Simon AI: Breathing New Life Into a Stale Finance Function
How a mature SaaS company with an existing finance function eliminated model debt, navigated a Series D, and gained real cash visibility
The Client
Simon AI is a mature SaaS company offering a customer data platform that enables enterprise marketing teams to activate their data at scale. After 10+ years of growth and evolution of the business, the existing underlying finance infrastructure became stale and outdated for a company of its current size.
The Challenge
When Ben Walker started working with South Bouquet Advisory, Simon AI already had an internal finance function in place. This wasn't a company starting from scratch. But years of rapid growth had left the finance operations carrying what Ben described as significant "operational debt." The daily demands of reporting, accounting, and operations were no longer telling an accurate story that could be directed toward a true viewpoint into the company's future.
The most critical problem was the financial model itself. The existing model had been built for an earlier version of the business and had since been patched repeatedly to keep pace with changes, too many "legacy band-aids," in Ben's words. The result was a forecasting tool that had diverged from reality in ways no one could explain. Cash balance projections and actual cash positions were drifting apart in ways that were, as Ben put it, "unacceptably inexplicable." The model could no longer be trusted, and no one on the internal team had the bandwidth or the distance from it to fix it.
Compounding this, Simon AI was navigating a Series D requiring clean, defensible financial records and detailed due diligence packages. The records needed for investor review hadn't always been properly stored, and the accounting books alone weren't sufficient to reconstruct them. Simon AI needed a team that could operate across the full stack: accounting, FP&A, and capital markets support, all at once.
The Solution
South Bouquet's onboarding was immediate. The number one reason Ben chose the team was what he called their "near instantaneous" time to value. It took very little time to walk them through the business before they were already identifying the right problems and starting to solve them. Within days, the team had diagnosed that the legacy model wasn't salvageable, it needed to be rebuilt from the ground up, not patched further.
A first draft of the new model was delivered within a week. It was built with a command of both the accounting layer and the operational dynamics of Simon AI's business, the kind of model that only gets built when the people constructing it understand the numbers and the business context behind them. From there, the team iterated quickly until the model was fully operational and trustworthy.
While the full operational model was being finalized, South Bouquet implemented a 13-week cash flow model to give Ben and the leadership team reliable near-term visibility. It required minimal ongoing effort from the internal team and served as a critical bridge into near term cash reserves.
On the capital side, South Bouquet went well beyond standard accounting support. They helped reconstruct historical records that had never been properly stored, built out customer ARR waterfalls, and compiled the due diligence packages needed for the Series D. This required going beyond the books entirely, piecing together information from operational sources and applying finance judgment to present it in a form that investors could evaluate.
The Results
More Than Accounting Expertise
What set South Bouquet apart, in Ben's view, wasn't just that they could close the books, it was that they could think across the entire financial stack. Working with accounting-only support had always felt limiting: a small leadership team focused on business outcomes doesn't want to be constrained to "two-dimensional problems." South Bouquet's hybrid background in both accounting and finance meant they could bridge from the ledger to the operating model to the boardroom without missing a step.
The working relationship itself was equally distinctive. Ben didn't experience the engagement as a consultative one. He described the team as embedded, highly responsive, available for late-night texts, and present in the work the way a full-time team member would be, just not physically in the weekly meetings. They treated the business like their own.
The Simon AI case demonstrates something important: even companies with experienced finance leadership and an existing function in place can reach a point where external expertise is the most effective path forward. Operational debt accumulates quietly. Models age out. Capital processes strain internal capacity. South Bouquet's value isn't limited to building from zero, it's equally suited to companies that already have something, but need it to be better.
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