AI answers that use your definitions, not its guesses.
Ask in plain English. Get the right number, with the query shown.
- Runs in your cloud
- Your choice of LLM
- Follows your access rules
Question"How many active customers did we have in September?"
About 1.24 million
✕Guessed "active" means anyone who logged in, and counted test accounts
842,300
1+ transaction in last 90 days, excl. test & staff✓Your team's definition, with the query shown
Dashboards show what changed. Hyperanalyst finds out why.
Choose a question to see how it works through it.
- 1You askin plain English
- 2It investigateschecks each likely cause
- 3You get the answerwith the evidence
Connecting Claude or ChatGPT to your data takes a day. Making it trustworthy takes months.
That's the part we do, so your data team doesn't have to.
Capturing your business context
Analysts spend weeks writing table notes and metric definitions by hand.
Definitions are drafted from your data and the work your team has already done. Your analysts approve them.
Knowing the answer is right
Every number gets rechecked by an analyst before anyone uses it.
Each answer says what it counted, in plain words. Your most-asked questions get verified answers.
Keeping it right
Saved answers go stale quietly when tables or definitions change.
Hyperanalyst spots changes in your data and definitions, flags answers that have quietly drifted, and updates them once your team approves.
Your data team keeps control. We take on the heavy lifting.
Know what happened, why it happened, and the moment it happens again.
Quick answers and dashboards for the what. Research mode for the why. Alerts for when it changes.
The deep-dive, done in minutes.
For questions where the first answer only leads to the next. Your analysts start from a finished first draft and take it further.
- Checks every likely cause, not just one
- Rules out holidays, one-offs and mix shifts
- A report with charts, caveats and next steps
9,420 Spark card customers qualify for the November 4x dining offer, 27% of the base. At 30% uptake the reward cost is about ₹1.4 L. 64% have never used the card for dining, so this works as a new-category push.
INSIGHTThe tenure rule is the bigger filter: only 43% of customers pass it.
Everyday questions, answered in seconds.
Type the question the way you'd ask a colleague.
- A chart and a one-line answer
- The SQL behind it, one click away
- Follow-up questions in the same thread
17,360 accounts across the top 5 states. Maharashtra has the most.
What I counted: loan accounts 90 or more days past due, excluding test accounts, as of today.
SELECT state, COUNT(*) AS accounts FROM loans.accounts WHERE days_past_due >= 90 AND is_test_account = FALSE GROUP BY state ORDER BY accounts DESC LIMIT 5;
Dashboards you describe in a sentence.
"Build me a daily collections view by region."
- Built from your approved metrics
- Change any chart by asking
- Refreshes on a schedule
Alerts that come with a reason.
Pick the KPIs to watch. When one moves, you hear about it first.
- How big the change is
- The most likely driver
- One click to a full research report
- HighUPI success rate fell to 94.1% (usually 97.5–98.2%)09:40Likely driver: one issuing bank accounts for 62% of failures since 08:15.Open research →
- MediumOnline card spend in Pune down 17% vs the last 4 TuesdaysYesterdayLikely driver: two large merchants had no transactions after 14:00.
- InfoNew-card activations up 12% this weekMonMost of the rise comes from the festive co-brand campaign.
Built for the questions your industry actually asks.
Up and running in weeks, not months.
We do the heavy lifting. Your team's main job is to sign off how each metric is calculated.
Connect
We set it up inside your cloud, on your warehouse, with your LLM.
Sign off your definitions
Hyperanalyst drafts how each metric is calculated from your data. Your analysts review and sign off.
Start narrow, then expand
One team starts with its most-asked questions. Once the numbers hold up, others follow.
Common questions
We've already connected Claude to our warehouse. Why would we need this?
Keep using it. Claude connected to your data is a capable assistant. What it doesn't come with is your business context, a way to check answers, or upkeep when your data changes. Hyperanalyst does that work, and it can run on Claude as its model.
How is this different from the text-to-SQL tool we already have?
Text-to-SQL turns a question you already know how to ask into one query and one number. Hyperanalyst also handles open questions: research mode decides what to look at, tests the likely explanations and writes up what it found. It also builds dashboards and watches your KPIs.
Does it replace our analysts?
No. It takes the first pass on routine and repeat questions so your analysts spend their time on judgement, not pulling numbers. They also stay in charge of the definitions it uses.
Does the AI make up its own definitions?
No. Key metrics use definitions your team has approved. If a question needs something that isn't defined yet, the answer says so and shows the logic used, so your analyst can approve or correct it.
What gets sent to the LLM?
Only aggregated, anonymised results, never raw customer or transaction records. You can check exactly what is shared, and with your own LLM the model stays inside your environment.
What data can it connect to?
The major cloud warehouses and databases, including Snowflake, Databricks, BigQuery, Redshift, Athena, Postgres, Oracle and ClickHouse. We add new sources quickly.
How long does it take to get started?
Usually weeks, not months. The exact time depends on the size and shape of your data, so we scope it with you first. Most teams start with one use case so they see useful answers early.
Your data stays where it is.
Runs in your cloudYour data doesn't leave your environment.
Your choice of LLMUse the model you already govern.
Your access rulesPeople only see data they're allowed to.
Full audit logWho asked what, and when.
See it on data that looks like yours.
A 30-minute walkthrough on sample data from your industry.