# I Built a Chipotle Dashboard in 4 Minutes With MatterFact

> I asked MatterFact to build a full Chipotle research dashboard with peer comps, podcast-sourced bull/bear cases, valuation analysis, and scenario modeling. It took 4 minutes.


> **TL;DR** — Most equity research dashboards at investment firms take weeks to build, days to update, and are a pain to share. I gave MatterFact a single natural language prompt and got back a complete Chipotle research artifact: price action relative to peers, bull and bear cases sourced from real podcast citations, a full fundamental comp table across the QSR peer group, and forward-looking scenario analysis. Four minutes, zero code, no BI tools, no engineers.

## How most research dashboards get built at investment firms

If you have ever exceeded the capacity of Excel with your financial models, we know how you feel. The next logical step may have been to ask your data or engineering team for a custom dashboard. After some time they come around to it, scope the work, and create a ticket. An engineer needs to pull the data or write a scraper, clean it, store it, model it, and push it into Tableau or Looker. Weeks (or months) after your initial request you get something close to what you asked for. Unfortunately the market has moved on and the opportunity is no longer there, you are now looking at a different name.

These workflows are a relic of the pre-AI era. They are a remnant of a world where data engineering was expensive and compute was limited. In 2026, neither of those things is true and you can go from idea to reality in seconds. Sometimes it feels like magic.

I wanted to test whether MatterFact could compress the entire cycle of idea → product into minutes with something we call "Artifacts". I wanted to build an equity research dashboard for a public company using only natural language. I knew I needed to see performance, peer comps, most recent bull and bear cases with real source citations. I needed some fundamental analysis to see if there was value relative to other companies and I also was curious to see some scenario modeling.

I chose Chipotle ($CMG) because the name is hotly debated in hedge fund circles. There are strong opinions on both sides of CMG right now and the data to support either case is scattered across earnings calls, sell-side notes, and hundreds of podcast conversations most analysts will never hear.

## What I asked for

I typed this prompt into the MatterFact platform:

*Build an interactive artifact for CMG. First, show me recent price action relative to the market and companies similar to Chipotle in the fast food restaurant space. I want to see how CMG was performing against its peer group.*

*Second, give me the best bull and bear case, and tell me who has made each argument and in which podcast it was mentioned.*

*Third, build a general dashboard so I can understand Chipotle's financials with a comp analysis across its peer group. Focus on valuation metrics, operating KPIs, the things that fundamental analysts look at when building a position.*

*Fourth, give me the best drivers of forward-looking returns and some scenario analysis and what-if modeling.*

That was it. One prompt. The agent went to work and what it produced was impressive.

## Price action and peer comparison

The first section of the dashboard gave me CMG's recent stock performance plotted against the broader market and its QSR peer group, which it chose automatically for me. You can immediately see how Chipotle was trading relative to McDonald's, Yum Brands, Restaurant Brands International, Wingstop, Cava, Sweetgreen, and the other names that institutional analysts track in the space, and it gave me the ability to turn off some names to focus on the group I care about.

![CMG price action vs the S&P 500 and the QSR peer group](/assets/images/blog/cmg-price-action.webp)

This would take a while in MS Excel or even Bloomberg, not because the data is hard to find, but because pulling the peer set, normalizing the time series, and formatting it into something presentable takes time.

## Bull and bear case with podcast citations

This is where it gets interesting.

The agent didn't produce generic bull and bear arguments from a language model. It went into MatterFact's [podcast intelligence engine](https://www.matterfact.com/blog/why-podcasts-why-now), scanned hundreds of relevant episodes, and pulled the strongest recent arguments from each side. All cited and sourced. You can see exactly who made each argument, on which podcast, and when.

![Chipotle bull and bear case with podcast citations](/assets/images/blog/cmg-dashboard.webp)

On the bull side, you see operators and analysts talking about menu pricing power, unit economics durability, digital penetration, and international expansion optionality. On the bear side, you hear arguments about BOGO-driven brand dilution, commodity headwinds from beef inflation running 7-20% across the sector, and the question of whether same-store sales growth can sustain at current valuation multiples.

This is the kind of research that used to cost thousands in expert network calls and two weeks of scheduling. The AI agent assembled it in seconds, and every claim is traceable to a real human expert speaking on a real podcast. These are all real people making specific arguments in actual conversations recorded live.

For analysts preparing a pitch to their portfolio manager, this is the section that can really help prep you. You can walk into the investment committee meeting with the counterarguments already in your back pocket, ready to defend.

```request-access
variant: inline
heading: Want to see the bull/bear engine on a name you cover?
description: Matterfact mines podcast intelligence across your peer group and cites every claim. Request access to point it at your coverage.
buttonText: Request access
```

## Fundamental comp analysis

The third section is the core of equity research: a full comp table across the QSR and fast casual peer group, organized around the metrics fundamental analysts rely on.

Valuation multiples like EV/EBITDA, P/E, and P/FCF across the peer set. Operating KPIs: same-store sales growth, restaurant-level margins, average unit volumes, new unit economics, digital mix. Balance sheet metrics: net debt/EBITDA, ROIC, free cash flow conversion.

![Same-store sales growth comparison across the QSR and fast casual peer group](/assets/images/blog/cmg-same-store-sales.webp)

This is the table every analyst covering restaurants has in their model, and it is the one they spend the most time maintaining. Every earnings season they have to update three dozen cells by hand. The dashboard builds it from scratch in minutes, across the full peer group in one view, and pulls in fresh data when it's out.

## Scenario analysis and forward-looking drivers

The dashboard also includes scenario modeling and what-if analysis for Chipotle's forward-looking returns.

What happens to the stock if same-store sales growth decelerates by 200 basis points? What if beef inflation persists through the back half of 2026? What if digital mix continues to expand and drives operating leverage? What if the international opportunity in Europe and the Middle East starts to contribute meaningfully to unit growth?

## Beyond Chipotle

This is one dashboard for one company built in a few minutes from a single natural language prompt. The same approach works for any public company in any sector, or anything an analyst could imagine.

Give MatterFact a ticker and a set of questions and it will build you a research artifact that combines market data, peer analysis, podcast-sourced intelligence, and scenario modeling. You can build custom screeners, risk dashboards, macro analysis, news aggregators. You are no longer limited by tech, only your imagination.

See the Chipotle dashboard for yourself: [app.matterfact.com/artifacts/chipotle-dashboard](https://app.matterfact.com/artifacts/chipotle-dashboard?owner=stan@acadia.im).

What company would you build a dashboard for? Tell us at [matterfact.com](https://www.matterfact.com) and we will build one for you. Or, give it a spin yourself.

## Built with MatterFact

This dashboard was built live inside the MatterFact platform using a single natural language prompt. No code, no BI tools, no engineering support. The platform combined market data, peer analysis, podcast intelligence from hundreds of episodes, and scenario modeling into a complete equity research artifact in under four minutes.

```request-access
heading: Build your dashboard.
description: Matterfact is deployed with select institutional partners. Request access to build a research artifact on the names you cover.
buttonText: Request access
```