistants.

It also enters a developing competitive landscape that includes AI visibility monitoring and Generative Engine Optimization tools. These platforms generally help businesses understand how their brands appear in AI-generated responses, identify visibility gaps, and monitor changes in AI search presence.

Cooper's differentiation is its public-access model. By making rankings, historical records, and cited-source evidence available without subscriptions, the platform targets both software buyers and vendors that want to understand AI recommendation patterns.

However, AI recommendations are not equivalent to product quality assessments. Rankings can change with model updates, prompts, and source availability. Buyers should use the index alongside product demonstrations, independent reviews, security assessments, and pricing comparisons.

Strategic Outlook

Cooper's launch reflects a potential shift in B2B software marketing measurement: from tracking where products rank in conventional search results to understanding how AI assistants recommend them during purchasing research.

Three developments will influence this category:

• AI recommendation monitoring: Software companies may increasingly track how frequently their products appear in AI-generated buying responses.

• GEO measurement: Marketing teams could incorporate AI recommendation visibility into broader SEO, brand monitoring, and demand generation strategies.

• Recommendation transparency: Historical rankings and cited-source evidence could help vendors identify changes in how AI systems describe and recommend their products.

For Cooper, the long-term challenge will be maintaining consistent measurement across four different AI systems. Its published methodology, repeatable prompts, source transparency, and historical data will be important factors in establishing credibility.

Top Insights

• Cooper introduces a free index tracking B2B software recommendations generated by ChatGPT, Gemini, Claude, and Perplexity across 80 categories.

• Four ranking types separate recommendations by product quality, affordability, ease of implementation, and rising visibility, supporting different software purchasing priorities.

• Historical rankings and cited-source evidence give software vendors a way to monitor changes in AI-generated product recommendations without purchasing subscriptions.

• The platform introduces another measurement approach for GEO strategies as B2B software companies explore visibility across generative AI discovery channels.

• Cooper's rankings measure observed AI recommendations rather than independently verified product quality, making additional research necessary before enterprise purchasing decisions.

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