Executive Overview
The architecture of the internet was forged in an era defined by human limitations. Traditional search engines—pioneered by giants like Google and Yahoo—were meticulously built, indexed, and optimized for human beings. People have finite spans of attention, limited cognitive bandwidth, and little patience, preferring concise snippets, keyword matching, and easily digestible lists of links.
However, the rapid ascension of Large Language Models (LLMs) and autonomous AI agents has fundamentally inverted this paradigm. Artificial intelligence does not skim web pages with human fatigue; it ingests, processes, and cross-references vast oceans of unstructured data at velocities previously unimaginable. Yet, the foundational plumbing of the web remains anchored to human-centric design.
Enter Keenable, a stealth-born infrastructure startup that has officially broken cover with $26 million in seed funding led by venture capital firm Accel, with participation from Conviction Partners and prominent angel investors. Founded by Andrey Styskin—the former head of search, AI, and cloud at Russian tech titan Yandex—alongside German AI scientist Matthias Petri, Keenable is engineering a web-scale search index designed specifically for machines.
By bypassing the traditional human interface and providing high-performance, cost-effective APIs built for AI consumption during training and runtime, Keenable is positioning itself at the bleeding edge of a tectonic shift. As tech heavyweights like Google and Microsoft increasingly pull back or monetize their legacy search APIs to protect their own ecosystems, Keenable aims to become the foundational search engine powering the next generation of autonomous AI agents.
Detailed Chronology: From Big Tech Roots to a Machine-First Vision
The intellectual origins of Keenable trace back years before the company’s recent funding announcement, rooted in the collective experiences of its founders within the corridors of Amazon and Yandex.
The Amazon and Yandex Genesis
Andrey Styskin spent two decades building and scaling search engines, most notably leading Yandex’s core search, artificial intelligence, and cloud divisions. Alongside co-founder Matthias Petri, Styskin later transitioned to Amazon, where the duo worked extensively on web search infrastructure for ambient computing applications like Alexa.
During their tenure at Amazon, Styskin and Petri observed a paradigm shift. Internal metrics and broader ecosystem observations—bolstered by traffic telemetry from network infrastructure provider Cloudflare—revealed that AI crawlers and automated bots were consuming an exponentially growing share of global search volume. The internet was no longer being queried exclusively by human hands typing queries into a browser bar; it was increasingly being scraped, parsed, and synthesized by algorithms.
Recognizing the API Void
This technological evolution brought a stark realization: legacy search APIs, historically relied upon by developers to power third-party applications, were entering a twilight phase.
As Google and Microsoft raced to integrate generative AI directly into their own consumer products, they faced a classic corporate dilemma. Continuing to license unrestricted, low-cost search APIs to external developers risked cannibalizing their own emerging AI chatbot ecosystems. Consequently, tech giants began restricting access, shutting down legacy APIs (such as Microsoft’s Bing Search API changes and Google’s custom search adjustments), and steering developers toward tightly controlled, bundled services.
Recognizing a massive infrastructure vacuum for independent AI labs, inference providers, and conversational agents, Styskin and Petri left Big Tech to launch Keenable.
Stepping Out of Stealth
Operating quietly while building its foundational technology, Keenable recently emerged from stealth mode backed by a lucrative $26 million seed round led by Accel. Zhenya Loginov, the Accel partner who spearheaded the investment, noted that AI developers currently face a dearth of viable, independent options for web-scale search infrastructure.
With its newly secured capital, Keenable has rapidly scaled its engineering footprint. Operating with a lean, highly specialized team of 15 engineers distributed across the United States and Europe, the startup plans to double its headcount by the end of the year to aggressively fuel its go-to-market strategy.
Supporting Context & Metrics: The Mechanics of Machine-First Search
To understand Keenable’s market disruption, one must examine the engineering hurdles of indexing the modern internet for machines.
The Scale of the Problem
Keenable has constructed an active web search index comprising over 100 billion documents. For context, managing an index of this magnitude requires enormous computational power, high-speed storage arrays, and sophisticated parsing logic.
According to Styskin, building a giant search index is "painfully expensive." However, the true economic bottleneck does not lie merely in storage; it lies in query execution costs.
Enterprise search solutions—often deployed within corporate intranets—frequently fracture and become financially unsustainable when forced to scale up to the size of the entire open web. If an infrastructure provider fails to fine-tune its index structures for specific, targeted machine operations, the computational cost of scanning the entire internet for every incoming AI prompt becomes economically prohibitive.
Eliminating the Economic Bottleneck
Keenable’s primary technological moat lies in its ability to radically narrow the search space milliseconds after a query is received. By optimizing how index structures are traversed, the startup drastically reduces the computational overhead required to retrieve and feed relevant documents to an LLM.
This capability is critical because AI chatbots perform exponentially better when their responses are grounded in verified source documents, mitigating hallucinations and ensuring factual accuracy. This creates a brand-new data feedback loop—a flywheel entirely distinct from the human-behavior algorithms that Google perfected over the last quarter-century.
Current Traction and Deployments
Despite its recent emergence from stealth, Keenable’s infrastructure is already operating in production environments. Several undisclosed AI labs and model inference providers utilize Keenable’s APIs during both the training phase of foundational models and at runtime.
Furthermore, the startup has begun forging strategic ecosystem alliances. Keenable recently announced a formal partnership with voice AI company Gradium, integrating Keenable’s search APIs to power real-time, low-latency information retrieval for conversational voice agents—a use case where every millisecond of search latency directly impacts user experience.
Official Statements and Industry Perspectives
The structural transition from human-centric to machine-centric search has ignited fierce debate across the venture capital and technology landscapes. Key leaders have offered profound insights into why this disruption is occurring now.
Andrey Styskin on the Innovator’s Dilemma
Reflecting on the competitive landscape against entrenched monopolies like Google, Styskin openly acknowledges the daunting financial and cultural barriers of unseating an industry titan. However, he points directly to economic theory to explain why Google is vulnerable:
"If you do not fine-tune your index structures for a specific task, the cost of serving and scanning the whole internet is enormous because of the volume. That’s why you need to innovate on how you can narrow the search space based on your query very fast. This is what we are bringing to the table."
Addressing the dominance of search engine behemoths, Styskin noted that while convincing human users to abandon Google for everyday browsing is nearly impossible, the Innovator’s Dilemma leaves the search giant exposed within the realm of agentic queries. Smaller, agile startups can innovate rapidly and deliver leaner, cost-efficient infrastructure tailored specifically to the operational realities of AI companies.
Zhenya Loginov on Infrastructure Scarcity
Accel partner Zhenya Loginov underscored the urgency of Keenable’s market entry, emphasizing that institutional consolidation by mega-cap technology firms has starved independent AI developers of reliable tools:
"AI players have very few options when it comes to web-scale search infrastructure, especially with major platforms stepping back to protect their proprietary ecosystems."
By refusing to bundle search into a rigid, walled-garden AI chat interface, Keenable acts as a neutral, foundational utility layer—serving as an indispensable pick-and-shovel provider for the modern artificial intelligence gold rush.
Future Outlook: The Death of the Ten Blue Links
As Keenable charts its trajectory toward becoming "the next Google for AI agents," it is entering an increasingly competitive arena. Other nimble players—such as Brave, which offers a developer search API, and Exa.ai, which builds embeddings-based search specifically for LLMs—are also carving out territory in the machine-search ecosystem. Meanwhile, Google itself is aggressively rearchitecting its core consumer search engine to prioritize AI Overviews and conversational summaries.
Yet, this crowded competitive landscape serves as ultimate validation for Keenable’s thesis. Whether engineered for human eyes or autonomous software agents, the foundational utility of traditional search is undergoing an existential transformation.
Keenable’s immediate product roadmap includes the rollout of proprietary retrieval innovations, most notably its upcoming Web Query Language. This advanced capability is designed to enable AI systems to synthesize complex answers by intelligently piecing together fragments of information across disparate web sources—even when no single URL contains a complete answer.
As the tech industry marches deeper into the agentic era, the broader implication is unmistakable: the foundational era of the "ten blue links" is drawing to a close. Startups like Keenable are betting heavily that the future internet will not be browsed by people, but navigated by machines—and whoever controls the infrastructure for machine cognition will control the digital economy of tomorrow.
