Riveter AI

Riveter is bringing the power of Perplexity to the scale of the spreadsheet. Their AI agents, custom scrapers, and integrations pack the power of browser use and automation for search, at a cost that allows for scale.

Most teams still spend hours (or hire UpWorkers/contractors) to manually copy product specs, check competitor pricing, pull custom KYB/KYC data to assess customer risk, or scrape filings.

Perplexity and ChatGPT are great for single questions, but they can’t handle large amounts of data at once. And they’re working with a single agent and context window instead of one in every cell.

And Riveter’s web browsing can go further:even reading PDFs and images that may contain answers.

With Riveter, anyone can automate complex, auditable web research… without coding.

Our founding team brings deep product and engineering expertise from to YC-backed companies Gusto, Retool, and Middesk.

High-Level Overview

Riveter AI is an advanced AI-powered data enrichment platform that automates large-scale data collection, cleaning, structuring, and enrichment using thousands of autonomous AI search agents running in parallel. It enables non-technical teams to transform spreadsheets and datasets into enriched, auditable, and business-ready data without requiring engineering resources. The platform serves sectors such as e-commerce, market research, finance, and sales by automating tasks like competitor pricing analysis, product specification gathering, customer feedback review, and compliance data extraction. Riveter’s ability to handle massive datasets with detailed audit trails helps companies accelerate strategic decision-making and operational efficiency, significantly impacting the startup ecosystem by democratizing access to sophisticated AI-driven data workflows[1][2][3][4].

Origin Story

Founded in 2024 by a team with deep product and engineering expertise from YC-backed companies like Gusto, Retool, and Middesk, Riveter emerged to solve the widespread challenge of manual, time-consuming data research and enrichment. The founders recognized that existing AI tools like Perplexity and ChatGPT were limited by single-agent architectures and context windows, making them unsuitable for large-scale, structured data tasks. By creating a platform where each spreadsheet cell can run its own AI browsing agent, Riveter enabled scalable, auditable, and no-code web research automation. Early traction came from growth-stage companies seeking faster, more reliable market and competitive intelligence without the overhead of traditional consulting or engineering-heavy solutions[3][4].

Core Differentiators

• Agentic Web Research: Thousands of autonomous AI agents operate in parallel, each capable of browsing, clicking, reading PDFs/images, and extracting data independently for each dataset cell.
• Scalability & Auditability: Handles large datasets (e.g., 200,000+ rows) with transparent, auditable logs showing sources, reasoning, and agent paths for every data point.
• No-Code Integration: Designed for non-technical users, enabling quick setup by uploading spreadsheets and writing simple prompts without engineering support.
• Multi-Modal Data Extraction: Capable of extracting structured data from diverse web formats including web pages, PDFs, and images.
• API-First Approach: Provides seamless integration with applications via a custom API, replacing traditional scraper infrastructure and enabling real-time data delivery.
• Breadth of Use Cases: Supports e-commerce research, sales enrichment, finance diligence, KYB/KYC processes, and more, making it versatile across industries[1][2][3][5][6].

Role in the Broader Tech Landscape

Riveter rides the wave of AI-driven automation and data democratization, addressing the critical bottleneck of scalable, reliable web data extraction and enrichment. As AI adoption grows, the demand for structured, auditable data at scale becomes paramount for strategic decision-making, especially in competitive and fast-moving markets. The timing is ideal given the limitations of traditional scraping and single-agent AI tools, which struggle with scale and consistency. Riveter’s parallel-agent architecture and no-code usability lower barriers for businesses to harness AI, influencing the broader ecosystem by enabling startups and enterprises alike to accelerate product launches, market analysis, and compliance workflows with minimal technical overhead[3][4][6].

Quick Take & Future Outlook

Looking ahead, Riveter is poised to expand its platform capabilities by enhancing agent intelligence, broadening data source integrations, and deepening API functionalities to serve more complex enterprise needs. Trends such as increasing regulatory scrutiny, demand for real-time competitive intelligence, and the rise of AI copilot tools for business strategy will shape its growth trajectory. Riveter’s influence is likely to grow as it becomes a foundational data infrastructure layer for AI-powered products and decision-making workflows, further democratizing access to high-quality web data and enabling faster, more informed business actions across sectors[4][6]. This positions Riveter as a key enabler in the evolving AI ecosystem, transforming how companies gather and leverage data at scale.