Bluefish Secures $20M to Help Fortune 500 Brands Win in the Age of AI Search

Bluefish Raises $20M Series A to Sharpen AI Marketing for Fortune 500 Brands

Bluefish, an AI marketing platform focused on how brands appear in AI-generated queries, secured $20 million in Series A funding. This brings their total funding to $24 million, with NEA and Salesforce Ventures leading the round.

Interestingly, the company shares its name with a common fish, which can complicate online searches. However, Bluefish is concentrating on generative engine optimization (GEO), a fresh approach distinct from traditional SEO methods.

A Focused Approach for Big Brands

Unlike many AI marketing platforms trying to cover all bases, Bluefish’s CEO Alex Sherman emphasizes a targeted strategy. The platform was built specifically to support Fortune 500 companies. Currently, 80% of Bluefish’s clients are major brands like Adidas and Tishman Speyer.

The new funding will expand Bluefish’s product offerings and grow its client-facing and engineering teams.

How Bluefish Helps Marketers

Bluefish supports marketers in three key areas:

• Tracking brand performance across AI systems such as ChatGPT
• Optimizing content by generating and rearranging it for better AI engagement
• Measuring the impact of those optimizations

The platform’s measurement tools evaluate how often and positively a brand appears in large language model (LLM) results and how well the brand’s content influences AI descriptions.

Sherman points out that simply producing a flood of content isn’t effective. Low-quality content may be easy to generate but won’t meet standards or serve brands well.

GEO vs. Traditional SEO

Traditional SEO focuses on credibility and relevance to keywords, affecting ranking on search result pages. LLMs, however, aim to generate long-form, qualitative descriptions in response to complex queries. This change means brands need to rethink their content strategy to appeal to AI models, not just humans or search engines.

Since websites were originally designed for human readers and traditional SEO, brands now face the challenge of optimizing for AI-driven content consumption.

The Role of Data and Content Volume

There’s no one-size-fits-all method to optimize for LLMs, but some patterns are consistent. For example, LLMs often source information from third-party sites like Reddit and blogs, which tend to have more extensive content than brand websites.

Brands are still learning how to get their content effectively “ingested” by AI systems. Bluefish acts as a guide, helping brands identify which platforms and content types to focus on.

For instance, simply appearing in AI query results isn’t enough. A brand must ensure enough supporting content exists online to answer potential follow-up questions from customers. If not, the brand can create that content to fill the gaps.

Preparing for an AI-Centric Marketing Future

Best practices for generative engine optimization are still developing. Sherman predicts that over the next few years, brands will invest heavily in adapting their marketing infrastructure to engage consumers through AI-driven channels.

For marketers aiming to stay ahead, exploring AI marketing tools and training can provide practical benefits. Resources like AI certifications for marketing specialists offer valuable skills to navigate this new landscape.