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Dot

Plain-English database querying with strong SQL transparency and follow-up context, but uneven insight and data-recency handling.

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Live PostgreSQLSQL tab visibilityFollow-up contextAuto charts
TL;DR — our verdictUpdated August 2026 · 12 test artifacts

Good self-serve analytics, best for users who will verify and interpret

Where it wins
  • You want non-technical users to ask plain-English questions against a live database and verify the answer with generated SQL.
  • You value follow-up context and want the tool to ask for clarification when a prompt is ambiguous.
  • You are comfortable with a result that may be a chart, a table, or text depending on the query shape.
Main limitation
  • You need the tool to proactively interpret the business meaning of every answer rather than just report the numbers.
Pricing (verified plans)
Free FreePro $180/monthTeam $720/monthEnterprise Custom
Strongest test artifacts
OUTPUT →

Our take

Dot is strong at answering live-database questions in plain English, showing the SQL, and preserving context across follow-ups. It is less reliable when the answer depends on proactive interpretation or on refusing comparisons that span data gaps, so it works best for users who are comfortable checking the result themselves.

Download video: Demo videoWatch video (streaming): Demo video0:00 — Introduction to Qeasol0:40 — Account Setup and Dashboard Overview2:05 — Connecting Data Sources4:45 — Natural Language Query Execution8:50 — Visualization and Advanced Features11:38 — Final Overview and Conclusion
Demo walkthrough of Dot’s database-chat workflow.

In-Depth Review

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

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AI Demos Team
Expert Reviewer
Verified Review

Feature-by-Feature Breakdown

Natural-Language Database Querying
Strong
▾
Test Summary
Feature tested: Natural-Language Database Querying
Result: Passed — Strong

Feature tested: Natural-Language Database Querying

Result: Passed

Verdict: Strong

Expected behavior: Dot answers business questions against a live PostgreSQL database without requiring the user to write SQL, and returns a natural-language summary with an auto-generated chart or table. The tested inputs included a 90-day customer-acquisition comparison, a customer-value ranking, and an order-status query.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): New customer acquisition fell 73%: 3 customers were created in the last 90 days versus 11 in the previous 90 days, and Dot showed a comparison chart plus the matching customer list. — input1-answer-and-comparison-chart.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): New customer acquisition fell 73%: 3 customers were created in the last 90 days versus 11 in the previous 90 days, and Dot showed a comparison chart plus the matching customer list. — input1-answer-and-comparison-chart.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): Dot returned a ranked customer table with order count, spend, and separate order/spend ranks, and summarized Rahul Sharma as the strongest all-around customer while Deepak Kulkarni was the biggest spender. — input2-main-combined-rank-table.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot returned a ranked customer table with order count, spend, and separate order/spend ranks, and summarized Rahul Sharma as the strongest all-around customer while Deepak Kulkarni was the biggest spender. — input2-main-combined-rank-table.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): Dot reported 93 total orders across seven statuses and rendered a horizontal bar chart sorted by order count. — input3-main-stage-breakdown-chart.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot reported 93 total orders across seven statuses and rendered a horizontal bar chart sorted by order count. — input3-main-stage-breakdown-chart.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Strong for direct self-serve queries: the answers were readable, accurate, and formatted with charts or tables, but the tool generally reports the number before adding business interpretation.

Dot answers business questions against a live PostgreSQL database without requiring the user to write SQL, and returns a natural-language summary with an auto-generated chart or table. The tested inputs included a 90-day customer-acquisition comparison, a customer-value ranking, and an order-status query.

text
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: New customer acquisition fell 73%: 3 customers were created in the last 90 days versus 11 in the previous 90 days, and Dot showed a comparison chart plus the matching customer list., input1-answer-and-comparison-chart.png
New customer acquisition fell 73%: 3 customers were created in the last 90 days versus 11 in the previous 90 days, and Dot showed a comparison chart plus the matching customer list.
text
Who are my best customers — the ones who order the most and spend the most?
↓
OUTPUT
Output artifact for "Natural-Language Database Querying" test: Dot returned a ranked customer table with order count, spend, and separate order/spend ranks, and summarized Rahul Sharma as the strongest all-around customer while Deepak Kulkarni was the biggest spender., input2-main-combined-rank-table.png
Dot returned a ranked customer table with order count, spend, and separate order/spend ranks, and summarized Rahul Sharma as the strongest all-around customer while Deepak Kulkarni was the biggest spender.
text
How many orders do we have at each stage right now?
↓
OUTPUT
Output artifact for "Natural-Language Database Querying" test: Dot reported 93 total orders across seven statuses and rendered a horizontal bar chart sorted by order count., input3-main-stage-breakdown-chart.png
Dot reported 93 total orders across seven statuses and rendered a horizontal bar chart sorted by order count.
Bottom Line
Strong for direct self-serve queries: the answers were readable, accurate, and formatted with charts or tables, but the tool generally reports the number before adding business interpretation.
From our researchQuery Live Databases Using Plain English with AI
SQL Transparency and Query Assumptions
Strong
▾
Test Summary
Feature tested: SQL Transparency and Query Assumptions
Result: Passed — Strong

Feature tested: SQL Transparency and Query Assumptions

Result: Passed

Verdict: Strong

Expected behavior: Dot shows the generated SQL behind a result in a separate Query tab and includes filter notes that explain date windows and ranking assumptions. In testing, this covered the 90-day acquisition comparison and the best-customers ranking queries.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): The Query tab displayed the generated SQL for both the 90-day comparison and the customer list query. — input1-query-tab-sql.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The Query tab displayed the generated SQL for both the 90-day comparison and the customer list query. — input1-query-tab-sql.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): The Query tab exposed the CTE-based SQL used to calculate customer metrics and combined rankings, along with the plain-English assumptions panel. — input2-main-query-tab-sql.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): The Query tab exposed the CTE-based SQL used to calculate customer metrics and combined rankings, along with the plain-English assumptions panel. — input2-main-query-tab-sql.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Very good transparency: the generated SQL and assumptions are inspectable, but they are one click away rather than embedded inline in the chat answer.

Dot shows the generated SQL behind a result in a separate Query tab and includes filter notes that explain date windows and ranking assumptions. In testing, this covered the 90-day acquisition comparison and the best-customers ranking queries.

text
Show all customers created in the last 90 days. How does new customer acquisition compare to the previous 90 days?
↓
OUTPUT
Output artifact for "SQL Transparency and Query Assumptions" test: The Query tab displayed the generated SQL for both the 90-day comparison and the customer list query., input1-query-tab-sql.png
The Query tab displayed the generated SQL for both the 90-day comparison and the customer list query.
text
Who are my best customers — the ones who order the most and spend the most?
↓
OUTPUT
Output artifact for "SQL Transparency and Query Assumptions" test: The Query tab exposed the CTE-based SQL used to calculate customer metrics and combined rankings, along with the plain-English assumptions panel., input2-main-query-tab-sql.png
The Query tab exposed the CTE-based SQL used to calculate customer metrics and combined rankings, along with the plain-English assumptions panel.
Bottom Line
Very good transparency: the generated SQL and assumptions are inspectable, but they are one click away rather than embedded inline in the chat answer.
From our researchQuery Live Databases Using Plain English with AI
Conversational Follow-Up Handling
Strong
▾
Test Summary
Feature tested: Conversational Follow-Up Handling
Result: Passed — Strong

Feature tested: Conversational Follow-Up Handling

Result: Passed

Verdict: Strong

Expected behavior: Dot keeps context across turns so follow-up questions can build on the previous result instead of starting over, and it asks clarifying questions when a reference is ambiguous. The tested follow-ups carried forward a top-three customer list into unpaid-order and payment-method questions.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Dot answered the follow-up using the prior top-three customer context and reported that Rahul Sharma had 1 unpaid order worth $2,199 while Deepak Kulkarni and Karan Joshi had none. — input2-followup1-unpaid-orders.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot answered the follow-up using the prior top-three customer context and reported that Rahul Sharma had 1 unpaid order worth $2,199 while Deepak Kulkarni and Karan Joshi had none. — input2-followup1-unpaid-orders.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): Dot kept the same top-three customer context and summarized their usual payment methods, while noting that Deepak and Karan each had only one order so their preference was not yet stable. — input2-followup2-payment-methods.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot kept the same top-three customer context and summarized their usual payment methods, while noting that Deepak and Karan each had only one order so their preference was not yet stable. — input2-followup2-payment-methods.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): Dot paused to ask which comparison was meant instead of guessing, offering the full order-stage breakdown versus the paid-and-pending comparison as alternatives. — input3-followup3-clarification-question.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot paused to ask which comparison was meant instead of guessing, offering the full order-stage breakdown versus the paid-and-pending comparison as alternatives. — input3-followup3-clarification-question.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Standout capability: it preserves context well and will ask before guessing when a reference is ambiguous. The weakness is that some follow-up answers become very terse, with supporting detail buried in logs.

Dot keeps context across turns so follow-up questions can build on the previous result instead of starting over, and it asks clarifying questions when a reference is ambiguous. The tested follow-ups carried forward a top-three customer list into unpaid-order and payment-method questions.

text
For the top 3 from that list — do any of them have unpaid orders?
↓
OUTPUT
Output artifact for "Conversational Follow-Up Handling" test: Dot answered the follow-up using the prior top-three customer context and reported that Rahul Sharma had 1 unpaid order worth $2,199 while Deepak Kulkarni and Karan Joshi had none., input2-followup1-unpaid-orders.png
Dot answered the follow-up using the prior top-three customer context and reported that Rahul Sharma had 1 unpaid order worth $2,199 while Deepak Kulkarni and Karan Joshi had none.
text
What payment methods do these top 3 usually use?
↓
OUTPUT
Output artifact for "Conversational Follow-Up Handling" test: Dot kept the same top-three customer context and summarized their usual payment methods, while noting that Deepak and Karan each had only one order so their preference was not yet stable., input2-followup2-payment-methods.png
Dot kept the same top-three customer context and summarized their usual payment methods, while noting that Deepak and Karan each had only one order so their preference was not yet stable.
text
Compare that to last month — same breakdown. I want to see if things have improved or got worse.
↓
OUTPUT
Output artifact for "Conversational Follow-Up Handling" test: Dot paused to ask which comparison was meant instead of guessing, offering the full order-stage breakdown versus the paid-and-pending comparison as alternatives., input3-followup3-clarification-question.png
Dot paused to ask which comparison was meant instead of guessing, offering the full order-stage breakdown versus the paid-and-pending comparison as alternatives.
Bottom Line
Standout capability: it preserves context well and will ask before guessing when a reference is ambiguous. The weakness is that some follow-up answers become very terse, with supporting detail buried in logs.
From our researchQuery Live Databases Using Plain English with AI
Time-Based Comparison and Historical Snapshot Analysis
Mixed
▾
Test Summary
Feature tested: Time-Based Comparison and Historical Snapshot Analysis
Result: Partial — Mixed

Feature tested: Time-Based Comparison and Historical Snapshot Analysis

Result: Partial

Verdict: Mixed

Expected behavior: Dot can compare time windows and reconstruct prior snapshots from order history. The tested cases included last-90-days versus previous-90-days comparisons, delivered versus cancelled share, and a current-versus-prior-month view of paid-but-pending orders.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): INPUT

Observed output: Output artifact (Image): Dot compared delivered versus cancelled orders as a share of all current orders and reported 26.9% delivered and 14.0% cancelled. — input3-followup1-delivered-vs-cancelled.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot compared delivered versus cancelled orders as a share of all current orders and reported 26.9% delivered and 14.0% cancelled. — input3-followup1-delivered-vs-cancelled.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): Dot reconstructed a month-over-month snapshot for paid-and-pending orders and reported no change: 2 orders totaling $1,690 both now and on July 9. — 3a2e58afe8db45b1b14143dea3bb0117.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot reconstructed a month-over-month snapshot for paid-and-pending orders and reported no change: 2 orders totaling $1,690 both now and on July 9. — 3a2e58afe8db45b1b14143dea3bb0117.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): Dot correctly identified May 2026 as the most recent month of order data and showed that the latest order was created on May 19. — diagnostic-latest-order-may19.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot correctly identified May 2026 as the most recent month of order data and showed that the latest order was created on May 19. — diagnostic-latest-order-may19.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): Dot reported that the most recent order month is May 2026, with 21 orders, and listed the order details sorted newest first. — diagnostic-most-recent-month-may2026.png

Input artifact: Input artifact (Text prompt): INPUT

Output artifact: Output artifact (Image): Dot reported that the most recent order month is May 2026, with 21 orders, and listed the order details sorted newest first. — diagnostic-most-recent-month-may2026.png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: It can reconstruct useful time-based views and even answer recency checks correctly, but the final month-over-month comparison was misleading because it compared July/August dates even though the tool’s own data cutoff was in May. That makes this capability useful but risky when the date window may be empty.

Dot can compare time windows and reconstruct prior snapshots from order history. The tested cases included last-90-days versus previous-90-days comparisons, delivered versus cancelled share, and a current-versus-prior-month view of paid-but-pending orders.

text
What percentage of our orders were successfully delivered vs cancelled?
↓
OUTPUT
Output artifact for "Time-Based Comparison and Historical Snapshot Analysis" test: Dot compared delivered versus cancelled orders as a share of all current orders and reported 26.9% delivered and 14.0% cancelled., input3-followup1-delivered-vs-cancelled.png
Dot compared delivered versus cancelled orders as a share of all current orders and reported 26.9% delivered and 14.0% cancelled.
text
Compare that to last month — same breakdown, I want to see if things have improved or got worse.
↓
OUTPUT
Output artifact for "Time-Based Comparison and Historical Snapshot Analysis" test: Dot reconstructed a month-over-month snapshot for paid-and-pending orders and reported no change: 2 orders totaling $1,690 both now and on July 9., 3a2e58afe8db45b1b14143dea3bb0117.png
Dot reconstructed a month-over-month snapshot for paid-and-pending orders and reported no change: 2 orders totaling $1,690 both now and on July 9.
text
What is the most recent month of order data you actually have?
↓
OUTPUT
Output artifact for "Time-Based Comparison and Historical Snapshot Analysis" test: Dot correctly identified May 2026 as the most recent month of order data and showed that the latest order was created on May 19., diagnostic-latest-order-may19.png
Dot correctly identified May 2026 as the most recent month of order data and showed that the latest order was created on May 19.
text
wht is most recent order month and show everything o g that
↓
OUTPUT
Output artifact for "Time-Based Comparison and Historical Snapshot Analysis" test: Dot reported that the most recent order month is May 2026, with 21 orders, and listed the order details sorted newest first., diagnostic-most-recent-month-may2026.png
Dot reported that the most recent order month is May 2026, with 21 orders, and listed the order details sorted newest first.
Bottom Line
It can reconstruct useful time-based views and even answer recency checks correctly, but the final month-over-month comparison was misleading because it compared July/August dates even though the tool’s own data cutoff was in May. That makes this capability useful but risky when the date window may be empty.
From our researchQuery Live Databases Using Plain English with AI

Free tier plus credit-based paid plans

Testing used the free 300-credit tier; paid plans are metered by credits.

TESTED
Free
Free
300 one-time credits, no card, full Pro feature access.
Pro
$180/month
150 credits/month, unlimited users, $1.80 per overage credit.
Team
$720/month
800 credits, SSO, row-level security, embedded Dot.
Enterprise
Custom
Self-hosted, audit logs, SLA.

A 10% annual discount was mentioned in the report.

✓ Use This If
●You want non-technical users to ask plain-English questions against a live database and verify the answer with generated SQL.
●You value follow-up context and want the tool to ask for clarification when a prompt is ambiguous.
●You are comfortable with a result that may be a chart, a table, or text depending on the query shape.
✕ Skip This If
●You need the tool to proactively interpret the business meaning of every answer rather than just report the numbers.
●You need a guarantee that every result will render as a chart; some queries return tables or text only.
●You need the tool to refuse or clearly block comparisons that fall outside the available data window without manual checking.
business-marketingother-business-marketingtextOther
In this test, Dot was connected to a live PostgreSQL database through its hosted cloud product. The report does note broader connector support, but this benchmark specifically used a live Postgres source, not CSV-only input.
Yes. Dot exposes the generated SQL in a separate Query tab, and it also shows plain-English filter notes. The SQL is not embedded directly in the chat bubble.
Yes. It retained context across the customer-ranking follow-ups and the order-pipeline follow-ups. It also asked a clarification question when a follow-up phrase like "same breakdown" could refer to more than one prior result.
No. Dot is adaptive. Some questions produced charts, some produced tables, and some follow-ups were text-only with supporting data buried in logs.
It handled several comparisons correctly, including the 90-day customer-acquisition comparison and a month-over-month snapshot query. But it also produced a plausible-looking comparison over a period after the database’s last real data, so date-recency checks still need caution.
The report states a free tier with 300 one-time credits and no card, plus paid Pro ($180/month), Team ($720/month), and custom Enterprise plans.

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