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Definite

AI Data Platform · NL2SQL · AI Analyst · Dashboard Builder · June 2026

nl2sqlai-data-analystdata-platformbusiness-intelligencedashboard-toolai-analyticsdata-visualizationsql-aiecommerce-analyticsoperations-analyticsfoundersoperations-teamsnon-technical-users
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Our take

Definite is strongest when a chat result needs to become a reusable dashboard. It can create dashboard-style outputs with KPI cards, charts, multiple views, and filters instead of keeping the result only inside the chat. The trade-off is speed and simplicity. The flow feels heavier than AskYourDatabase and Basedash because visualizations are not shown directly inside the chat. Definite also missed part of the intent in the best-customer query by ranking only on spend instead of balancing spend and order frequency.

Download video: Demo video
Hands-on walkthrough of Definite answering ecommerce database questions, handling follow-up analysis, creating dashboard-style outputs, and showing where its business reasoning works well or misses query intent.

In-Depth Review

Our detailed analysis of Definite — features, performance, and real-world testing.

AG
Ayush Ghosh
AI Demos Team
Verified Review

Feature-by-Feature Breakdown

Natural Language Database Querying
Good — accepts plain English database questions and returns structured answers, but can miss part of a multi-intent query.
7.2/10
▾
Test Summary
Feature tested: Natural Language Database Querying
Result: Partial (7.2/10) — Good — accepts plain English database questions and returns structured answers, but can miss part of a multi-intent query.

Feature tested: Natural Language Database Querying

Result: Partial (7.2/10)

Verdict: Good — accepts plain English database questions and returns structured answers, but can miss part of a multi-intent query.

Expected behavior: Definite lets users ask database questions in plain English and get table-based answers without writing SQL manually.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite correctly understood the natural language query and returned a customer acquisition comparison. It showed: * Last 90 Days: 13 customers * Previous — definite-natural-language-database-querying-1.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite correctly understood the natural language query and returned a customer acquisition comparison. It showed: * Last 90 Days: 13 customers * Previous — definite-natural-language-database-querying-1.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite accepted the question and returned a readable customer table, but the interpretation was incomplete. The query asked for customers who: * order the m — definite-natural-language-database-querying-2.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite accepted the question and returned a readable customer table, but the interpretation was incomplete. The query asked for customers who: * order the m — definite-natural-language-database-querying-2.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Definite can answer plain English database questions, but the artifact is worth checking because it shows both sides: simple queries are handled clearly, while multi-intent questions can be narrowed without clarification.

Definite lets users ask database questions in plain English and get table-based answers without writing SQL manually.

QUERY
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
OUTPUT
Output artifact for "Natural Language Database Querying" test: Definite correctly understood the natural language query and returned a customer acquisition comparison. It showed: * Last 90 Days: 13 customers * Previous, definite-natural-language-database-querying-1.png

Definite correctly understood the natural language query and returned a customer acquisition comparison. It showed: * Last 90 Days: 13 customers * Previous 90 Days: 22 customers * Change: -40.9% It also listed customers created in the last 90 days with name, email, phone, and created date.

QUERY
Who are my best customers — the ones who order the most and spend the most?
↓
OUTPUT
Output artifact for "Natural Language Database Querying" test: Definite accepted the question and returned a readable customer table, but the interpretation was incomplete. The query asked for customers who: * order the m, definite-natural-language-database-querying-2.png

Definite accepted the question and returned a readable customer table, but the interpretation was incomplete. The query asked for customers who: * order the most * spend the most Definite ranked customers mainly by total spend. Order count appeared as a column, but it was not used as a real ranking factor.

Bottom Line
Definite can answer plain English database questions, but the artifact is worth checking because it shows both sides: simple queries are handled clearly, while multi-intent questions can be narrowed without clarification.
AI Business Commentary
Strong — adds business-friendly observations that make outputs easier to understand.
8/10
▾
Test Summary
Feature tested: AI Business Commentary
Result: Passed (8/10) — Strong — adds business-friendly observations that make outputs easier to understand.

Feature tested: AI Business Commentary

Result: Passed (8/10)

Verdict: Strong — adds business-friendly observations that make outputs easier to understand.

Expected behavior: Definite adds natural language commentary around database results, including observations, caveats, and business meaning.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite found that Rahul Sharma had one unpaid order worth $2,199. It also added useful business commentary that the unpaid order had been sitting confirmed bu — definite-ai-business-commentary.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite found that Rahul Sharma had one unpaid order worth $2,199. It also added useful business commentary that the unpaid order had been sitting confirmed bu — definite-ai-business-commentary.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Definite does not only return rows. The artifact is worth checking because the commentary gives business context, like acquisition clustering and overdue unpaid-order risk, which a raw SQL table would not explain by itself.

Definite adds natural language commentary around database results, including observations, caveats, and business meaning.

QUERY
For the top 3 from that list — do any of them have unpaid orders?
↓
OUTPUT
Output artifact for "AI Business Commentary" test: Definite found that Rahul Sharma had one unpaid order worth $2,199. It also added useful business commentary that the unpaid order had been sitting confirmed bu, definite-ai-business-commentary.png

Definite found that Rahul Sharma had one unpaid order worth $2,199. It also added useful business commentary that the unpaid order had been sitting confirmed but unpaid since April 2025 and was worth following up.

Bottom Line
Definite does not only return rows. The artifact is worth checking because the commentary gives business context, like acquisition clustering and overdue unpaid-order risk, which a raw SQL table would not explain by itself.
Follow-Up Question Handling
Mixed — follow-up context is retained, but follow-ups can inherit the first answer’s narrowed interpretation.
7.5/10
▾
Test Summary
Feature tested: Follow-Up Question Handling
Result: Partial (7.5/10) — Mixed — follow-up context is retained, but follow-ups can inherit the first answer’s narrowed interpretation.

Feature tested: Follow-Up Question Handling

Result: Partial (7.5/10)

Verdict: Mixed — follow-up context is retained, but follow-ups can inherit the first answer’s narrowed interpretation.

Expected behavior: Definite lets users continue analysis through follow-up questions and uses previous context to answer the next query.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite retained the context from the previous customer list and checked unpaid orders for the selected top customers. It found Rahul Sharma had one unpaid ord — definite-follow-up-question-handling-1.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite retained the context from the previous customer list and checked unpaid orders for the selected top customers. It found Rahul Sharma had one unpaid ord — definite-follow-up-question-handling-1.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite reused the same customer context and returned payment methods for the selected customers. It also connected Rahul Sharma’s unpaid order with his paymen — definite-follow-up-question-handling-2.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite reused the same customer context and returned payment methods for the selected customers. It also connected Rahul Sharma’s unpaid order with his paymen — definite-follow-up-question-handling-2.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Definite can maintain follow-up context, but the artifact is worth checking because it shows an important risk: when the first interpretation is narrowed, later follow-ups can continue from that narrowed scope.

Definite lets users continue analysis through follow-up questions and uses previous context to answer the next query.

QUERY
For the top 3 from that list — do any of them have unpaid orders?
↓
OUTPUT
Output artifact for "Follow-Up Question Handling" test: Definite retained the context from the previous customer list and checked unpaid orders for the selected top customers. It found Rahul Sharma had one unpaid ord, definite-follow-up-question-handling-1.png

Definite retained the context from the previous customer list and checked unpaid orders for the selected top customers. It found Rahul Sharma had one unpaid order and explained the issue in a business-friendly way.

QUERY
What payment methods do these top 3 usually use?
↓
OUTPUT
Output artifact for "Follow-Up Question Handling" test: Definite reused the same customer context and returned payment methods for the selected customers. It also connected Rahul Sharma’s unpaid order with his paymen, definite-follow-up-question-handling-2.png

Definite reused the same customer context and returned payment methods for the selected customers. It also connected Rahul Sharma’s unpaid order with his payment method behavior and suggested it may be an oversight rather than a repeated pattern.

Bottom Line
Definite can maintain follow-up context, but the artifact is worth checking because it shows an important risk: when the first interpretation is narrowed, later follow-ups can continue from that narrowed scope.
Dashboard Generation
Strong — turns chat answers into reusable dashboard-style outputs with multiple views and charts.
8.6/10
▾
Test Summary
Feature tested: Dashboard Generation
Result: Passed (8.6/10) — Strong — turns chat answers into reusable dashboard-style outputs with multiple views and charts.

Feature tested: Dashboard Generation

Result: Passed (8.6/10)

Verdict: Strong — turns chat answers into reusable dashboard-style outputs with multiple views and charts.

Expected behavior: Definite lets users generate a separate dashboard/doc from chat results, making the analysis reusable instead of leaving it as a one-time response.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite created a separate dashboard for the customer acquisition result. The dashboard included: * Trend view * Comparison view * Recent customer list * — definite-dashboard-generation-1.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite created a separate dashboard for the customer acquisition result. The dashboard included: * Trend view * Comparison view * Recent customer list * — definite-dashboard-generation-1.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite created a separate order pipeline dashboard/doc. The dashboard included: * Orders By Stage * Orders By Month * KPI cards * donut/pie chart * — definite-dashboard-generation-2.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite created a separate order pipeline dashboard/doc. The dashboard included: * Orders By Stage * Orders By Month * KPI cards * donut/pie chart * — definite-dashboard-generation-2.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: This is Definite’s strongest feature. The artifact is worth checking because the output becomes a reusable dashboard with views and filters, not just a temporary chart inside chat.

Definite lets users generate a separate dashboard/doc from chat results, making the analysis reusable instead of leaving it as a one-time response.

QUERY
Can you visualize it?
↓
OUTPUT
Output artifact for "Dashboard Generation" test: Definite created a separate dashboard for the customer acquisition result. The dashboard included: * Trend view * Comparison view * Recent customer list *, definite-dashboard-generation-1.png

Definite created a separate dashboard for the customer acquisition result. The dashboard included: * Trend view * Comparison view * Recent customer list * KPI-style cards * visual chart area

QUERY
Show me this in pie chart.
↓
OUTPUT
Output artifact for "Dashboard Generation" test: Definite created a separate order pipeline dashboard/doc. The dashboard included: * Orders By Stage * Orders By Month * KPI cards * donut/pie chart *, definite-dashboard-generation-2.png

Definite created a separate order pipeline dashboard/doc. The dashboard included: * Orders By Stage * Orders By Month * KPI cards * donut/pie chart * comparison charts * date filter controls

Bottom Line
This is Definite’s strongest feature. The artifact is worth checking because the output becomes a reusable dashboard with views and filters, not just a temporary chart inside chat.
Metric Assumption Handling
Strong — explains assumptions when a metric can be interpreted in more than one way.
8.2/10
▾
Test Summary
Feature tested: Metric Assumption Handling
Result: Passed (8.2/10) — Strong — explains assumptions when a metric can be interpreted in more than one way.

Feature tested: Metric Assumption Handling

Result: Passed (8.2/10)

Verdict: Strong — explains assumptions when a metric can be interpreted in more than one way.

Expected behavior: Definite can explain how it interprets business metrics and show caveats when the result depends on workflow assumptions.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite returned delivered vs cancelled percentages and separated the result into: * percentage of all orders * percentage of resolved-only orders It also — definite-metric-assumption-handling-1.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite returned delivered vs cancelled percentages and separated the result into: * percentage of all orders * percentage of resolved-only orders It also — definite-metric-assumption-handling-1.png

What changed: Text prompt transformed into Image

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Input

Observed output: Output artifact (Image): Definite compared April vs May and added an important caveat that May was only halfway through, so the final picture may change. — definite-metric-assumption-handling-2.png

Input artifact: Input artifact (Text prompt): Input

Output artifact: Output artifact (Image): Definite compared April vs May and added an important caveat that May was only halfway through, so the final picture may change. — definite-metric-assumption-handling-2.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: This feature is useful for business users because Definite does not silently choose one interpretation. The artifact is worth checking because it shows both the metric output and the assumptions behind it.

Definite can explain how it interprets business metrics and show caveats when the result depends on workflow assumptions.

QUERY
What percentage of our orders were successfully delivered vs cancelled?
↓
OUTPUT
Output artifact for "Metric Assumption Handling" test: Definite returned delivered vs cancelled percentages and separated the result into: * percentage of all orders * percentage of resolved-only orders It also, definite-metric-assumption-handling-1.png

Definite returned delivered vs cancelled percentages and separated the result into: * percentage of all orders * percentage of resolved-only orders It also added a note that “Completed” orders could count as a successful end state depending on the workflow.

QUERY
Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
OUTPUT
Output artifact for "Metric Assumption Handling" test: Definite compared April vs May and added an important caveat that May was only halfway through, so the final picture may change., definite-metric-assumption-handling-2.png

Definite compared April vs May and added an important caveat that May was only halfway through, so the final picture may change.

Bottom Line
This feature is useful for business users because Definite does not silently choose one interpretation. The artifact is worth checking because it shows both the metric output and the assumptions behind it.

Pricing & Access

Plans as of June 2026

TESTED
Free
$0 forever
Plan Tested. Includes 2 users, 2 connectors, Fi AI assistant, daily sync, 1 GB storage, and 5 credits/month.
Standard
$250/month
Includes unlimited users, unlimited connectors, API access, hourly sync, 10 GB storage, and 100 credits/month.
Enterprise
Custom
Includes 1,000+ credits/month, SSO, SOC 2 Type II, near real-time sync, audit logs, SCIM, dedicated support, SLA, and on-prem deployment options.
Data Team as a Service Add-on
$2,500/month
Optional add-on for senior data engineering support, connector setup, dashboard building, and data modeling. Also listed as $100/hour for ad hoc work.

Testing was completed on Definite’s 14-day free trial. Paid plan limits, credit usage, connector access, and enterprise deployment options should be verified again before publishing.

Is This Right For You?

A side-by-side guide based on our hands-on testing.

✓ Use This If
●You want chat results to become reusable dashboards.
●You need KPI cards, charts, filters, and multiple dashboard views.
●You want business commentary around order pipeline and operational metrics.
●You are comfortable moving from chat into a generated dashboard/doc.
●You want an AI analyst inside a broader data platform, not only a simple SQL chatbot.
✕ Skip This If
●You want every visualization to appear directly inside chat.
●You need the fastest and simplest NL2SQL experience for non-technical users.
●You expect the tool to always clarify multi-intent questions before answering.
●You want customer ranking logic to balance spend and order frequency automatically.
●You prefer lightweight chat responses over dashboard/app generation.

Use Case Track Record

Query Live Databases Using Plain English with AI
Business & MarketingE-commercetextFoundersMarketing
Not in the tested workflow. Visualizations required an additional prompt, and charts were created in a separate dashboard/doc instead of directly inside the chat response.
Definite is strongest at turning analysis into reusable dashboards. In testing, it created dashboard views with KPI cards, charts, filters, and multiple analysis sections.
Yes, but performance varied by workflow. Definite handled follow-ups well in the order pipeline workflow, but in the best-customer workflow, follow-ups were based on an incomplete first interpretation because the main query was ranked only by spend.
Partially. The business commentary is useful, but the separate dashboard/doc flow can feel heavier than a simple chat-based answer. It is better for users who want dashboard-style analytics.
Not overall. Definite was strongest in dashboard generation and order pipeline reasoning, but AskYourDatabase and Basedash were smoother for direct non-technical NL2SQL workflows.
Visualizations required an additional prompt, and charts were created in a separate dashboard/doc instead of directly inside the chat response. In testing, the generated dashboards included KPI cards, charts, filters, and multiple analysis sections.
Its best result came from the order pipeline workflow, where it handled multiple follow-ups, workflow assumptions, old pending-paid records, and month-over-month comparison well. Dashboard generation was also Definite’s strongest feature.
The best-customer query missed the order-frequency part of the intent. Definite ranked customers mainly by total spend, and later follow-ups continued from that narrowed scope.
Yes. Definite can explain how it interprets business metrics and show caveats when the result depends on workflow assumptions. In testing, it separated delivered vs cancelled percentages into percentage of all orders and percentage of resolved-only orders, and noted that “Completed” orders could count as a successful end state depending on the workflow.
Testing was completed on Definite’s 14-day free trial. The plan tested was the Free Plan.
Definite is strongest when the user wants a reusable dashboard, not when they want the fastest lightweight NL2SQL chat experience.

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