Customer Stories

E-commerce brand · Shopify + Neon PostgreSQL

From 4-hour reporting cycles to answers in 8 minutes

Operations Lead, 8-person DTC apparel brand (~$4M ARR)

-97% reporting time

The problem

Every Monday, the ops lead needed return rate by SKU, revenue by channel, and inventory sell-through for the weekly review. Getting those numbers meant filing a Jira ticket, waiting 2–3 days for an analyst, and receiving a CSV that was already stale by the time it arrived.

What Queryra did

Connected their Neon PostgreSQL in 90 seconds. First query asked:

Which products had the highest return rate last quarter?

Answer in 12 seconds. Chart included.

The full Monday report — five questions that used to take days — now runs in 8 minutes before the team standup.

I used to dread Mondays because I'd spend the first hour chasing the analyst for numbers I needed yesterday. Now I pull them myself before my coffee gets cold. I don't even think of it as 'querying the database' — I just ask a question.

— Operations Lead, DTC apparel brand

B2B SaaS · 140-person company · PostgreSQL on Supabase

Finance answers churn questions without touching SQL

Head of Finance, Series A SaaS (~$7M ARR)

Zero SQL required

The problem

Board prep meant the Head of Finance needed cohort churn rates, MRR movement, and expansion revenue broken down by plan tier. Engineering was stretched thin, and pulling ad-hoc financial queries from a PostgreSQL database wasn't on anyone's sprint backlog.

What Queryra did

Connected to Supabase in under 2 minutes. Queryra's schema intelligence recognized the subscriptions table automatically. Sample queries asked:

What was net MRR change last month by plan?

Answer: breakdown across Starter / Growth / Enterprise in 9 seconds.

I spent three years asking engineers for numbers that should take five minutes to produce. Now I ask Queryra directly. I ran 22 queries in the two days before our board deck was due — I would have submitted maybe four tickets and received them the week after.

— Head of Finance, Series A B2B SaaS

Performance marketing agency · MySQL + Google Sheets

11 ad-hoc queries per day. No analyst. No SQL.

Senior Marketing Analyst, 25-person performance agency

11× query frequency

The problem

Client campaign data lived split across MySQL (attribution) and Google Sheets (budgets). Reconciling them required exporting, pivoting, and a lot of VLOOKUP. The analyst could answer about one data question per day before the manual overhead became prohibitive.

What Queryra did

Connected MySQL and a Google Sheets budget tracker as two separate data sources. Questions like:

Which campaigns exceeded budget by more than 15% last week?

Answer in 8 seconds, with a bar chart already formatted for the client report.

The analyst went from answering ~1 data question per day to averaging 11 — without hiring anyone.

My old workflow was: export the MySQL data, open it in Excel, copy in the Sheets budget data, write the VLOOKUP, build the chart, clean up the formatting. That was 45 minutes per question. Queryra does it in under 10 seconds. I genuinely don't know what I was doing before.

— Senior Marketing Analyst, performance agency

Real teams querying their data in seconds

Three teams. Three problems. One tool.

From 4-hour reporting cycles to answers in 8 minutes

Finance answers churn questions without touching SQL

11 ad-hoc queries per day. No analyst. No SQL.

The analyst who had to wait days for a SQL query

Try Queryra free — no SQL required

Real teams querying their data in seconds

Three teams. Three problems. One tool.

From 4-hour reporting cycles to answers in 8 minutes

Finance answers churn questions without touching SQL

11 ad-hoc queries per day. No analyst. No SQL.

The analyst who had to wait days for a SQL query

Try Queryra free — no SQL required

See how Queryra compares