Queues for simple questions.
Analysts spend their days on one-off requests, and managers wait for answers they need today.
How did the North region do last quarter?
Ask a question about the business in plain words and get the answer from your own systems, within what you are allowed to see, with every figure sourced.
See it worked throughThe answer exists.
Getting it takes a week.
Most business questions are already answered somewhere in your systems. Getting at them means a ticket to the data team, a query and a spreadsheet nobody can check.
Numbers nobody can trace.
A figure pasted into a slide loses the query, the filters and the date it came from.
Access is all or nothing.
Either people see a whole dataset or none of it, so most are kept away from the data they need.
What the AI reads.
Every question turned into a query against your own databases, run with the asker’s permissions, and answered with its sources.
In
- Data warehouse tables
- Finance and sales systems
- Business definitions and glossary
- Access rights by person
- Existing reports and dashboards
At work
- Understand Turns the question into a precise query, using your own definitions of revenue, region and quarter.
- Check Runs it only on the data this person may see, and checks the result against published reports.
- Operate Answers in plain language, with every figure, its source and the query behind it.
Out
A plain answer. Every figure with its source and the query that produced it.
Every figure sourced.
Every limit respected.
A regional manager asks a question and gets the answer, the figures behind it and what they are not allowed to see, in one place.
5 figures used · 4 sourced · 1 withheld by access rights
| Figure | Measure | Source | Detail | Value |
|---|---|---|---|---|
| F1 | Revenue, North, Q3 | Sourced | Sales ledger · table sales_q3 · net of returns | £4.82m |
| F2 | Revenue, North, Q2 | Sourced | Sales ledger · table sales_q2 · net of returns | £4.51m |
| F3 | Gross margin, North, Q3 | Sourced | Finance mart · margin by region · agrees with the Q3 board pack | 31.4% |
| F4 | Target, North, Q3 | Sourced | Budget file FY25 v3 · approved in March | £4.70m |
| F5 | Margin by customer | Withheld | Needs finance access; not shown to this user | Not shown |
Withheld
Needs finance access; not shown to this user.
F5 · Margin by customer
Illustrative example. Region, figures and systems are fictional.
Built for governed data.
The AI answers. Your access rules still decide who sees what.
Your permissions, not ours.
Every query runs with the asker’s own access rights, so nobody sees more than they already may.
How Check works ↗Every figure shows its working.
The source table, the filters and the query sit beside each number.
How Understand works ↗Runs in your environment.
Questions, queries and data stay inside your boundary.
Deployment options ↗Same records.
Other questions.
The documents and data behind this work answer other questions too.
Why does month-end reporting take a week?
Reporting
Pull the figures together automatically, every number traced to its source.
See how →Can we trust these numbers?
Data quality
Find duplicates, gaps and records that disagree across systems.
See how →What should we have charged?
Revenue leakage
Find money that should have been charged.
See how →Who is about to leave?
Customer churn
See which accounts are drifting, and why.
See how →Bring us the questions your data team answers every week.
We’ll connect to a copy of your data and show you each one answered in plain language, with its sources.
Talk to us →LondonDiscovery AI Limited · United Kingdom
HyderabadReDscvry Technology Private Limited · India
ChennaiReDscvry Technology Private Limited · India