GenAI Data Copilot: Get Instant Insights Through Natural Language

Tired of waiting weeks for data insights? This GenAI Data Copilot turns natural language into powerful database queries — delivering business answers in seconds.

Why Use This Copilot?

In mid and large sized enterprises, when executives or senior leadership need enterprise data for decision making, even simple questions like:

• “How can we reduce customer churn?”

• “What product lines are more scalable?”

…takes weeks to answer. Here’s what typically happens:

1. A user sends an email or creates a ticket requesting data.
2. It takes days or weeks until an analyst has time to respond.
3. There’s back-and-forth to clarify the question.
4. The analyst queries the company databases using technical languages like SQL.

The whole process can take weeks, delaying decisions.

Even when a report already exists, users frequently struggle to find what they need:

“How do I actually use this?”

They wonder while staring at a confusing dashboard.

Bottom line: users often wait weeks for data or get lost in poorly designed reports—wasting time and delaying decisions.

Having seen this problem repeatedly in my years of BI consulting, I built this GenAI copilot to test whether conversational AI could finally solve the ‘waiting for data’ bottleneck.

The data team gets powerful benefits too:

They can monitor real usage patterns – The genAI copilot has built-in capabilities to track every user prompt. For data teams responsible for building enterprise reports, this is gold: they can see exactly what users are asking for and why. This allows them to identify repeatable use cases and understand what users actually need. As a result, they can shorten development cycles and deliver relevant, user-centric reports.

GenAI Data Copilot Demo

The GenAI Data Copilot is a reusable agent that adapts to your existing stack—whether you’re using Snowflake, Databricks, Azure, or AWS.

This copilot is built on top of a demo database containing public Amazon ratings. The database resembles a typical star schema in a data warehouse: a main table (fact) with individual feedback ratings, and two secondary tables (dimensions): a product table and a company table.

Although it’s built on a demo database, the copilot is domain-agnostic and can be deployed across various departments—sales, finance, operations, and more.

When a user enters a prompt, such as “What is the average product ratings?”, the agent executes 4 intelligent steps, where each step builds on the previous one:

Advanced Capabilities

Follow-up questions

The agent handles natural conversation flow. For example:

• User asks: “What is the average product ratings of each company?”
• The agent replies: “Samsung has a rating of 3.5, Apple a rating of 4 etc”
• User follows-up: “How about the revenue?”
• The agent understands from previous messages that it needs to show company’s revenue.

Ambiguity Clarification

Some questions can be interpreted in multiple ways from the available data. For example:

“What is the product with the highest ranking?”

Can be answered in 2 ways: using pre-calculated product ratings, or taking every feedback rating per product and averaging it. In these scenarios, the agent doesn’t guess—it clarifies. Agent answer:

Great question! Just to clarify, when you ask for the “product with the highest ranking,” there are a couple of ways to interpret that:

• Are you looking for the product with the highest average rating from the products table (which summarizes all feedback for each product)?
• Or do you want the product that received the highest individual feedback rating from the feedback table (for example, a single 5-star review)?

Let me know which one you’re interested in, and I’ll get you the right answer. If you’d like, I can also show you the top-ranked products by company or see how rankings have changed over time. Which would you prefer?

Data Availability Detection

If the data doesn’t exist, it suggests alternatives. For example, if the user asks:

“Do customers from different regions rate our products differently?”

And there is no information about customer’s regions, the agent replies:

Unfortunately, we don’t have regional data in our database, so we can’t compare product ratings by customer location. However, we can still analyze how different companies’ products are rated overall. For example, we could:

• Compare the average feedback ratings for each company’s products to see which companies are rated highest.
• Look at how product ratings have changed over time for a specific company.

Would you like to explore company ratings overall, or see how ratings have changed over time for a particular company? Let me know which option interests you!

Complex Query Generation

Handles multi-step logic and advanced reasoning. For example:

“Are premium-priced products (top 25% by price) getting better ratings than budget products?”

Sql query created:

WITH price_quartiles AS (
    SELECT 
        product_id,
        product_price,
        NTILE(4) OVER (ORDER BY product_price) AS price_quartile
    FROM products
    WHERE product_price IS NOT NULL
),
feedback_with_quartile AS (
    SELECT 
        f.feedback_rating,
        pq.price_quartile
    FROM feedback f
    JOIN price_quartiles pq ON f.product_id = pq.product_id
    WHERE f.feedback_rating IS NOT NULL
),
avg_ratings_by_quartile AS (
    SELECT 
        price_quartile,
        AVG(feedback_rating) AS avg_feedback_rating
    FROM feedback_with_quartile
    GROUP BY price_quartile
)
SELECT 
    (SELECT avg_feedback_rating FROM avg_ratings_by_quartile WHERE price_quartile = 4) AS avg_rating_top_25pct,
    (SELECT avg_feedback_rating FROM avg_ratings_by_quartile WHERE price_quartile = 1) AS avg_rating_bottom_25pct

I built this prototype based on patterns I’ve observed across years of BI consulting. While I’ve successfully implemented it with one client, I’m now looking to pilot this approach with 2-3 additional companies to refine the methodology and prove its broader applicability.

How we can work together

If this resonated with you, we can start with a focused pilot in one department of your choice. Typical duration: 2 months, depending on internal availability and scope.

Step 1: Connect to your data stack
We integrate the agent with your existing warehouse (Databricks, AWS, Azure, etc).

Step 2: Align with your business language
Collaborate with your analysts to fine-tune responses to your business language (prompt engineer).

Step 3: Roll-out to power users.
We deploy the copilot to a real department. Based on user feedback, we adapt it to real user questions and workflows.

Step 4: Transformation Partnership (ongoing).
Successful pilots typically evolve into broader AI transformation initiatives: expanding to 3-5 departments, strategic optimization, and ongoing innovation partnerships.

Outcome?

A working GenAI copilot, deployed on your data, adapted to your organization, and tested by real users.

Interested in exploring whether this approach could transform your data access? — Let’s start with a quick intro call about your current data challenges.