Lyndsey Bunting

Lyndsey Bunting runs Blue Onion, a New York company that does something most people never think about until it breaks: it makes the numbers behind a retail business agree with each other. Every order, every payment, every deposit that lands in a bank account gets matched, checked, and turned into clean financial records. As CEO and co-founder, Bunting has grown Blue Onion into a platform that has reconciled more than $30 billion in transactions for over 200 retail and ecommerce brands, from Supergoop! to BarkBox to a.k.a. Brands.

In April 2025 the company announced a $10 million Series A led by Viola FinTech, bringing its total funding to roughly $17.6 million. The pitch behind the round is straightforward and a little contrarian: before anyone can put artificial intelligence to work inside a finance department, the underlying data has to be trustworthy. Bunting has spent her career watching that data fall apart, and Blue Onion is her answer.

"There is a huge opportunity for AI in this space," she said in an interview after the raise. "Currently, many companies are too blocked by bad data to even use AI." It is a practitioner's observation, not a slogan. Bunting is not describing a problem she read about. She is describing one she lived, month after month, from inside the finance teams she used to run.

A problem she lived every month

Before Blue Onion, Bunting was VP of Finance at Birchbox, the subscription beauty company, and later CFO at Resonance Companies. Those are the jobs where the reconciliation problem stops being abstract. A brand sells across a website, a marketplace, a retail partner, and a handful of payment processors. Each of those systems reports money differently. Someone has to sit down and make them line up, catch the refunds and the partial shipments and the fees, and produce a number the company can actually trust. At Birchbox, that someone was often Bunting's team.

She has said the tools available at the time put the burden on the customer. "In the legacy ERP world and for most accounting software, ensuring clean financial data was the responsibility of the customer, requiring expensive and faulty data connectors, time-consuming reconciliations and manual adjustments," she explained. The work was slow, error-prone, and never really finished. It was the kind of problem that follows a finance leader home.

So in May 2020 - in the middle of the pandemic, when a lot of people were pulling back - she started building the thing she had always wished existed. Blue Onion joined Y Combinator's Summer 2020 batch. Her co-founders brought the technical and data science muscle to match her finance depth. The premise was simple: instead of asking customers to clean their own data, Blue Onion would take on that responsibility itself.

From investment banking to the Peace Corps to fintech

Bunting's route to founding a fintech was not a straight line. She studied accounting and finance at The Wharton School at the University of Pennsylvania, then started her career in 2007 at Jefferies & Company as an industrials investment banking analyst. In 2010 she left Wall Street for the Peace Corps, working as a community economic development consultant, helping local organizations and entrepreneurs build basic business capacity in difficult conditions.

That combination - the rigor of banking and the ground-level work of helping small operators understand their own money - shows up in how she talks about Blue Onion's mission. She traces her interest in finance back further still, to her grandfather and her father, both small business owners. Growing up around people who ran their own shops gave her an early, personal sense of what it means to fight to understand your own numbers. Blue Onion, in a way, is that fight automated.

The bet: close the books daily

Blue Onion's platform connects to a brand's order systems, payment processors, and bank accounts, then automates the reconciliation that used to eat days of manual work. "We believe that there's a better way to process financial data," Bunting said. "The Blue Onion platform integrates with your order systems, payment processors, and your bank accounts to fully automate the reconciliation process."

The most concrete way to describe the payoff is timing. In most companies, the accounting close is a monthly or quarterly fire drill - a scramble to gather data, chase discrepancies, and produce financials before a deadline. Bunting's ambition is to make that continuous. For the first time, she has said, Blue Onion lets companies close their books daily instead of struggling at month and year end. The product suite reflects that framing, with modules the company calls Close, Fabric, and Edge, each aimed at a different part of the finance workflow.

The company frames itself as the data backbone for the next generation of AI-powered finance teams, and that is where the Series A thesis and Bunting's own experience meet. The market is racing to bolt AI onto finance. Bunting's argument is that the sequencing matters. Bad data in means bad answers out, no matter how capable the model. Clean the data first, and the automation - and eventually the AI - has something solid to stand on.

What comes next

The Series A gives Blue Onion room to push further into that vision: deeper automation, more integrations, and a data layer that CFOs can rely on as they start experimenting with AI. Bunting's stated goal is to make financial information less intimidating and more actionable - to take a function that has always been reactive and slow and make it fast, continuous, and trustworthy.

There is a quiet consistency to her story. The granddaughter of a small business owner, the banker who left for the Peace Corps, the finance chief who got tired of bad data and built the tool she needed. Each step points at the same idea: that people running businesses deserve to trust their own numbers. Blue Onion is the largest version of that idea she has built yet, and with $30 billion in transactions already run through it, it is no longer a bet on whether the problem is real. It is a question of how far the fix can go.