Natural Language Query
Your data has answers. But SQL is a barrier, analysts are bottlenecks, and “my numbers don't match yours” is a daily conversation.
“Speak what we feel, not what we ought to say.
The data exists. Getting to it is the problem.
Every organisation has data. But between the question and the answer sits SQL complexity, analyst queues, and definitions nobody agrees on.
"I've got 47 dashboards bookmarked but none of them answer my actual question. Every time I need something specific, I submit a ticket and wait 3 days."
You know the data exists. You can see it in six different dashboards, none of which show exactly what you need. You could answer the question yourself if you knew SQL, but you don't, and learning it isn't your job.
What if the insight you need is sitting in a database right now, but by the time you get it the decision will already be made?
"My CRM shows different numbers than finance. In every review meeting, we spend the first 20 minutes arguing about whose numbers are right."
You know your team is performing. But every time you try to prove it, someone questions your data. "Where did that number come from?" "That's not what I'm seeing." You've given up trying to bring your own analysis.
What if you're making decisions based on numbers that nobody actually trusts?
"We have five different definitions of 'revenue' across the business. Every month I spend two days reconciling numbers before the board meeting."
Sales says revenue is $4.2M. Operations says $3.9M. The CFO asks why you can't just give them a straight answer. You can, but first you need everyone to agree what "revenue" even means.
What if the board loses confidence because your numbers keep changing based on who you asked?
"I spend 60% of my time running the same queries with slight variations for different managers. I should be finding insights, not being a human SQL translator."
Your job title says "Analyst" but you're really a request queue. Same questions, different filters. You've written documentation nobody reads. You've built dashboards nobody uses. They just keep emailing you.
What happens to your career if you never have time to do actual analysis?
The cost of data inaccessibility
This isn't just frustrating. It's expensive: analyst time, decision delays, and the opportunities you miss while waiting.
Nearly three-quarters of employees don't have direct access to the data they need to make decisions.
Average wait time for an ad-hoc data request. Too slow for a fast-moving business.
Analysts spend 80% of their time preparing data. Only 20% on actual analysis.
Large organisations lose millions annually to decisions made with inconsistent or untrusted data.
The real problem isn't the data
You've invested in data infrastructure. You've hired analysts. You've bought BI tools. But most of your organisation still can't answer their own questions, because there's a skill barrier (SQL) between them and the answers they need.
Ask questions in English. Get answers with transparency.
Natural language query removes the SQL barrier while adding governance. Everyone uses the same definitions. Every query is logged. Every answer shows its working.
Plain English queries
“What were our top 10 customers by revenue last quarter?” No SQL knowledge needed. The system understands intent, not just keywords.
“Show me churn risk by segment” works even if you don't know the table names, join syntax, or exact column definitions.
Full transparency
Every answer shows the SQL that ran, the rows matched, and the execution time. Nothing hidden. Everything verifiable.
Answer: “$4.2M”. Click to expand and see the exact query, 847 rows matched, 0.3 seconds to run.
From question to answer in seconds
Behind the simple interface is enterprise-grade governance: governed metrics, access controls, audit logging, and safety limits.
Ask in plain English
Type your question naturally. "What were our top 10 customers by revenue last quarter?" No SQL required.
The system understands intent, not just keywords. "Top customers" and "biggest accounts" mean the same thing.
Business users query data directly. No waiting for analysts.
Map to governed metrics
AI translates your question into SQL using your organisation's approved metric definitions.
"Revenue" always means the same thing: gross sales minus returns and discounts, as defined by finance. Not open to interpretation.
Consistent definitions. No more "my numbers don't match yours."
Execute safely
Queries run read-only with automatic row limits, timeouts, and cost caps.
Full table scans blocked. DELETE/UPDATE impossible. Even if someone types something dangerous, the system refuses.
Zero risk of accidental data modification or runaway queries.
Show the working
Results include the generated SQL and matched rows. Click to expand and see exactly how the answer was calculated.
Answer: "$4.2M", with expandable section showing the exact query, 847 rows matched, 0.3 second execution time.
Full transparency. CFO asks "how did you get that?" Click and show them.
Log everything
Every query is logged with user, timestamp, question, SQL, and results.
Compliance review: "Show me all queries touching customer data in the last 90 days", instant report with full context.
Complete audit trail. Ready for any compliance or governance review.
Data access that actually works
Not another dashboard nobody uses. Not another BI tool with a learning curve. Actual answers, instantly, for the people who need them.
Analysts get their time back. The routine queries that filled their inbox are now self-service.
One finance team reduced analyst ad-hoc requests from 40/week to 4/week within the first month.
Questions that took 3 days now take 30 seconds. Decisions happen while the context is still fresh.
Sales managers pull their own pipeline data in real-time instead of waiting for weekly reports.
Every query uses the same definitions. Finance, sales, and operations finally agree on the numbers.
Board reporting now uses the same metrics as operational dashboards, because they come from the same place.
Every query logged with full context. Know exactly who accessed what data and when.
Compliance teams can produce data access reports in minutes instead of days.
Objections we hear, and how we address them
Every answer shows the generated SQL. Users can see exactly what query ran. More importantly, the system uses your governed metric definitions, so "revenue" can't accidentally mean something different. And it's read-only, so a bad query can't hurt anything.
Natural language query works best for common questions with established patterns, which is exactly the 80% of queries that consume analyst time. Complex analysis still needs analysts. This frees them up to do it.
The opposite. This enforces governance. Every query uses centrally-defined metrics. Every query is logged. Every query respects access controls. It's more governed than emailing an analyst who writes ad-hoc SQL.
BI tools answer the questions you anticipated when building dashboards. Natural language query answers the questions you didn't anticipate. They're complementary: dashboards for monitoring, NLQ for exploration.
Related capabilities
Natural language query integrates with these complementary solutions.