Seltz Raises $12.5M in Seed Funding Led by B Capital

The round also saw participation from futurepresent, Italian Founders Fund, arc Investors, United Ventures, Vento Ventures, Mango Capital, 2100 Ventures, and Future Back Ventures.

The company plans to use the funding to improve its crawling and indexing technology, expand its engineering and research teams, and speed up go-to-market efforts with AI labs, AI-native startups, and enterprise customers.

Mallia has spent much of his career preparing for this opportunity. He earned a PhD in Computer Science from New York University, where he focused on information retrieval. He later worked as an applied scientist on Amazon’s artificial general intelligence team and as a research scientist at vector database company Pinecone before founding Seltz.

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According to Mallia, today’s AI-driven search landscape is similar to the early 2000s when Google’s PageRank transformed how information was discovered online. He believes a new revolution is underway, powered by transformer models and AI systems that increasingly perform search and information retrieval on behalf of users.

Mallia says Seltz stands out because it owns its entire search technology stack, including its web crawler, search index, retrieval models, and ranking system, instead of relying on another company's search engine. Many AI search products depend on APIs from Google, Bing, or Brave.

Some AI companies, including OpenAI and Perplexity, are also building their own search indexes and web crawlers to provide up-to-date information. However, Google is still considered to have the strongest search index because its massive user base and billions of daily searches allow it to discover and index more of the web than its competitors.

In December, Google filed a lawsuit against SerpApi, a service that scraped Google search results and reportedly counted OpenAI among its customers. Other AI companies, including Anthropic and Mistral, have reportedly relied on Brave’s search index to power web search features. Mallia believes this shows that even major AI labs still depend on external search providers, creating an opportunity for independent alternatives.

Seltz has built its own system, which crawls hundreds of millions of web pages each day and delivers results in less than 200 milliseconds. Instead of returning an entire webpage or summary, the platform identifies and extracts the exact table, text, image, or passage an AI agent needs, an approach Mallia calls "context engineering."

Seltz was founded in October and is incorporated in the United States. The company currently has a small team of 15 people, including six full-time employees, and operates fully remotely. Team members are based in the San Francisco Bay Area as well as near universities in Pisa, Italy, and Leipzig, Germany.

According to Mallia, many team members hold PhDs in information retrieval, and several have experience from Amazon’s AI teams. Seltz is also supported by advisors and angel investors from companies such as Google, Ramp, Cohere, Synthesia, and Databricks, along with researchers from New York University and the University of Glasgow.

Seltz founder and CEO Antonio Mallia said traditional search engines were designed for people typing short keyword-based queries and then skimming a list of ranked links. AI agents work differently. They fire off long, precise queries—some "research agents" send dozens or hundreds of queries in parallel—and they need machine-ready information they can cite, not a snippet designed to entice a human to click through.

"The old search methods don't work because they were architected for humans," Mallia said. "The information [the AI agent needs] is actually not in the snippet. It's in the body of the web page, it's in things like tables, images, and other forms of representation that can be useful for an LLM or for an agent."

About Seltz

Founded by Antonio Mallia, Seltz provides web retrieval infrastructure for AI agents and large language models. Its platform helps AI systems access up to date, reliable, and low latency information through an API, eliminating the need to rely on search engines built for human users.

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