From Question to Answer in 10 Seconds: How Klairr Works

You Have a Question. Klairr Has the Answer.

You are the VP of Sales. It is Monday morning. You need to know how last quarter went before your 10 AM leadership meeting. Specifically, you want to know: what were your top 10 deals last quarter?

In the old world, you would open Salesforce, export a CSV, sort by deal size, cross-reference with finance’s numbers, realize the data does not match, Slack the data team, and hope someone responds before your meeting. That process takes hours on a good day and days on a bad one.

In Klairr, you type: “What were our top 10 deals last quarter?”

Ten seconds later, you have your answer. Here is exactly what happens in those ten seconds.

Step 1: Understanding Your Question

The moment you submit your question, Klairr’s natural language engine parses it to understand precisely what you are asking.

“What were our top 10 deals last quarter?” breaks down into:

• Entity: deals (maps to the opportunities or deals table in your data)
• Metric: implicit — deal value/revenue
• Sort: descending by value
• Limit: 10
• Time range: last quarter (the system calculates the exact date range based on today’s date)

This is not keyword matching. The system understands the intent behind your words. If you had asked “Show me the biggest deals we closed in Q1” or “Which deals brought in the most revenue last quarter,” you would get the same answer. The phrasing is flexible. The interpretation is precise.

Step 2: Applying AI Memory

Before generating any query, the system consults your organization’s AI Memory — the knowledge layer that stores your company’s specific terminology, metric definitions, and data conventions.

AI Memory might contain entries like:

• “Deals” refers to the opportunities table where stage = 'Closed Won'
• “Deal value” uses the contract_value column, not gross_amount
• “Last quarter” means fiscal quarter, and your fiscal year starts in February
• Data hint: Exclude rows where is_test = true

These definitions ensure the system interprets your question the way your company means it, not the way a generic AI might guess. (To learn how AI Memory works in depth, see What Is AI Memory — and Why Your BI Tool Needs It.) Every user in your organization benefits from the same definitions, so the VP of Sales, the CFO, and a new hire all get the same answer to the same question.

Step 3: Selecting the Data Source

Klairr connects to multiple data sources — BigQuery, Mixpanel, and others. When you ask a question, the system’s multi-source intelligence determines which source has the data you need.

For a question about deals, the system identifies that your opportunities data lives in BigQuery, in a specific dataset and table. If your question spanned multiple domains — say, “How do our top deals correlate with product usage?” — the system could pull deal data from BigQuery and usage data from Mixpanel, combining them into a single answer.

You never have to specify where the data lives. The platform knows.

Step 4: Generating the Query

With the question parsed, AI Memory applied, and the data source selected, Klairr generates a precise query. For your question, it might look something like this:

SELECT
  account_name,
  opportunity_name,
  contract_value,
  close_date,
  owner_name
FROM `your_project.sales.opportunities`
WHERE stage = 'Closed Won'
  AND is_test = false
  AND close_date BETWEEN '2026-02-01' AND '2026-04-30'
ORDER BY contract_value DESC
LIMIT 10

Notice what AI Memory contributed: stage = 'Closed Won' (your definition of “deals”), contract_value (your definition of “deal value”), the fiscal quarter date range, and the test row exclusion. Without AI Memory, the system would have to guess at each of these, and any guess could produce wrong results.

This query is not hidden. It is shown to you alongside the answer. If you are technical, you can read it and verify the logic. If you are not, you can skip it and trust the answer — the confidence score tells you whether that trust is warranted.

Step 5: Executing the Query

The generated query runs against your BigQuery instance in real time. This is not a cached result from last night’s data refresh. It is a live query against your current data. The results are the freshest numbers your data warehouse has.

Klairr applies execution guardrails automatically: result limits to prevent runaway queries, DML blocking to ensure the system can only read data (never modify it), and byte caps to keep response sizes manageable. These protections happen at the infrastructure level, independent of the AI, so they cannot be bypassed.

Step 6: Returning Your Answer

The query results come back and Klairr assembles your answer. You see several things at once.

The answer itself. A clear, formatted table showing your top 10 deals with account name, deal name, contract value, close date, and owner. The data is sorted by value, largest first, exactly as you asked.

The confidence badge. In this case, “High” — the question mapped cleanly to your data schema, AI Memory provided clear definitions, and the query executed without issues. If the confidence were lower, you would see an explanation of what the system was uncertain about.

The data source. A label showing that this answer came from BigQuery, from a specific dataset. You know exactly where the numbers originated.

The query. The full query is available for inspection. Data team members can review the logic, copy it, or modify it directly using the query editor.

The raw data. The underlying result set is available if you want to explore beyond the formatted answer.

Every piece of this answer is citable. When someone in your leadership meeting asks “Where did you get that number?”, you show them the source, the query, and the confidence level. This is not a number from a gut feeling or a hastily assembled spreadsheet. It is a grounded, traceable answer.

Step 7: The Follow-Up

Your meeting starts. The CEO looks at the top 10 deals and asks: “Can you break that down by region?”

You do not start over. You do not file a new request. You type a follow-up in the same conversation: “Break that down by region.”

Klairr maintains conversation context through threads. It knows “that” refers to your top 10 deals from the previous question. It generates a new query that adds a region grouping, runs it, and returns the breakdown — still scoped to last quarter, still excluding test rows, still using your fiscal calendar.

The follow-up takes another few seconds. You have your regional breakdown before the CEO finishes their coffee.

You keep going. “Which region had the highest average deal size?” “How does that compare to the same quarter last year?” “Show me the win rate by region.” Each follow-up builds on the previous context, creating a chain of grounded answers that together tell a complete story. No tickets. No waiting. No context lost between questions.

What Just Happened

Here is what the platform accomplished in those ten seconds.

1. Parsed a natural language question into structured intent
2. Applied organizational knowledge (AI Memory) to resolve terminology
3. Selected the correct data source from multiple connected systems
4. Generated a precise, optimized query
5. Executed the query against live data with safety guardrails
6. Assembled a formatted answer with confidence scoring and full transparency
7. Maintained context for follow-up questions

Every step is designed to be fast, transparent, and grounded. No hallucinated numbers. No black-box answers. No guessing.

And every step is logged in the GRC audit trail, so there is a complete record of what was asked, what was queried, and what was returned. For compliance-conscious organizations, this is not optional. It is essential.

Try It Yourself

The best way to understand how Klairr works is to try it. Connect a data source, type a question in plain English, and see the answer — with the query, the confidence score, and the raw data — in seconds.

No training required. Anyone can ask a question in plain language. No dashboards to build first.

Start with Klairr for free and go from question to answer in 10 seconds.