What Is NLQ in BI? Examples in English and Hindi | FireAI

For most people in a business, data has always been locked behind a skill they do not have. To get a number, you needed to know SQL, learn a BI tool's filters, or ask someone who did. Natural language queries remove that barrier: if you can ask a colleague the question, you can ask questions to your data the same way.

In India, the barrier is also a language one. In the 2011 Census, 43.6% of Indians reported Hindi as their first language, while only about 10.6% reported speaking English at all, whether as a first, second or third language (Census 2011 language data). A BI tool that only understands English leaves most of a company's field team out. This guide explains what NLQ is, how it works, gives example questions by department in English and Hindi, and covers where it works well and where it does not.

What is a natural language query?

A natural language query is any request for information written or spoken the way people normally talk. In business intelligence, NLQ means typing or speaking a question into an analytics tool and getting an answer from company data.

Method	Example
SQL	SELECT region, SUM(net_amount) FROM sales WHERE month = '2026-08' GROUP BY region;
BI filters	Open the sales report, set the date filter to August, add region to rows, sum net amount
Natural language query	"Show August sales by region"

All three return the same result. Only one needs no training. That difference decides who in a company can use data: with SQL it is a handful of analysts, with filters it is people who were trained on the tool, and with natural language it is anyone who can describe what they want to know.

How do natural language queries work?

1. Understanding. A language model reads the question and identifies the metric (sales), the dimension (region), the time period (August) and any filters.
2. Mapping to data. The system matches those terms to tables and columns, often using a semantic layer where the business has defined "sales", "region" and the fiscal calendar.
3. Query generation. It writes a database query, usually SQL. This step is called NL to SQL, and the component is often sold as a text to SQL AI tool.
4. Execution and display. The query runs and the tool picks a suitable chart or table.
5. Conversation. The tool remembers context, so a follow-up like "now only for the top 5 distributors" works without repeating the whole question. This is what people mean by conversational analytics.

15 natural language query examples by department

These are the kind of natural language query business data users ask every week.

Sales

• "Which distributors had falling sales three months in a row?"
• "Compare primary and secondary sales for Maharashtra this quarter."
• "Top 10 SKUs by growth in the South, last 90 days."

Finance

• "Show receivables older than 90 days by customer."
• "What is our DSO this month compared with last quarter?"
• "Which cost heads are more than 10% over budget?"

Operations and supply chain

• "Which SKUs will stock out in the next 2 weeks at current sales?"
• "Average delivery time by transporter for September."
• "Which warehouse has the most expired stock?"

Marketing and e-commerce

• "Return rate by courier partner this month."
• "ROAS by campaign on Meta vs Google in the last 30 days."
• "How many Diwali 2025 customers have ordered again?"

Leadership

• "Why did gross margin drop last month?"
• "Summarise this week's performance against target."
• "Which region needs attention right now?"

Notice the questions that start with "why". Answering them needs more than a query: it needs root cause analysis. FireAI answers them with Causal Chain, which traces the drivers behind a change.

Natural language queries in Hindi and regional languages

Many sales managers, distributors and business owners think and speak in Hindi or a regional language. Examples of business questions in Hindi:

• "पिछले महीने पुणे में कितनी बिक्री हुई?" (How much did we sell in Pune last month?)
• "कौन से डिस्ट्रीब्यूटर का पेमेंट 90 दिन से ज़्यादा बाकी है?" (Which distributors have payments pending for more than 90 days?)
• "इस हफ्ते सबसे ज़्यादा बिकने वाले 5 प्रोडक्ट दिखाओ" (Show the top 5 selling products this week)

People also mix languages. A question like "Last month ka sales region wise dikhao" is normal in many offices, so test mixed Hindi-English questions during any demo. See multilingual analytics and Hindi analytics dashboards.

NLQ vs search vs chatbots

	Natural language query in BI	Keyword search in BI	General chatbot (e.g. ChatGPT)
Understands full questions	Yes	Partly, keywords only	Yes
Connected to live company data	Yes	Yes	No, unless files are uploaded
Applies user permissions	Yes	Yes	No
Consistent metric definitions	Yes, with a semantic layer	Yes	No
Handles follow-up questions	Yes	Rarely	Yes

How to ask better natural language questions

NLQ tools work best when questions are specific. Five habits make a big difference:

1. Name the metric. "Net sales" is clearer than "business".
2. Give a time period. "Last month", "this quarter" or "since 1 April" removes guessing.
3. Say how to slice it. Add "by region", "by distributor" or "by SKU".
4. Set a limit. "Top 10" or "more than 90 days" keeps the answer readable.
5. Follow up instead of starting over. Ask "now only for Gujarat" rather than retyping the whole question.

Compare "how are we doing?" with "net sales by region this quarter vs last quarter". The second gets a precise answer the first time.

Benefits of natural language queries

• Anyone can get data. No SQL, no BI training.
• Answers in seconds. No waiting in the analyst queue.
• More questions get asked. When asking is free, managers explore instead of guessing, and follow-up questions uncover causes a fixed report would miss.
• Analysts focus on hard problems instead of repeat report requests.
• Wider adoption of BI, including field teams and non-English speakers.

Limits of natural language queries

• Ambiguity. "Best customers" can mean several things. Good tools ask a clarifying question.
• Undefined business terms. Without agreed definitions, "sales" may be calculated differently from your MIS.
• Complex analysis. Multi-step statistical work still benefits from an analyst.
• Trust. Users should be able to see how an answer was calculated. Look for tools that show the underlying query or logic, so a finance controller can check a number before it goes into a board pack.

For a direct comparison, read natural language queries vs SQL.

How to choose an NLQ tool

1. Test with 20 real questions your team asked last month and compare answers with your MIS.
2. Check that it connects directly to your sources (Tally, ERP, CRM, Shopify).
3. Check support for Hindi and the other languages your teams use, including mixed-language questions.
4. Confirm it applies row-level security to every answer.
5. See whether it can answer "why" questions, not just "what".

Natural language queries in FireAI

FireAI is a causal decision intelligence platform built by Steeplogic Systems in India. With Ask FireAI, teams ask questions in 90+ languages across 700+ connected data sources, including Tally, ERPs and POS systems. Answers come back as charts and tables, and "why" questions are answered with Causal Chain. It works on the web and on FireAI's iOS and Android mobile app. Book a demo.

Related reading

• NL to SQL: how it works and where it fails
• Mobile BI: what it is and how CXOs use it
• AI in business intelligence
• Why NLQ is the future of BI

Sources

• Census of India 2011 language data (summarised on Wikipedia, Multilingualism in India)