Reddit asked, Ramp Labs answered: 5 insights from the team behind Ramp Sheets

Alex Stauffer and Alex Shevchenko from the Ramp Labs team — an experimental group within Ramp’s Applied AI team — recently joined r/Ramp for a live AMA to answer questions about their most viral project yet: Ramp Sheets. They explained how the tool works, how the team built it, and where the product is heading.

In true Reddit fashion, someone also asked the team to settle the debate over whether to fight 100 duck-sized horses or one horse-sized duck. Ramp Sheets responded the only way it knows how: by generating a structured economic model comparing both scenarios.

Here are five big insights from the AMA:

1. Ramp Sheets can build complete, multi-factor models from a single prompt

Ramp Sheets is an AI-native spreadsheet editor that can build models, clean data, write formulas, and even search the web. Designed like “Cursor for Excel,” the tool features the standard Excel interface with an editor, but with the AI’s reasoning made visible to the user. You can upload any Excel file or start with a blank workbook, type what you want in plain English, and Ramp Sheets cleans the data, builds new tabs, writes formulas, pulls in web data, and formats everything while keeping the file fully editable.

A joke that’s become a staple of Reddit AMAs for years ended up becoming an impressive demonstration of Ramp Sheets' reasoning and modeling power: Would you rather fight 100 duck-sized horses or one horse-sized duck? The two Alexes took the question and asked within Ramp Sheets which option would be best. Sheets generated a full economic decision model that compared both options across dozens of variables.

The model included combat attributes, health points, attack power, movement speed, medical costs, calorie burn, psychological impact, emotional guilt, time efficiency, and even an estimate of story value at parties. It produced a side-by-side comparison table, calculated total economic cost, estimated probability of victory, and provided a final recommendation.

Example stats from the model

Scenario A: One horse-sized duck

• Height: 5.5 feet
• Weight: 1,100 pounds
• Estimated health points: 500
• Medical costs: $2,500
• Therapy sessions required: 3

Scenario B: 100 duck-sized horses

• HP per horse: 8
• Total enemy HP: 800
• Medical costs: $400
• Therapy sessions required: 1
• Emotional guilt factor: $100

Comparison table

Sheets' final recommendation

Despite the giant duck offering legendary story value, the economically rational choice is the 100 duck-sized horses.

Takeaway

Sheets is able to interpret ambiguous prompts, translate them into structured models, run multi-step reasoning, evaluate alternatives, and present a clear recommendation. This is the same capability that allows Sheets to turn messy CSV files and vague prompts into detailed financial models.

2. Ramp Sheets is based on real finance workflows and user behavior

Before building Sheets, Ramp Labs watched hours of Loom recordings from Ramp’s own accounting and finance team. The team noticed something consistent across every workflow: no matter the system or process, everything eventually returned to a spreadsheet.

Takeaway

Sheets does not try to replace spreadsheets. Instead it makes them more intuitive, helping to save time when building complex models, assisting with functions like bookkeeping, forecasting, error detection, and so much more.

3. Sheets uses multiple frontier models to leverage the strengths of each tool

Spreadsheet reasoning requires spatial awareness, formula logic, and the ability to navigate multiple tabs and cell references. No single model does this perfectly, so Sheets uses a mix of models based on task. Ramp Sheets runs on an OpenAI Agent SDK powered by a frontier LLM. Behind the scenes, a smart evaluation engine performs the kinds of actions a human would: adding formulas, dragging them across cells, checking the results, and updating the Excel view in real time. And when calculations go beyond what Excel can natively handle, the system uses a code interpreter to generate the right output.

Takeaway

Model specialization improves reliability and accuracy. Sheets chooses the right model for the job instead of relying on a single general model.

4. Accuracy comes from Excel-native structures and checks that protect formulas and references

One of the most popular questions was how Sheets keeps AI from breaking formulas or corrupting tabs. The team explained how Sheets is built to keep spreadsheets stable, predictable, and correct.

Takeaway

Sheets respect the fundamental rules of spreadsheets. It preserves formulas, references, and tab structures, while using guardrails to prevent errors before the user ever sees them.

5. Real world use cases are broad, from FP&A to wedding planning

Reddit asked what people are actually building with Sheets today. The answers were both expected (think: finance work) and surprising!

Takeaway

Sheets adapts to whatever structure a workflow requires. It is equally comfortable cleaning a messy CSV, building a 13-week cash forecast, analyzing vendor spend, or planning a major life event.

A highly versatile tool

Ramp Sheets is designed for the people who build models, analyze data, reconcile accounts, and think through problems in spreadsheets every day. The AMA showed how Sheets automates work that normally takes hours and transforms vague instructions into clean, structured, multi-tab analysis. And yes, it can produce a risk-adjusted cost/benefit analysis for a hypothetical battle involving a giant duck.All of the examples boil down to one simple idea: Ramp Sheets is making advanced financial analysis accessible in seconds. And the best is still yet to come!