expand.ai

What do they actually do

expand.ai provides a developer SDK and managed service that turns public web pages (or sets of pages) into a structured, type‑safe API. It automatically infers a schema for the data on a page and returns typed objects you can query directly from code, rather than writing and maintaining custom scrapers yourself (expand.ai).

The service runs the hard parts of web extraction for you—browser rendering, proxies, bot protection, crawling at scale, and reliability checks—and can optionally generate semantic, source‑attributed markdown summaries for use with LLMs. The site also shows upcoming dataset features and export/sync targets like S3, Postgres, and Google Sheets (expand.ai).

The product appears to be in early access with a waitlist and no public pricing, and the company is a YC Summer 2024 startup with a very small founding team listed on YC’s directory. They are hiring and describe a mission to “turn the internet into a database,” with claims of operating at web‑scale scraping on their site (expand.ai; YC listing; careers).

Who are their target customer(s)

• Startup/SaaS developers building features from web data: They don’t want to build and maintain fragile, site‑specific scrapers or map messy HTML to types. They need a typed API that stays stable even as sites change (expand.ai).
• Data engineers / ETL teams ingesting web data into warehouses: Managing crawling, rendering, proxies, and export pipelines is time‑consuming and brittle. They need structured outputs and simple syncs to S3, Postgres, or Sheets without running scraping infra themselves (expand.ai).
• AI/LLM product teams needing reliable context: Feeding raw pages to models creates noisy context and hallucinations. They want semantically cleaned, source‑linked markdown and typed records to ground model answers (expand.ai).
• Market research & competitive‑intelligence teams: Manual scraping or one‑off vendors break frequently and lack freshness. They want stable, structured snapshots of pricing, features, or listings with reliability/back‑checking (expand.ai).
• Agencies/consultancies delivering custom data integrations: Supporting many client sites means constant scraper maintenance and anti‑bot work. They need a managed layer for rendering, proxies, and schema maintenance to reduce delivery risk (expand.ai; careers).

How would they acquire their first 10, 50, and 100 customers

• First 10: Use YC and founder networks for warm intros to product/data teams, run paid pilots with hands‑on setup/validation, and secure testimonials from live use cases. Offer short‑term SLA and direct support to get one production integration per customer quickly.
• First 50: Package the first wins into case studies and copy‑paste vertical guides (pricing, job listings, market maps). Do targeted outreach to startups, agencies, and data teams with pilot credits; run a few webinars/demos and sponsor two meetups or hackathons.
• First 100: Add self‑serve onboarding, prebuilt export connectors, and templates so small teams can go live without help. Use paid search and developer‑community ads to drive trials, plus channel partnerships with ETL/analytics consultancies and a simple referral credit.

What is the rough total addressable market

Top-down context:

Analysts size the modern web‑scraping/managed extraction market at roughly ~$1.0B today, while the broader data‑integration market is ~$15.18B in 2024. Counting the scraping market plus 5–20% of integration spend tied to external web/unstructured data yields a practical range of about $1.7B to $4.1B (Mordor Intelligence; Grand View Research).

Bottom-up calculation:

Illustratively, if ~40,000 target teams (startups, mid‑market, agencies, internal data/AI teams) buy at an average of $25,000 per year, that implies ~$1.0B in spend; layering 10,000 larger teams at ~$100,000 per year adds another ~$1.0B, putting a $1–2B bottom‑up range broadly in line with the top‑down view.

Assumptions:

• Tens of thousands of globally addressable teams regularly buy web data/LLM‑grounding pipelines.
• Average contract values spanning ~$25k (SMB/mid‑market) to ~$100k+ (enterprise).
• Meaningful adoption across both net‑new AI use cases and replacement of bespoke scrapers.

Who are some of their notable competitors

• Diffbot: APIs and a large web knowledge graph that auto‑classify pages and return structured entities (articles, products, people), letting teams query web content without page‑specific scrapers (product; Extract API).
• Zyte (formerly Scrapinghub): Managed scraping infrastructure (proxies, headless browsers, CAPTCHA handling) and an API for extracting structured fields at scale, aimed at reliability‑sensitive teams (Zyte API; homepage).
• Apify: A platform for building and running scrapers/automations (“actors”); developers can deploy custom extraction code or use templates and integrate outputs into pipelines (docs | homepage).
• Browse.ai: No‑code, point‑and‑click robots that turn a website into an API or spreadsheet endpoint, designed for non‑developers who want monitored extraction without writing code (features; help).
• Import.io: UI‑first extractors and API that train from examples, schedule runs, and deliver structured JSON/CSV or push to sheets/warehouses for self‑service enterprise use (products; homepage).