Left Field Labs has launched LFL Morse Intelligence, a product that examines how major artificial intelligence models describe brands.
The system analyses responses from models including ChatGPT, Gemini, Claude and Perplexity to show how a brand is perceived when users ask for recommendations or comparisons. It is designed to identify gaps between a company's intended positioning and the descriptions generated by AI systems.
According to Left Field Labs, the tool produces a brand perception score, a model-by-model gap analysis, summaries of how each model tells a brand's story, and an executive summary of key findings. Each analysis is carried out on a client's behalf rather than delivered as an automated self-service report.
The launch reflects growing interest among marketing and technology firms in how large language models influence discovery and decision-making. As consumers increasingly use AI assistants to research products and services, agencies and brands are examining how those systems rank, compare and characterise companies.
Model differences
Left Field Labs said the product emerged from internal work comparing how different AI systems interpreted the same brand. It found larger-than-expected differences in positioning, recommendations, competitive framing and perceived strengths across multiple brands, sectors and models.
That led the agency to build a tool to make those variations visible to clients. It argues that AI systems form their own picture of a brand by drawing on a broad range of signals across the internet rather than simply repeating a company's messaging.
That means websites sit alongside sources such as reviews, online forums, creator material, structured data, technical documentation and media coverage in shaping what a model says about a business. For brands, the risk is that those sources can produce a version of the company that differs from the one marketing teams intend to present.
Rich Foster, Senior Vice President, Creative, at Left Field Labs, set out the company's view.
"The most important conversation about your brand is increasingly happening between your customer and an AI model. If AI misunderstands your company, flattens what makes you different, or recommends a competitor instead, that's a business problem. We help brands understand how AI perceives them, where that perception diverges from their intended positioning, and what they can do to strengthen it across their user digital experiences to fix it," said Rich Foster, Senior Vice President, Creative, at Left Field Labs.
New metric
Left Field Labs frames the issue around what it calls "share of model" - whether a brand appears among the limited set of recommendations often returned by AI assistants. When a user asks an AI system for a recommendation, the answer will typically include only a small number of brands, it said.
That could make inclusion in AI-generated answers more important for visibility, especially in categories where customers use conversational tools before visiting a company website. It also creates another layer of competition, as brands seek not only search ranking or social attention but also favourable treatment in AI-generated summaries.
The agency said LFL Morse Intelligence asks questions in the way a customer might and records the responses it receives. It then compares those answers across models to show where they align and where they differ.
Left Field Labs said the analysis feeds into a broader client process that begins with what it calls a vision sprint. It uses that as an initial step in developing digital products, platforms and experiences for clients responding to the spread of large language models.
Agency backdrop
Left Field Labs describes itself as a creative technology agency and lists Google, Salesforce, Qualcomm and Meta among the companies it has worked with. The LFL Morse Intelligence launch places it among firms trying to turn shifts in AI behaviour into advisory and strategy work for brands.
The business case rests on the idea that AI systems are becoming intermediaries between companies and their customers. If those systems summarise a brand inaccurately, compare it with the wrong rivals or fail to recommend it, that may affect customer choice before a buyer reaches the brand's own channels.
For agencies, that creates a new area of research and consulting tied to machine-mediated discovery. For brands, it raises the question of whether longstanding measures such as market research, sentiment analysis and customer feedback are enough when AI systems are increasingly shaping what people hear first.