LLMLayer

Models got good. Their blind spot didn't move: they don't know what's happening now, and they can't read a page they never saw in training.

The fix is to hand the model a tool that fetches the live web. Here's what most people miss. It's one tool, not a category. Search, scrape, crawl, answer — those aren't four businesses. They're one need, give the agent the world, chopped into endpoints. The first company that treats them as one thing behind one key owns the primitive. The market still sells them apart, by different vendors, with different keys. That split is the opening.

Three things lined up.

• Agents got real. Tool-calling and standards like MCP turned the agent from a party trick into something developers actually ship. Each one needs live web access.

• The incumbents stayed split. Search vendors do search. Scrapers scrape. Answer APIs answer. The developer holds three contracts to do one job.

• Price became the fight. Per-call pricing is racing down, which suits a lean entrant with no minimums and no expiring credits over heavy, enterprise-priced incumbents.

Demand is here. The field is fragmented. Price is the lever. Good time to consolidate.

LLMLayer is a single API for everything an agent wants from the web.

• Answer — a web-grounded response with its sources attached, search and model in one call.

• Search — raw results across general, news, images, video, shopping, and scholar.

• Scrape — a URL turned into clean markdown, HTML, or a screenshot, with JavaScript executed.

• Crawl — a whole domain, streamed back page by page as markdown.

• Map — up to five thousand URLs on a domain, for planning a crawl.

• Extract — a page into your own JSON schema, plus summaries, Q&A, links, brand intelligence, PDF content, and YouTube transcripts.

It's model-agnostic on purpose, for developers who won't be locked to one vendor. Swap between GPT-5.1, Claude, DeepSeek, Llama and more (15+, per the company), or use LLMLayer's own flat-priced llmlayer-web and llmlayer-fast models. List prices: scrape $0.001 a page, search and extract and map $0.002, a cited answer $0.007. No subscription. Credits don't expire.

The wedge is price and simplicity. A developer who needs the web for their agent finds one key that does the whole job for a fraction of what gluing vendors together costs. Easy first yes.

The growth is volume. Each agent makes a lot of web calls. The number of agents keeps climbing. A thin fee on a rising tide of calls grows without selling the same customer twice.

The path runs in three moves. Start with indie developers and small teams, the ones who feel the price and the friction hardest. Grow with them as their agents hit production and call volume climbs. Then get baked into the frameworks and starter templates new builders copy from, so the default is set before anyone runs a price comparison.

The market number is an estimate, not a measurement. The broad AI-agents market is cited at roughly $8–12B in 2026 and growing fast. The slice this sits in — web access for agents — is plausibly $100M–$500M today and on the order of $1–3B by 2030 at category growth rates. Directional, not bottom-up. The point is the shape: a layer every agent touches, with usage that only goes up.

First edge: switching cost. Once an agent runs on one key for the whole web, ripping it out to reassemble four vendors is work nobody volunteers for. Boring and dependable keeps the customer.

Second edge: unit cost. Search, scrape, and answer under one roof means one stack to optimize instead of paying three vendors their margins. That funds a price a single-product rival can't match.

Third edge compounds. More calls means better caching and routing, which cut cost per call, which let the price drop, which win more calls. The lead widens on its own.

Yassine Khazzan is the founder, in Rabat. He taught himself AI during COVID on top of prior engineering work, then built CeleritAI — an open-source-Perplexity-style product with paying clients — before going all in here. He's already shipped the painful version of this once. He knows where the bodies are buried in web infrastructure.

My role is the one I bring to everything I build. I'm fundraising-in-chief: I open the doors and help raise the first funding, I don't put in the cash. I bring the hiring infrastructure to staff up fast, the network to reach the first serious users, and the guidance to keep the product sharp as it grows.

The target is the one I hold every venture to: breakeven with salaries paid, or first funding, within six months. No vanity. A business that pays for itself.

• Platform risk. Model providers could fold web access into their own stack and bundle it free. The defense is being faster, cheaper, and neutral across every model.

• Commodity risk. Web access could turn into a near-zero-margin utility. The defense is volume, breadth across all the endpoints, and the integration lock-in that holds the default position.

• Unproven traction. The product is live and shipping — Python SDK, n8n node, public docs — but revenue, customer count, and query volume aren't public. No funding either. The thesis is sound. The numbers still have to come.

I'm betting web access becomes a standard call every agent makes, and the company that makes it the simplest and cheapest call becomes the default. One key, any model, the whole web, priced so low that building your own makes no sense. If that's right, LLMLayer isn't a feature. It's a piece of plumbing the agent economy runs through, and the volume takes care of the rest.