Can you make a document really digging deep what you kind explaining how the product works for Keenable, how it was made, and the diffrent offering for example I understnad how Mcdonalds works I really want to understand every part of how the business. Then can you compare that to its main competitors for now lets focus on Parallel and Exa
Researched as of 5 September 2026 from 240 official product and documentation pages across the three vendors plus 757 independent and company reporting extracts. All three companies sell web intelligence to developers and AI agents; product claims about quality, latency and coverage below are vendor claims unless attributed to independent reporting.
Six stages, three companies. A solid block is a documented product; an outlined amber block is a capability reached only indirectly or still announced; an empty dotted cell is not offered publicly. Hover any block for detail.
Keenable is an independent, agent-oriented web search and page-fetch layer built on its own 100B+ document index, sold as two low-latency primitives (search and fetch) with under-250ms p95 claimed in US East. Parallel Web Systems is the broadest explicitly modular “programmatic web” stack — Search, Extract, Task, Responses, Monitor, FindAll, Entity Search and Chat — founded by former Twitter CEO Parag Agrawal. Exa is a semantic/neural search and data platform with Search, Contents, Agent, Websets and Monitors, plus proprietary people, company and publication indexes.
Where each wins: Keenable for high-volume, latency-sensitive grounding at frontier scale and for keyless prototyping; Parallel when the buyer wants one vendor for the whole workflow from cheap retrieval to long-running cited research and monitoring; Exa for entity-heavy work (people, companies, papers), neural retrieval quality, and the most mature customer ecosystem.
“Web intelligence” is really five distinct jobs, and the lead visual is organised on them: SERP/search (ranked results and excerpts for a query), fetch/extract (turning one URL into clean model-ready text), deep research (multi-hop, cited synthesis), entity/list building (exhaustive enumeration of companies or people matching criteria), and monitoring (standing queries pushed on change). Keenable competes in the first two; Parallel and Exa ship products across all five. Do not flatten the units: a search request, an extracted URL, a Task run, a Responses call, a matched entity and a monitor check are priced and consumed as different products.
Think of Keenable as a kitchen that pre-cooks the entire web. Raw inputs: public web pages, crawled and refreshed continuously. The production line: parse, clean and index those pages into a store of 100B+ documents. The menu: exactly two core items — search (ask a question, get ranked results with snippets and timestamps) and fetch (hand over a URL, get the page back as clean markdown, from the indexed copy or fetched live). Ordering channels: REST API, a remote MCP server for agents (tools search_web_pages and fetch_page_content), a CLI, and roughly 26 framework integrations from LangChain and LlamaIndex to Vercel AI SDK and NVIDIA NeMo. The counter with no cashier: a keyless shared public tier (1,000 requests/hour, 10/second per IP) lets anyone taste the product; an API key removes the hourly cap and switches on the revenue meter — credits per operation under SKUs search.realtime, search.pro, fetch and fetch.live, with 100,000 free requests each month. Distinctive menu options: point-in-time search (query_time re-bases the whole query to a past instant), date/site filters, live fetching, and an LLM extraction instruction on fetch.
Solid = documented by Keenable; dashed amber = inferred from public evidence, standard for any web-scale search system; dotted grey = not publicly disclosed. Keenable has not published its crawler design, ranking models, hardware or margins, and none are invented here.
Discover and re-visit pages at web scale. Required by a 100B+ index and live fetch, but crawler design is undisclosed.
Strip pages to clean text/markdown. Implied by markdown fetch output; method undisclosed.
100B+ documents with acquisition and publication timestamps (ISO 8601 in API responses).
Natural-language query plus site, date and point-in-time filters; realtime vs pro modes.
Ranking models, hardware and index structures are proprietary; founders cite task-tuned index design as the cost lever.
Return the indexed copy or fetch live (live=true); optional LLM extraction instruction on the page.
Titles, URLs, snippets, timestamps, markdown with truncation flags — sized for a context window, not a human SERP.
REST, remote MCP, CLI, SDK/framework integrations, agent connectors (Claude, ChatGPT plugins, OpenClaw, ketch…).
Credits per authenticated operation by SKU; keyless tier unbilled; 402 when credits run out.
Users vs buyers. The user is an agent or a developer; the economic buyer is an AI lab, inference provider or agent-platform team — Keenable’s own hiring page says its most important customers are “the teams building frontier models and the products on top of them”, ranging from a solo builder doing hundreds of queries a day to a lab doing millions. Acquisition loop. Keyless public tier → framework integrations put the tool inside every popular agent stack → free 100k monthly requests convert tinkerers → usage-based credits and dedicated-capacity contracts monetize scale. Costs (analytical inference, not disclosed figures): crawling and refreshing the web, index storage and serving compute are the dominant variable costs; the CEO calls index building “painfully expensive”. Fixed costs are small — roughly 15 engineers across the US and Europe. Unit-economic drivers (inference): requests served, share of live fetches vs cached copies, index refresh rate, and compute per query. Partnerships. One disclosed: voice-AI company Gradium, for live retrieval within a spoken-conversation latency budget. Defensibility. An owned index at 100B+ documents is the moat claim; the risk section below tests it.
Keenable was founded by Andrey Styskin (CEO; previously led Yandex’s search, AI and cloud division, later a director at Amazon AGI) and Matthias Petri (Chief Scientist; Principal Applied Scientist at Amazon AGI, where he worked on web-grounding infrastructure for Alexa). PitchBook and several reports state a 2025 founding and a $26M seed dated 5 November 2025; the company emerged from stealth on 25 August 2026 — treat the exact founding date as reported, not confirmed by the company. The seed was led by Accel with Conviction Partners, Brightwing Capital, ScOp Venture Capital and angels from Google and Amazon. The thesis: consumer search engines were optimized for human clicks and are being closed to third-party API access, so agents need an independent index tuned for high-frequency, low-latency machine queries. Production use is reported at several unnamed AI labs and inference providers, during both training and runtime.
Prices are current public list prices from official documentation as of 5 September 2026 and can change. Units are not equivalent across rows or vendors.
| Dimension | Keenable | Parallel | Exa | Sources |
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All three own an index, so the moat contest moves to second-order factors. Index scale and freshness: all claims are self-reported and none independently audited. Retrieval quality: each vendor publishes benchmarks in which it wins; treat all as marketing until reproduced. Agent-native output is table stakes — dense excerpts, markdown, structured output. Workflow breadth favors Parallel, entity data favors Exa, price/performance at frontier volume is Keenable’s wedge. Distribution: Keenable has moved fastest per dollar — 26 framework integrations on a $26M seed — while Exa has the deepest installed base and Parallel the strongest enterprise channel (Google Gemini Enterprise grounding). Capital intensity cuts both ways: Keenable’s founders argue task-tuned index design makes web search 10x cheaper; if true it is the moat, if not, the $331M funding gap to its rivals is decisive.
These are open questions raised by the public record, not allegations.
Today Keenable is a fast, cheap, narrow search-and-fetch layer on an owned web-scale index, one year old, with $26M and 15 engineers. To become a durable large business it must convert unnamed production use into named, expanding contracts, prove its cost and latency claims independently, and either stay the best-price primitive as volume explodes or grow a second act (Web Query Language). Choose Keenable for high-volume grounding and fetch where latency and unit cost dominate; Parallel when one vendor must cover search through deep research, entity discovery and monitoring; Exa for neural retrieval quality, people/company/publication data and the most proven ecosystem.
Product details come from each vendor’s official documentation and blog (80 pages per vendor in this research set) and are therefore vendor claims — including all quality, latency, coverage and benchmark statements. Company facts (funding, founders, customers, risks) come from independent reporting: TechCrunch, SiliconANGLE, Pulse2, Quasa, PitchBook and others, drawn from 757 company-reporting extracts. Where sources conflict — for example Keenable’s founding date and seed timing — the conflict is stated rather than resolved. Prices are public list prices as of 5 September 2026 and can change.