Rastro

What do they actually do

Rastro runs AI-assisted “catalog agents” for retailers and distributors. They take messy supplier inputs (Excel files, PDFs, web pages), crawl supplier/manufacturer and public retail sites to pull specs, images, and prices, then match and normalize those into a customer’s product schema with channel‑ready titles, attributes, and translations (the site mentions 50+ languages) rastro.ai NY Tractor Parts case study Un Amour de Tapis case study.

High‑confidence records are auto‑accepted while low‑confidence items are routed to human review. Final outputs are delivered in the customer’s exact schema or PIM format, and Rastro also collects competitor pricing to help set initial prices for new ranges Sunco case study NY Tractor Parts case study.

Public case studies show multi‑thousand SKU jobs completed in days or hours (e.g., 3,200 SKUs enriched and published in under 24 hours; 10,000 products online in less than a week; 98% attribute coverage and an 80+ hours/week manual workload reduction) NY Tractor Parts Un Amour de Tapis Sunco.

Who are their target customer(s)

• Catalog operations teams at mid‑to‑large retailers (5k+ SKUs): They receive large volumes of inconsistent supplier spreadsheets/files and spend weeks or months cleaning, mapping, and normalizing before items can be published, delaying launches and consuming headcount YC launch Sunco case study.
• Distributors/wholesalers onboarding new supplier ranges: They must reconcile and standardize wildly inconsistent supplier data (Excel, PDFs, manufacturer pages), which is slow and error‑prone, making it hard to bring large SKU sets live quickly NY Tractor Parts case study.
• Marketplace/product managers and SEO teams: They need channel‑ready titles, categories, and localized content for multiple marketplaces but lack a repeatable way to generate, map, and translate content at scale Un Amour de Tapis case study rastro.ai.
• Pricing and merchandising teams: They need current competitor and supplier pricing to set launch prices; gathering this data across the web is manual and slow, leading to suboptimal initial pricing NY Tractor Parts case study.
• PIM/IT owners responsible for integrations and data quality: They must map incoming supplier feeds to the company’s exact product schema and handle exceptions, creating constant one‑off integration work that slows supplier onboarding rastro.ai Sunco case study.

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

• First 10: Founder‑led, high‑touch paid pilots for warm leads (YC intros, inbound, referrals), delivering a few supplier ranges to launch‑ready SKUs in days; founders/engineers fill integration gaps and handle human review to prove time/headcount savings with real outputs NY Tractor Parts.
• First 50: Stand up a small SDR/AE team for targeted outbound to distributors, catalog ops, and PIM owners with a repeatable fixed‑scope pilot offer; convert wins with standardized case studies and ROI one‑pagers showing hours saved and SKU throughput.
• First 100: Productize onboarding with prebuilt supplier adapters, mapping templates, and a self‑serve pilot portal; add partnerships with PIM vendors/SIs and a referral program, while increasing automation to reduce human review so more lower‑touch accounts can be supported.

What is the rough total addressable market

Top-down context:

Analyst reports size the PIM/catalog market in the low‑to‑mid tens of billions today (roughly $11–14B) with forecasts of $30B+ by the end of the decade Grand View Research ResearchAndMarkets.

Bottom-up calculation:

Assuming 10,000–20,000 mid‑to‑large retailers and distributors worldwide with 5k+ SKUs and an average annual contract value of $50k–$150k for catalog onboarding/automation, the bottom‑up TAM for Rastro’s niche is roughly $0.5B–$3.0B. This aligns with enterprise PIM pricing bands where mid/enterprise customers often spend thousands to tens of thousands per month on PIM and related services PIM pricing overview.

Assumptions:

• There are 10k–20k global retailers/distributors with 5k+ SKUs that would consider specialized catalog automation.
• Average ACV for this workflow is $50k–$150k/year, consistent with enterprise PIM and services pricing bands PIM pricing overview.
• A meaningful share of these buyers prefers specialized onboarding/automation services in addition to (or alongside) core PIM platforms.

Who are some of their notable competitors

• Salsify: Product‑experience/PIM platform for centralizing product data and syndicating to channels. Overlap: channel‑ready content and taxonomy/mapping. Difference: platform for ongoing management vs. Rastro’s supplier‑crawl + extraction + human‑in‑the‑loop onboarding service.
• Akeneo: Hosted PIM with supplier onboarding and syndication. Overlap: schema mapping, enrichment, supplier data flows. Difference: PIM for maintaining master data, not positioned as an end‑to‑end supplier‑crawl + automated extraction + launch service.
• Feedonomics: Feed‑management and listing optimization service for marketplaces and ad channels. Overlap: data transformations and channel requirements. Difference: focuses on routing/optimizing feeds; Rastro focuses upstream on creating clean master rows from messy supplier inputs.
• Productsup: Product‑to‑consumer feed platform automating transformations and multi‑channel exports. Overlap: rule engines and large‑scale content ops. Difference: built around feed/rule engines; does not market supplier crawling + extraction + human‑in‑the‑loop onboarding for rapid range launches.
• Diffbot: Web‑data extraction and knowledge‑graph APIs. Overlap: crawling and structured product extraction. Difference: a data/extraction building block rather than a finished catalog‑onboarding product with fuzzy matching, human review, and schema‑specific outputs.