“Basedash lets the whole team pull the data they need and spot trends instantly, without writing a single query.”

Bolna is a voice AI platform built for India. Founded in 2024 and backed by Y Combinator, Bolna lets businesses build, test, deploy, and scale conversational voice agents that handle over a million inbound and outbound calls daily across 22 Indian languages. More than 2,500 companies rely on it to power customer support, cart recovery, collections, and recruitment calls at scale.

Running voice AI at that volume generates an enormous amount of operational data. Every call, agent, campaign, and customer interaction produces signal, and the team needs to study all of it — high-level trends across the business and low-level details on a single workflow — without turning every question into an engineering task.

“We’re processing a huge volume of calls. The only way to keep improving is to actually look at what’s happening across all of them, quickly.”

Before Basedash

In the early days, getting answers out of that data meant writing queries. Anyone who wanted to understand how a campaign was trending, how agents were performing, or where a workflow was breaking down had to either know SQL or wait on someone who did.

That created a bottleneck. The cost of pulling data was high enough that a lot of useful questions simply never got asked — a familiar trade-off at a fast-moving startup, and an expensive one for a team whose product improves by studying real conversations.

Why Basedash

Bolna chose Basedash for a few reasons that mattered specifically to how the team works. It connects to far more data sources and integrations than the alternatives they looked at, and those plug cleanly into the tools they already use — so their data came together in one place without a migration project. From there, anyone can ask a question and get an answer without writing a query.

“Basedash supports way more data sources and integrations, and they plug right into the tools we already use. It removed the hassle of writing queries just to pull data.”

Just as important, it was easy enough that the whole team could use it. Engineers, operators, and everyone in between can visualize and study both high-level and low-level trends quickly, which turned analytics from a specialist task into something everyone does for themselves.

“It’s genuinely easy to use. People can look at high-level trends and then drill into the low-level details on their own, in the same place.”

A self-serve team

The biggest shift wasn’t any single report — it was that data stopped routing through a single person. With Basedash, everyone at Bolna is self-served, which means questions get answered the moment they come up instead of piling into a queue.

That has a compounding effect on a small, fast team: less time spent pulling data for each other, and more people making decisions backed by what’s actually happening across their calls.

“Basedash improves the whole team’s productivity by making everyone self-served. That’s the part I didn’t expect to matter as much as it does.”

Impact

Since adopting Basedash, Bolna has turned analytics into something the entire team can do without engineering in the loop:

• Everyone self-served, answering their own questions instead of routing through one person
• No SQL required to pull data, so questions that used to feel too expensive now just get asked
• High-level and low-level trends studied side by side, in the same place
• Many data sources and integrations connected and plugged into the tools the team already uses
• Engineering time reclaimed for the product instead of ad-hoc data requests

“Basedash made our data self-serve. For a team moving as fast as ours, that’s the difference between guessing and knowing.”