Flatfile

Flatfile

Flatfile was founded by David Boskovic and Eric Crane, former product managers who repeatedly encountered the same bottleneck: every new customer implementation triggered a manual, error-prone scramble to clean and structure incoming data. They built Flatfile to replace brittle, one-off import scripts with a foundation layer that normalizes messy files into production-ready datasets. The company operates from Denver, Colorado. The platform ingests data from uploads, APIs, webhooks, databases, and cloud storage, then applies agentic AI to detect file structure, parse content, and auto-map fields—Flatfile claims 95% of fields are auto-mapped using AI. Customers use the workspace to enforce validation rules, run AI Transform prompts across entire datasets, and maintain a human-in-the-loop review before any change goes live. The architecture supports nested fields, linked records, and multi-sheet relationships, bringing database-level structure to a spreadsheet-like interface. Flatfile reports a 4.7x acceleration in data onboarding versus manual processes and a 78% rate of customers going live within 30 days. The firm has raised nearly $100 million in disclosed funding. Its Series B was led by Tiger Global, with participation from Gradient Ventures, Scale Venture Partners, Workday Ventures, Afore Capital, and Two Sigma Ventures. Angel investors include individuals from Airtable, DocuSign, LinkedIn, and Gainsight. The capital is funding the transition from an AI-assisted import tool to a collaborative agent-powered data migration workspace. Flatfile targets enterprise teams running complex implementations, citing a 25% decrease in per-project person hours and a 20% decrease in time to launch among its users. Flatfile’s structural distinction lies in treating data onboarding as a dedicated software category rather than a feature of broader integration platforms. The company builds for the messy reality of customer-supplied files—unstructured, inconsistently formatted, and versioned—with an AI-native engine that pairs autonomous extraction and transformation with mandatory human approval gates. This architecture shifts data preparation from an engineering dependency to a configuration-driven workflow that implementation and customer-success teams can own directly.