Getting useful charts out of spreadsheet data has always been more friction than it should be. Polymer and Bricks both try to fix this with AI, but they take very different approaches — one replaces the spreadsheet as a visualisation tool, the other enhances it from within. Which one makes sense depends almost entirely on your workflow.
What Polymer Is
Polymer is a standalone dashboard tool designed around a simple premise: upload a CSV, and the AI does the rest. It analyses your data automatically, identifies the most meaningful dimensions and metrics, and generates an interactive dashboard with charts, filters, and a summary view — without you having to specify what to visualise. The result is shareable as a live link, with filters that let recipients slice the data themselves.
The AI layer in Polymer is most visible in the setup stage: the auto-generated dashboard gives you a reasonable starting point based on what the AI identifies as significant in your data. You can customise from there — add or remove charts, change chart types, adjust filters — but the initial output requires no configuration, which is Polymer’s primary selling point.
The limitation is that Polymer works from uploaded snapshots. If your underlying data updates, you need to upload a new file to refresh the dashboard. For data that changes frequently, this creates a recurring manual step that may offset the time savings elsewhere.
| Polymer | Bricks | |
|---|---|---|
| Core concept | Upload data; AI auto-generates a browsable, filterable dashboard | Build spreadsheet-native visualisations with AI assistance inside Google Sheets |
| Where it lives | Standalone web app — separate from your spreadsheet | Google Sheets add-on — visuals live inside your spreadsheet |
| AI role | Analyses data automatically; builds initial dashboard without instruction | AI suggests chart types; helps interpret data; generates narratives |
| Interactivity | ✅ Filters, drill-downs, cross-filtering built in | ⚠️ Charts are interactive but within spreadsheet constraints |
| Sharing | ✅ Share as a live, filterable dashboard link | ⚠️ Share via Google Sheets sharing — recipients need Sheets access |
| Setup effort | Very low — upload and explore | Low — install add-on; charts linked to sheet data |
| Data stays live | ⚠️ Snapshot on upload; re-upload to refresh | ✅ Charts update when underlying sheet data changes |
| Best for | One-time dashboards and shareable reports from static data | Ongoing charts embedded in working spreadsheets that update automatically |
What Bricks Is
Bricks is a Google Sheets add-on that brings AI-assisted chart building and narrative generation inside your existing spreadsheet. Rather than exporting data to a separate tool, you build visuals directly in Sheets with AI help: describe what you want to see, and Bricks generates the chart from your sheet data. It also provides an AI narrative feature that writes a plain-English interpretation of what a chart shows — the “so what” that most charts leave the reader to figure out themselves.
The key advantage of Bricks is live data connection. Because the charts live inside your spreadsheet and reference your sheet data directly, they update automatically when the underlying data changes. For teams that maintain ongoing operational reports in Google Sheets — weekly sales data, monthly financial summaries, regularly updated metrics — this means charts are always current without any manual refresh.
Bricks is also the lower-friction option for teams already working in Google Sheets. Adding an add-on to an existing tool requires less workflow change than adopting a separate dashboard application, which matters for teams where tool adoption is a genuine concern.
The Sharing Difference
This is often the deciding factor. Polymer dashboards can be shared as a standalone link that anyone can view and filter without a Google account or Sheets access — which makes them genuinely easy to share with external stakeholders, clients, or colleagues who don’t use the same tools as your team. Bricks charts live inside a Google Sheet, which means sharing them requires sharing the Sheet — with all the access management overhead that involves.
If the main purpose of your visualisation is internal (team members who all have Sheets access), Bricks’ live-updating approach is more practical. If you regularly share reports externally, Polymer’s shareable link is meaningfully easier to work with.
🎯 Pick the Right Tool for Your Use Case
AI Quality in Practice
Both tools are genuinely useful, but the AI elements work differently. Polymer’s AI is strongest at the setup stage — the automated dashboard generation removes the blank-canvas problem of traditional chart building. Once the initial dashboard is generated, you’re mostly working with a conventional chart editing interface rather than ongoing AI interaction.
Bricks’ AI is more conversational and persistent — you can ask follow-up questions about your data, request chart modifications in plain English, and get narrative explanations of what a chart shows. For users who want ongoing AI interaction with their data rather than a one-time automated setup, Bricks’ approach may feel more useful day-to-day.
Data Volume and Performance
Both tools handle typical business datasets comfortably — spreadsheets with thousands of rows generate dashboards quickly in Polymer and charts in Bricks without noticeable lag. For larger datasets, Polymer’s performance may degrade depending on the plan, and there are row limits on some tiers. Bricks works within Google Sheets’ own data limits, which are generous for most business use cases but finite. For very large datasets — hundreds of thousands of rows — you’d want to aggregate the data before uploading to either tool rather than working with the raw export.
Customisation and Branding
If the output is going to clients or external stakeholders, branding matters. Polymer allows some customisation of dashboard appearance — colour themes, logo upload on paid plans — which makes the output look more polished and less like a generic tool output. Bricks charts inherit Google Sheets’ chart styling, which you can customise to match brand colours. Neither tool produces the pixel-perfect branded dashboards you’d get from a dedicated BI tool like Tableau or Looker, but for internal reporting and straightforward client-facing summaries, both produce professional-looking results without design overhead.
One practical consideration: Polymer dashboards are hosted by Polymer, which means the link requires their service to stay available. Bricks charts live in your Google Sheet, which is under your own control. For long-lived reports that need to remain accessible for months or years, keeping the output in a tool you control has an advantage over relying on a third-party hosting service for the link to remain valid.
The Right Tool Is the One You’ll Actually Use
Both Polymer and Bricks are genuinely good at what they do, and both are meaningfully better than building charts manually without AI assistance. The comparison that matters isn’t which tool scores higher on a feature list — it’s which one reduces friction for the specific visualisation tasks your team actually performs regularly. A tool you use consistently for the right job creates compounding value; a more powerful tool you use sporadically does not. Try both on your most frequent use case, and let the experience on real data make the decision for you.
The visualisation landscape for spreadsheet users is improving quickly. Features that require specialist tools today are becoming available in mainstream applications like Google Sheets and Excel. Revisiting this comparison in six months is worthwhile — the gap between what dedicated tools like Polymer and Bricks offer versus what’s built natively into spreadsheets is narrowing steadily.
Which One to Try First
If you have a specific CSV you want to turn into a shareable dashboard quickly: start with Polymer. Upload your file and see what it generates in two minutes. If you primarily work in Google Sheets and want better charts that stay current: try Bricks. Install the add-on and run the AI chart generator on your existing data. Both have free tiers adequate for a meaningful evaluation. The one that fits your most common visualisation task is the right choice — and you’ll know within an hour of testing it on your real data.