How Snowflake and Streamlit Turned Long-Term Planning Into a Conversation

Long-term planning is one of the most important jobs in finance. It's also one of the hardest to scale. At Snowflake, we stared down a 10-year forecast for a business that had grown into a tangle of 40-plus entities, each with over 100 cost centers, and hundreds of spending categories. That level of detail isn't just for the finance team anymore. Tax needs the split between goods and services, legal entity, jurisdiction—operational details that shape their work. Treasury wants a cash view. People planning needs headcount assumptions. Executives want to see the trade-offs between growth, margins, investment, and free cash flow.

So, like many companies, when the spreadsheet broke under the weight of our own ambition, we did what finance teams do: we built a massive Excel model. It worked. It also turned into a Frankenstein. New tabs piled on, formulas were patched together, logic stacked on top of older logic. The model became more valuable and more brittle at the same time. Maintaining it, governing it, and scaling it got harder every quarter.

From spreadsheet to planning platform

About a year ago, we rebuilt that long-term planning model directly on Snowflake, with Streamlit as the UI layer. The result is Snowplan, our internal long-term planning application. The goal wasn't to build a dashboard. We wanted a real planning platform—something that felt familiar to finance users but sat on top of Snowflake's compute, governance, and scale.

In Snowplan, analysts update assumptions through an editable Streamlit interface. Those changes write straight back to Snowflake, the model runs, and new outputs appear instantly. No more broken formulas. No more emailing files back and forth. No more guessing which version is the source of truth.

That architecture changed the whole planning workflow. Instead of maintaining a giant offline workbook, we now have an app connected to actual business data sources, with governance baked in. Actuals flow in automatically—no one spends hours updating files. Assumptions are versioned, scenarios can be compared, and different roles work at their own level of granularity on the same platform.

For individual contributors and associates, Snowplan offers fine-grained input pages, assumption management, scenario creation, and version control. For directors and managers, it provides visibility into logic and assumption changes, making review and approval straightforward. For executives, it shows consolidated P&L, free cash flow, and key scenario views.

This matters because long-term planning is never just a modeling exercise. It's an organizational alignment process. The more time finance spends maintaining the model, the less time they have to actually stress-test strategy with the business.

Why building the model on Snowflake changed the model itself

The biggest decision we made was to put the model where the data already lives. Because Snowplan runs on Snowflake, it's naturally connected to our raw data sources and governed data models. We don't manually update actuals or reconcile offline pulls. The model sits in the same environment as the financial data, permissions, logic, and history.

That brings several advantages.

• Scale: A 10-year forecast spanning entities, cost centers, expense categories, headcount, revenue, balance sheet, and free cash flow generates a lot of data. Snowflake is built for that.
• Governance: Role-based permissions and row-level security control access. Executives don't see the same interface as analysts, and analysts don't need to export different versions for every stakeholder.
• Reusability: The same platform can support headcount planning, equity modeling, treasury cash forecasting, hedging, legal entity forecasting, COGS planning, and M&A scenario analysis.

That's the bigger story. Snowplan isn't a one-off planning app. It's becoming a financial planning platform.

Snowflake CoCo makes scenario planning conversational

Streamlit made Snowplan scalable and usable. Snowflake CoCo made it conversational. Before CoCo, Snowplan already gave us a better way to manage long-term planning. Analysts could update assumptions, run scenarios, and compare outputs. But users still had to know where to click, which assumption to tweak, and how to interpret downstream impacts.

CoCo changes that interaction. Instead of navigating through pages of assumptions, I can ask questions in plain English. I can ask CoCo to compare two forecast versions and summarize the key drivers. I can ask what changed between the plan we showed the board last year and the version we're preparing now. I can ask about the net impact, the drivers of margin expansion or dilution, and which assumptions deserve the most attention.

That's powerful in executive planning. When you're prepping for a board discussion, the real question isn't usually "Can you get me the latest numbers?" It's "What changed, why, and what does that mean for our narrative?" CoCo collapses what used to be a manual comparison into a conversation.

The value isn't just speed. It's that finance can keep iterating while the strategic discussion is still happening.

A real example: scenario planning around a potential tax change

One of the clearest examples is scenario planning around a potential tax change. In the past, this kind of question started with a meeting. We'd talk to tax, define affected sales, pull data, build assumptions, update the model, review outputs, create sensitivity tables, and then decide who else needed to be involved.

With CoCo in Snowplan, the process flows much more smoothly. I can ask CoCo to summarize the potential tax change. Then I can ask it to create a new forecast version that assumes the tax change goes through. It immediately turns into the kind of back-and-forth that happens in a finance meeting: Is the tax passed on to customers or absorbed as a margin hit? What percentage can realistically be passed through? Which sales are affected? What's the impact on revenue, gross margin, operating margin, and free cash flow?

Because the analysis is built on Snowflake tables, CoCo can identify which sales would be affected, output the financial impact, and show the key metrics behind the numbers. It can also create sensitivity tables showing how operating margin dilutes under different pass-through assumptions.

Just as importantly, it flags risks and caveats. For example, a first-order model might not capture the extra indirect costs of supporting filings, maintaining compliance datasets, or meeting new reporting obligations. That kind of reminder is exactly what a good finance partner would raise before treating a scenario as a conclusion.

CoCo can even help draft next steps—like an email to the tax team summarizing the analysis, key assumptions, open questions, and decision points. The system isn't just spitting out a number. It's helping frame the problem, identify the right experts, and keep the process moving.

At that point, it's not just AI-assisted modeling. It's AI-assisted planning.

Why this matters for finance teams

Finance teams are constantly asked to answer strategic questions faster than traditional planning cycles allow. What if we accelerate growth? What if we open a new office? What if cloud costs improve by 25 basis points? What if compensation inflation runs hotter than expected? What if a tax or regulatory change hits a portion of sales? What if we reallocate investment across functions?

These aren't hypotheticals. Executives throw them out in real time. The problem is that traditional planning tools and giant spreadsheet models weren't designed for that level of iteration. They're built to produce a plan, not to support an ongoing strategic conversation.

By building Snowplan on Snowflake with Streamlit, we created a planning platform that scales with business complexity. Adding CoCo turned it into a conversational system. That combination changes what finance can actually do. Instead of spending time updating actuals, maintaining formulas, reconciling scenarios, or manually comparing versions, the team spends more time on the work that matters: challenging assumptions, aligning executives, weighing trade-offs, and shaping long-term strategy.

Trust is the foundation of AI-driven planning

For conversational planning to work in finance, the numbers have to be trustworthy. That's why architecture matters. CoCo isn't generating a forecast out of thin air. It's interacting with the same governed data, assumptions, and logic that power Snowplan. When it compares versions, explains drivers, or creates scenarios, it's working from the Snowflake data models and planning logic we already use.

Every scenario is versioned, every change can be reviewed, and access controls follow the app's role model. Analysts and executives can compare before and after, understand what changed, and roll forward or roll back when needed. This is a critical distinction. We're not asking leaders to trust a black box. We're using AI to operate a well-governed planning platform where data, business logic, permissions, and outputs are visible, explainable, and auditable.

That's what makes AI viable for corporate finance.

From planning tool to strategic platform

The most exciting part of Snowplan is that it's outgrown its original use case. Once the model was on Snowflake, the architecture became reusable. The same foundation already supports—or could support—multiple financial planning workflows: headcount planning, equity modeling, treasury cash forecasting, hedging, legal entity forecasting, COGS planning, and M&A scenario modeling.

That's the payoff of building a platform instead of a one-off app. Each new planning workflow reuses the same governance backbone, connects to the relevant data sources, and exposes a finance-friendly Streamlit interface. With CoCo, each workflow becomes easier to question, adjust, and explain through natural language.

I suspect more finance teams will follow a similar path: first, move models to where the data lives; second, build an intuitive app layer for users; third, use AI to make planning conversational.

The real ROI of Snowplan isn't making finance more technical. It's giving time back to judgment. Long-term planning shouldn't be about maintaining a massive workbook. It should help teams understand where the business is going, help leaders decide where to invest, how to balance growth and profitability, which risks are emerging, and which trade-offs matter most.

Snowflake and Streamlit gave us a platform that makes long-term planning scalable, governable, and connected to real-time data. CoCo is making it faster, more interactive, and more strategic. That's the shift. Finance teams can spend less time updating models and tweaking assumptions, and more time iterating with executives on the company's long-term strategy.

For FP&A teams, that's where the real value of AI in planning lies. It's not about replacing finance. It's about removing the manual labor that slows finance down, so the team can focus on what it should actually be doing.