GET EVERYTHING YOU WILL EVER NEED
TO KNOW ABOUT YOUR DATA
Statistics As A Service
No Code • Automated Deep Pattern Recognition • Conversational AI Voice Assistant • Plug & Play
DS
C

Report

Live Stream
LIVE STREAM
205 rows
4 features
44 ms
Throughput
264.0MB/s
Latency
14ms
Quality
99.8%
Data Quality
Before → After Cleaning
BeforeAfter45%98%
Outlier Detection
8 Anomalies Found
⚠ 3 outliers flagged
Pattern Discovery
Hidden Clusters Found
3 distinct patterns identified
Model Accuracy
Training Progress
100%75%50%96%Epochs →

Data Cleaning

Ready
customer_id
transaction_date
amount
currency
region
referral_code
device_type
session_duration
Drop (1)

Univariate

Ready
Mean
32.45
Median
33.53
Std Dev
4.12
Transaction_ValueNumeric
Peak: 33.5
0.00500.001000.00

Distribution Analysis

Ready

Gaussian Fit Test

Column: Age_Demographic

Passed (p > 0.05)
μ = 45
184580+

Outlier Detection

Ready
Mean
425.20
Std Dev
12.5
Anomaly
3
IDValueZ-Score
Row 8429,203.4+4.2σ
Row 10512.1-3.8σ

Bivariate Analysis

Ready
X-AXISMarketing_Spend
vs
Y-AXISRevenue_Q4
Pearson Correlation
+0.87

Customer Segmentation

Ready
3
Groups
High Value
Freq > 5/mo
45%
Loyalists
Tenure > 2yr
30%
At Risk
No Activity 30d
25%

Time Series Analysis

Ready
Decomposition: Multiplicative
Original
Trend
Seasonality

K-Means Clustering

Ready
Inertia: 452.1

Classification

Ready
Model
Accuracy
98.2%
F1 Score
0.97

Confusion Matrix

850True Positive
24False Positive
12False Negative
114True Negative

Predictive Forecasting

Ready
ARIMA
PROPHET
Forecast (30d)
+24.5%
High Confidence

Deep Chat Assistant

Ready
Agent Online

I've analyzed your forecasting model. The ARIMA parameters suggest a strong seasonal component in Q4. Would you like to adjust the seasonality prior?

You

Yes, set seasonality to 12 months and run the simulation again.

Running Simulation...
Type a message...
Highly abstracted and simplified demonstration of the actual application
PostgreSQL




Most Analytics Asks:

What do you want to find?

Deep Statistics Tells Out Of The Box:

What is your data trying to tell you?










01

Upload/Connect Your Data

Drag and drop your CSV, Excel file, or connect your database. We handle cleaning and type detection automatically.

Drop CSV / Excel
users.sql
sales.csv
02

Automated Statistical Analytics

Our AI scans your data for patterns, outliers, and correlations tailored to your industry (SaaS, HR, Finance, etc.).

Patterns
Outliers
Correlations
Trends
03

What Your Data Trying To Tell

We don't just show charts. We explain what they mean in plain English, highlighting the key drivers of change.

Key Insight
Revenue +45% in Q3
04

Chat/Talk With Your Data

Ask follow-up questions to dig deeper. "Why did sales drop?" or "Forecast next month."

Deep Chat
Why did churn spike in May?

Analysis indicates a correlation with the new pricing update rolled out on May 1st.

Config ChangeUser Drop-off
|
05

It Learns With You

The more you use it, the better it understands your business context and what matters to you.

Knowledge Graph Updating...

Loved by Analysts, Data Scientists, Data Engineers, & ML Engineers


Transformed how we analyze our marketing data. The AI insights are game changing.

AS

Aayush Srivastava

Data Analyst


The speed & accuracy of the anomaly detection saved, countless hours of manual review.

RP

Ritu Prajapati

Data Engineer


Finally, a tool that makes complex statistical analysis accessible to our entire team.

J

Jayashree

ML Engineer



No Code • Automated Deep Pattern Recognition • Conversational AI Voice Assistant • Plug & Play
DS
C

Report

Live Stream
LIVE STREAM
205 rows
4 features
40 ms
Throughput
260.7MB/s
Latency
13ms
Quality
99.8%
Data Quality
Before → After Cleaning
BeforeAfter45%98%
Outlier Detection
8 Anomalies Found
⚠ 3 outliers flagged
Pattern Discovery
Hidden Clusters Found
3 distinct patterns identified
Model Accuracy
Training Progress
100%75%50%96%Epochs →

Data Cleaning

Ready
customer_id
transaction_date
amount
currency
region
referral_code
device_type
session_duration
Drop (1)

Univariate

Ready
Mean
32.45
Median
33.53
Std Dev
4.12
Transaction_ValueNumeric
Peak: 33.5
0.00500.001000.00

Distribution Analysis

Ready

Gaussian Fit Test

Column: Age_Demographic

Passed (p > 0.05)
μ = 45
184580+

Outlier Detection

Ready
Mean
425.20
Std Dev
12.5
Anomaly
3
IDValueZ-Score
Row 8429,203.4+4.2σ
Row 10512.1-3.8σ

Bivariate Analysis

Ready
X-AXISMarketing_Spend
vs
Y-AXISRevenue_Q4
Pearson Correlation
+0.87

Customer Segmentation

Ready
3
Groups
High Value
Freq > 5/mo
45%
Loyalists
Tenure > 2yr
30%
At Risk
No Activity 30d
25%

Time Series Analysis

Ready
Decomposition: Multiplicative
Original
Trend
Seasonality

K-Means Clustering

Ready
Inertia: 452.1

Classification

Ready
Model
Accuracy
98.2%
F1 Score
0.97

Confusion Matrix

850True Positive
24False Positive
12False Negative
114True Negative

Predictive Forecasting

Ready
ARIMA
PROPHET
Forecast (30d)
+24.5%
High Confidence

Deep Chat Assistant

Ready
Agent Online

I've analyzed your forecasting model. The ARIMA parameters suggest a strong seasonal component in Q4. Would you like to adjust the seasonality prior?

You

Yes, set seasonality to 12 months and run the simulation again.

Running Simulation...
Type a message...
Highly abstracted and simplified demonstration of the actual application