BigQuery and Snowflake are on our integrations list too, and they're the right choice if you have a data team pulling AIVA's conversation data into a warehouse alongside everything else. Most small businesses don't have that team. They have a spreadsheet, and someone who already knows how to use it better than any dashboard.
Who actually asks for this
Not developers. Ops managers doing a weekly review, a business owner who wants to glance at bookings between other things, a franchise operator comparing locations without asking anyone to pull a report. The Google Sheets integration exists for the person whose data tool of choice is, honestly, a spreadsheet — and who shouldn't need to file a ticket to get one.
It's worth naming why that matters more than it sounds like it should. Most small businesses running AIVA don't have anyone whose job is "look at the data." They have an owner or a manager who does it between everything else they're responsible for, in whatever tool they already know cold. Asking that person to learn a new dashboard, a new set of filters, a new mental model just to see how many calls came in last week is real friction, even when the dashboard itself is good. A spreadsheet removes that friction entirely, because the tool isn't new — only the data in it is.
What lands in the sheet
Every conversation and booking appends as a new row, as it happens — channel, language, outcome, and the booking details if there was one. Instead of exporting a CSV on Fridays, the sheet is just current, the same way a live document is current. Open it Monday morning and the weekend's calls and chats are already sitting in it, not waiting on someone to remember to pull a report.
The row-per-conversation shape is deliberate. It would be easier, in some sense, to hand over a pre-aggregated summary — calls this week, bookings this week — but that's exactly the kind of shaping the dashboard already does well. The sheet's job is different: it's the raw material, unaggregated, so whatever question a business actually wants to ask of the data — including questions we never anticipated when we built the dashboard — can be asked directly, in a tool they already know how to pivot, filter, and chart.
What people build on top of it
Nothing exotic, which is the point. A pivot table for which hours actually get the most calls. A filtered view for whoever's doing follow-up outreach that day. A copy shared with a business partner or investor who doesn't have — or need — a login to the AIVA dashboard. It's the same data, in the one format almost everyone already knows how to manipulate.
This is also often the first step toward something more automated. A sheet that updates itself is easy to build on top of — a simple formula flags any booking without a follow-up call logged next to it, or a conditional format highlights conversations AIVA marked as escalated so nothing sits unread. None of that requires anyone on the team to know what a data pipeline is.
A few patterns come up often enough to be worth naming specifically. A clinic tracking a simple flag next to each booking for whether a follow-up reminder call happened, filled in by whoever's doing outreach that day. A multi-location retailer with one tab per branch, fed from the same underlying sheet, so a comparison across locations is a formula away rather than a request to someone else. A franchise operator building a single rolled-up view across a dozen locations' sheets using nothing more exotic than a spreadsheet function that already existed long before AIVA was in the picture.
What a week actually looks like with it running
Picture a two-location salon owner who checks the sheet every Monday morning before opening. She's not running formulas or building a report — she's scrolling the new rows from the weekend, glancing at which language each conversation happened in, and noticing that Saturday afternoon had three missed-then-resolved calls in a row, which is the kind of pattern a single weekly glance catches and a monthly summary would smooth over entirely. She flags two bookings that don't have a confirmed follow-up next to them, using a column she added herself months ago, and forwards the sheet to her business partner, who doesn't have an AIVA login and has never needed one. None of that required a request to us, a support ticket, or a new tool to learn — it required a spreadsheet she already knew how to use before AIVA existed.
How it compares to the dashboard
The dashboard is still the richer, real-time view — live KPIs, auto-categorized intent breakdowns, the kind of thing you'd want for an actual operational review. The Sheets integration isn't trying to replace that. It's for the raw rows, in a place you can sort, filter, and share yourself, without asking anyone else to run a report. It also pairs naturally with how long AIVA keeps conversation data by default — a sheet that's been quietly appending for months already functions as a longer offline history than the dashboard's live window on its own.
Choosing between Sheets, webhooks, and a warehouse
AIVA's integrations list covers a range of ways to get conversation data out, and it's worth being direct about which one actually fits a given need rather than defaulting to whichever sounds most technical. Google Sheets is the right choice when the goal is "let a person look at and manipulate the data themselves" — no engineering required, nothing to maintain. Webhooks are the right choice when the goal is "notify another system the instant something happens" — a booking landing in an internal tool, an escalation triggering an alert somewhere that isn't Sheets at all. BigQuery or Snowflake are the right choice when a business already has a data team and wants AIVA's data to live alongside everything else they track, queried the way the rest of their warehouse is queried.
These aren't mutually exclusive, and a fair number of businesses end up using more than one. A franchise operator might keep Sheets for the manager who wants a quick weekly look, while their small ops team also pipes the same events through a webhook into an internal Slack channel for anything that gets escalated. Nothing about picking one forecloses the others — they're all reading from the same underlying conversations, just shaped for different audiences.
What Sheets isn't good for
It's worth being honest about the limits here too. A spreadsheet isn't the right tool for a business doing serious volume — thousands of conversations a month will make even a well-organized sheet slow and unwieldy long before a proper warehouse would strain. It's also not real-time in the way a live dashboard is; a new row appends as a conversation happens, but a spreadsheet already open in a browser tab doesn't refresh itself the way a dashboard's live view does — you generally need to reopen or refresh the sheet to see the newest rows. And it's a read-and-manipulate surface, not a two-way integration — nothing you edit in the sheet writes back into AIVA. For anything beyond looking at, filtering, and lightly building on top of the raw conversation record, the dashboard, webhooks, or a proper warehouse connection are the better fit.
Setup
Connect a Google account and pick a spreadsheet from your AIVA dashboard settings — new or existing. Rows start appending from the next conversation onward. There's no field mapping to configure by hand and nothing to backfill manually; the columns are set up automatically to match what a conversation record actually contains, so the first row shows up looking exactly like the thousandth.
Who this is for
Small teams without a dedicated analyst, franchise or multi-location operators comparing branches by hand, or anyone who just wants "give me the data" to mean a spreadsheet they already know how to open.
See the fuller picture on the analytics page, or start free and connect your own sheet.