Funding Flatfile to remove barriers between humans and data

Let’s turn messy spreadsheets into useful product data

Today we are excited to announce Flatfile’s first outside funding of $2 million! The decision to move from bootstrapped to venture-backed business was not an easy one. After seeing the breadth and depth of problems that data import causes for software applications, though, we knew that raising venture capital was the fastest way to tell the world about our solution to the oft-neglected data import process.

Table of contents:

1. How we started

2. Finding market fit

3. Raising capital

4. Where we’re going next

How we started

Flatfile started as a side project, a response to the frustration of building a data importer for the umpteenth time. Data import is as ubiquitous as billing or reporting, but previously had no standard solution in the competitive software industry where speed and execution are the only true long-term differentiators.

My co-founder David took it upon himself to design and build a standardized import experience. He didn’t want it to only function under the best circumstances--he created something that thoughtfully allowed for real humans in the process, no matter that human’s technical abilities.

With my abnormal affinity for spreadsheets, organization, and data analysis, I was naturally interested when David asked me to join him. The software industry as a whole had come to accept that moving data from a spreadsheet into a product was a disjointed, frustrating experience. And to me, that status quo was unacceptable.

Finding market fit

To validate that it wasn’t just our own personal frustrations, David and I took the initial version of Flatfile to market by running small awareness campaigns. We explicitly chose to avoid selling into our own networks right away in order to determine whether the frustration with importing data was as pervasive as we thought.

The response was overwhelmingly positive. From garage startups to publicly traded enterprises, marketing automation to software-enabled services companies, we learned that data import was fundamentally broken across the market and that we have the solution. Firefighters, real estate agents, analysts, and athletes all began using Flatfile on a daily basis.

As we leaned into these early customer relationships, the resounding feedback we heard was how broad the problem was not just across the market, but within software teams. Data import is consistently challenging for product teams because, in order to do it well, it must be prioritized against other differentiating features. Engineering teams become frustrated when they take time away from solving new problems to resolve data import. Support teams then get stuck with the aftermath of a “check-the-box” level solution, relegated to cleaning spreadsheets instead of building lasting client relationships. And design teams sit helplessly and cringe as they watch their customers struggle to simply import a spreadsheet.

Examples of frustrating data import experiences featuring unhelpful errors

Ultimately, these internal frustrations lead to external ones. Prospects abandon a new application when it takes forever to get set up. Users bombard support channels with the same questions over and over again, ignoring volumes of painstakingly written support docs that address every single variation. Customers lose their patience with an app and switch to a competitor when they consistently deal with frustrating experiences, thinking surely someone can help me make use of my data!

Raising capital

The aftermath of this exploration left me wanting to run through the streets with a megaphone, screaming, “stop building your data importer! We’ve got one for you already!” That kind of brand outreach isn’t particularly practical or scalable, so we decided to do the next best thing: we sought outside capital.

We started with significant evidence to support the pervasiveness of the data import problem: a set of active, paying customers who were excited about speaking with prospective investors. We also had already assembled a dedicated team of quality humans, so we knew we could move quickly as soon as the fundraising round closed. As a result, the process was, we are told, “fast” and “smooth”.

We are extremely proud to have such a strong syndicate of investors excited about us, the market opportunity, and our ability to capture it. Many thanks to Gaurav Jain and Anamitra Banerji from Afore, our lead investor, as well as David Frankel from Founder Collective, Ben Blumenfeld and Enrique Allen from Designer Fund, Michael Ma and Joe Montana from Liquid2, and MZ from Gradient Ventures (Google’s AI fund) for believing in us. Additionally, we’d like to give a shoutout to our cohort of angel investors: Nick Candito, Timothy Chen, Anthony Goldbloom, EJ Keisari, Benny Wong, and Alnoor Pirani.

Investors of Flatfile: Afore Capital, Founder Collective, Gradient Ventures, DesignerFund, and Liquid2 Ventures

Where we’re going next

Flatfile has already helped hundreds of companies seamlessly import, clean, and consolidate more than 100 million rows of data across more than 50,000 end users. We will use this investment to further scale Flatfile to thousands of businesses and millions of end-users.

An example of CSV field validation and cached imports, all possible with Flatfile’s data onboarding platform.

While we are excited about how we’ve been able to solve data import inefficiencies so far, we are not stopping here. Our team of eleven is already hard at work teaching our product to be faster and better. In the near term, expect more input sources, webhooks for instant connections, and new ways to implement Flatfile. We will be achieving compliance certifications - like our forthcoming SOC2 audit - as well as designing asynchronous import flows. And on top of it all, we will train machine learning systems with real user interactions to cut down on the effort required to import and validate data.

Our ultimate goal is single-click import: seamless integration between data sets. We believe in a world where labeling and cleaning data is a long-forgotten challenge. Where data scientists spend the majority of their time learning from data, not scrubbing it. And where companies can focus on building new, unique experiences on top of quality customer data. We are excited about this first of many milestones along the way.