The Problem: Why AI Agents Need a New Web Index
Traditional search engines like Google and Bing were designed for human users who cannot process entire webpages at once. These engines rely on keyword matching, ranking algorithms, and snippet-based results to deliver concise answers. However, AI agents—such as chatbots, virtual assistants, and autonomous research tools—operate differently. They require access to raw, structured, and comprehensive data to generate accurate, context-aware responses.
The limitations of existing search infrastructure for AI are becoming increasingly apparent. AI agents need to:
- Process vast amounts of data in real time, far beyond what a human can digest.
- Understand context across multiple pages, not just surface-level snippets.
- Retrieve structured data (e.g., tables, APIs, or metadata) rather than unstructured text.
- Avoid bias introduced by traditional ranking algorithms, which prioritize human-readable content over machine-readable data.
Andrey Styskin, co-founder of Keenable and former head of Yandex’s search, AI, and cloud division, highlights this gap: “Search engines were built for people who can’t scan entire pages. AI agents don’t have that limitation—they need a different kind of infrastructure.” This insight underscores the need for a web index purpose-built for AI, not humans.
Keenable’s Solution: A Web Index for Machines
Keenable is addressing this challenge by building a web index specifically designed for AI agents. Unlike traditional search engines, which prioritize human-readable snippets, Keenable’s index focuses on:
- Raw data retrieval: Providing AI agents with direct access to unfiltered, structured web content.
- Contextual understanding: Enabling agents to process information across multiple sources without losing nuance.
- Scalability: Supporting the massive computational demands of AI-driven queries.
Technical Breakdown: How Keenable’s Index Works
While Keenable has not disclosed all technical details, its approach likely involves:
- Crawling and Indexing:
- A distributed crawler that fetches web content at scale, similar to Googlebot but optimized for AI consumption.
- Indexing not just text but also metadata, APIs, and structured data (e.g., JSON-LD, Schema.org markup).
- Data Structuring:
- Converting unstructured web content into machine-readable formats (e.g., knowledge graphs, embeddings, or vector databases).
- Storing data in a way that allows AI agents to retrieve and synthesize information efficiently.
- Query Processing:
- A query engine that translates AI agent requests into optimized searches across the index.
- Support for complex, multi-step queries (e.g., “Summarize the latest research on quantum computing and compare it to 2025 trends”).
- Integration with AI Models:
- APIs that allow AI agents to plug directly into Keenable’s index, bypassing the need for traditional search engine APIs.
Partnerships and Early Use Cases
Keenable has already partnered with Gradium, a voice-AI company, to enable live information retrieval for voice-based AI assistants. This collaboration demonstrates how Keenable’s index can power real-time, context-aware responses for AI-driven applications. Other potential use cases include:
- Autonomous research tools for academia and enterprise.
- AI-powered customer support agents that retrieve and synthesize information from multiple sources.
- Enterprise knowledge management systems that index internal and external data for AI-driven insights.
Why This Matters: The AI Search Revolution
The emergence of Keenable reflects a broader shift in the search industry. Traditional search engines are struggling to adapt to the demands of AI agents, and their reluctance to open up their infrastructure is creating opportunities for startups like Keenable.
The Incumbent Problem: Google and Microsoft’s Dilemma
Google and Microsoft have historically dominated the search market, but their business models are built around human users. Opening up their search APIs to AI agents could:
- Cannibalize ad revenue: AI agents don’t click on ads, which are a primary revenue source for search engines.
- Increase computational costs: Serving AI agents requires more resources than serving human users.
- Expose proprietary data: AI agents could extract and synthesize data in ways that undermine search engines’ control over information.
As a result, both companies have shut down or restricted access to their search APIs, leaving a gap in the market. This has created an opening for startups like Keenable, Brave, and Exa to build alternative infrastructure.
The Competitive Landscape
Keenable is not alone in this space. Other players are also exploring AI-first search solutions:
- Brave: Known for its privacy-focused browser, Brave is expanding into AI-driven search with its Brave Search API, which offers ad-free, unbiased results.
- Exa: A startup focused on semantic search, Exa provides APIs for retrieving structured data from the web, similar to Keenable.
- Perplexity AI: While not a direct competitor, Perplexity AI offers an AI-powered search engine that synthesizes information from multiple sources, highlighting the demand for AI-native search tools.
However, Keenable’s focus on building a dedicated web index for AI agents sets it apart. Unlike Brave or Exa, which retrofit existing search infrastructure for AI, Keenable is starting from scratch with a machine-first approach.
Industry Impact: What Keenable’s Rise Means for AI and Search
Keenable’s $26 million seed round, led by Accel, signals strong investor confidence in the future of AI-native search. The implications of this shift are far-reaching:
1. Accelerating AI Adoption
AI agents are becoming increasingly sophisticated, but their effectiveness is limited by the quality of the data they can access. Keenable’s index could unlock new capabilities for AI agents, such as:
- Real-time fact-checking for news and research.
- Automated content generation with accurate, up-to-date information.
- Enterprise automation (e.g., legal research, financial analysis, or customer support).
2. Disrupting Traditional Search
If Keenable succeeds, it could challenge the dominance of Google and Microsoft in the search market. While these incumbents are unlikely to disappear, their control over information could weaken as AI agents rely more on alternative indexes.
3. New Business Models for Search
Traditional search engines monetize through ads, but AI-native search opens up new revenue streams, such as:
- Subscription-based APIs for enterprises and developers.
- Pay-per-query models for high-volume AI applications.
- Partnerships with AI companies (e.g., licensing data to LLM providers).
4. Ethical and Regulatory Challenges
As AI agents become more reliant on web indexes like Keenable’s, new ethical and regulatory questions will arise:
- Data privacy: How will Keenable handle sensitive or personal data in its index?
- Bias and fairness: Will AI agents inherit biases from the web content they access?
- Copyright and licensing: How will Keenable navigate copyright issues when indexing and redistributing web content?
These challenges will require careful navigation as the industry evolves.
The Future of AI Search: What’s Next for Keenable?
Keenable’s immediate priority is scaling its engineering team, with plans to double its headcount by the end of 2026. This expansion will likely focus on:
- Improving the crawler and indexer to handle more data sources and formats.
- Enhancing query processing to support more complex AI agent requests.
- Expanding partnerships with AI companies, enterprises, and developers.
Long-Term Vision
In the long run, Keenable aims to become the default web index for AI agents, much like Google is the default search engine for humans. To achieve this, it will need to:
- Build a robust ecosystem of developers and AI companies using its APIs.
- Differentiate from competitors like Brave and Exa by offering superior data quality and performance.
- Navigate regulatory and ethical challenges to ensure its index remains compliant and trustworthy.
Potential Challenges
Keenable’s success is not guaranteed. Key challenges include:
- Competition from incumbents: Google and Microsoft could eventually open up their search APIs to AI agents if they see a threat.
- Data quality: Ensuring the index remains accurate, up-to-date, and free from misinformation.
- Adoption barriers: Convincing AI companies to switch from traditional search APIs to Keenable’s index.
FAQ: Keenable and the Future of AI Search
1. What is Keenable?
Keenable is a startup building a web index specifically designed for AI agents. Unlike traditional search engines, which prioritize human-readable results, Keenable’s index provides raw, structured data for AI-driven applications.
2. Why do AI agents need a different web index?
AI agents require comprehensive, structured data to generate accurate responses. Traditional search engines are optimized for humans, who need concise snippets, not raw data. Keenable’s index bridges this gap by providing AI agents with the information they need to operate effectively.
3. How does Keenable’s index differ from Google or Bing?
Google and Bing focus on ranking and snippet-based results for human users. Keenable, on the other hand, provides direct access to unfiltered, structured web content for AI agents. This allows AI agents to process and synthesize information more effectively.
4. Who are Keenable’s competitors?
Keenable’s main competitors include:
- Brave: A privacy-focused search engine expanding into AI-driven search.
- Exa: A startup offering semantic search APIs for AI applications.
- Perplexity AI: An AI-powered search engine that synthesizes information from multiple sources.
5. What are the potential use cases for Keenable’s index?
Keenable’s index can power a variety of AI-driven applications, including:
- Autonomous research tools for academia and enterprise.
- AI-powered customer support agents.
- Enterprise knowledge management systems.
- Real-time fact-checking for news and research.
6. How does Keenable plan to monetize its index?
Keenable is likely to monetize through:
- Subscription-based APIs for enterprises and developers.
- Pay-per-query models for high-volume AI applications.
- Partnerships with AI companies (e.g., licensing data to LLM providers).
7. What are the ethical concerns around AI-native search?
Key ethical concerns include:
- Data privacy: How will Keenable handle sensitive or personal data?
- Bias and fairness: Will AI agents inherit biases from web content?
- Copyright and licensing: How will Keenable navigate copyright issues when indexing and redistributing web content?
For more on AI-driven security challenges, check out our recent article on OpenAI Exposes Russian AI-Powered Influence Operation .
Conclusion: A New Era for AI Search
Keenable’s emergence from stealth mode marks a significant milestone in the evolution of search technology. By building a web index tailored for AI agents, the startup is addressing a critical gap in the market and positioning itself as a key player in the AI revolution.
The $26 million seed round, led by Accel, underscores the potential of this approach. As AI agents become more integrated into our daily lives—whether through chatbots, virtual assistants, or autonomous research tools—the demand for AI-native search infrastructure will only grow.
However, Keenable’s success is not guaranteed. It will need to navigate competition from incumbents, ensure data quality, and overcome adoption barriers. If it succeeds, it could reshape the search industry and accelerate the adoption of AI-driven applications across sectors.
For now, Keenable’s focus on scaling its engineering team and expanding partnerships will be critical. The next 12 months will determine whether it can establish itself as the go-to web index for AI agents—or whether incumbents like Google and Microsoft will adapt and reclaim the space.
One thing is clear: the future of search is no longer just about humans. It’s about building infrastructure for the machines that will define the next era of technology.
Source: Original Article