Sequel
Data Science

AI Data Analyst
for Data Science

Explore model performance, feature distributions, and dataset quality in plain English — so your team spends time on models, not SQL.

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Use cases

Every data science question, answered instantly.

Model drift to feature correlations — ask naturally and get the statistical insight you need right now.

Show me the distribution of model prediction errorsWhat features correlate most with churn?How is our model performing in production?Which data quality issues need attention?Show me feature importance rankingsWhat's the bias in our training data?How has model accuracy changed over time?Show me the confusion matrix for last weekWhich segments have the highest prediction uncertainty?What's the precision/recall tradeoff for threshold 0.6?Show me the distribution of model prediction errorsWhat features correlate most with churn?How is our model performing in production?Which data quality issues need attention?Show me feature importance rankingsWhat's the bias in our training data?How has model accuracy changed over time?Show me the confusion matrix for last weekWhich segments have the highest prediction uncertainty?What's the precision/recall tradeoff for threshold 0.6?
Which experiments are statistically significant?What's the p-value for our A/B test?Show me data drift over the past monthHow many missing values are in our dataset?Show outliers in the user behavior dataWhat's the Gini coefficient for our model?Which training samples are most influential?Show me lift curves for our propensity modelWhat's the false positive rate by segment?How does the model perform on the holdout set?Which experiments are statistically significant?What's the p-value for our A/B test?Show me data drift over the past monthHow many missing values are in our dataset?Show outliers in the user behavior dataWhat's the Gini coefficient for our model?Which training samples are most influential?Show me lift curves for our propensity modelWhat's the false positive rate by segment?How does the model perform on the holdout set?
Show me label distribution in our training dataWhat's the SHAP value breakdown for top features?Which features have the most missing values?Show correlation heatmap for our feature setHow has data volume changed week over week?Show me label distribution in our training dataWhat's the SHAP value breakdown for top features?Which features have the most missing values?Show correlation heatmap for our feature setHow has data volume changed week over week?
Model monitoring

Know when your models start to drift

Production models degrade silently. Sequel lets any team member — not just MLEs — ask 'how has model accuracy changed this week?' and get a real answer with a chart, not a ticket.

  • Track accuracy, precision, and recall across model versions
  • Detect feature distribution shift before it hits users
  • Compare performance across population segments
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Experimentation

Ship experiments faster with instant analysis

Stop waiting for a stats-heavy notebook. Ask Sequel for the p-value, lift, and confidence interval of any experiment — and get a clear, shareable answer your stakeholders will actually understand.

  • Statistical significance testing via natural language
  • Lift, confidence intervals, and sample size analysis
  • Compare multiple variants in a single query
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Capabilities

Built for data science teams

Model Performance Monitoring

Track accuracy, precision, recall, and F1 over time across model versions, segments, and deployment environments.

Data Drift Detection

Ask whether feature distributions have shifted since model training — and get a statistical breakdown by column.

Feature Importance & SHAP

Explore which features drive predictions and how their influence has changed across model iterations.

Experiment Tracking

Query experiment results, compare model variants, and surface statistically significant differences — without writing any stats code.

A/B Test Analysis

Get p-values, confidence intervals, and lift estimates for any running experiment with a plain English question.

Data Quality Auditing

Surface missing values, unexpected nulls, schema mismatches, and outliers across your datasets instantly.

Dataset Exploration

Understand distributions, correlations, and label balance across any dataset — before you start modeling.

Ad-hoc Query Builder

Write complex analytical queries in plain English and get the SQL, result, and chart all in one response.

FAQ

Frequently asked questions

Our ML models write predictions to a database — can Sequel analyze those directly?

Yes. If your model outputs, scores, or prediction logs are stored in a SQL-accessible database or warehouse, Sequel can query them directly. Ask for prediction error distributions, per-segment accuracy breakdowns, or threshold sensitivity analysis in plain English — no additional pipeline required.

Is Sequel suitable for real statistical analysis, or just basic reporting?

Sequel generates the SQL your database engine needs to compute statistical results — aggregations, percentiles, variance, and conditional distributions. For analysis that requires custom ML computations or iterative model fitting, your Python or R environment is still the right tool. Sequel excels at the exploratory and monitoring layers that don't require custom code.

Can data scientists use Sequel alongside Python or R notebooks?

Absolutely. Many data scientists use Sequel to quickly explore dataset properties or check model outputs before loading data into a notebook for deeper analysis. The SQL Sequel generates can also be copied directly into your workflow as a starting point, saving time on query construction.

Can we track model drift over time without building a custom monitoring pipeline?

If your feature values and predictions are logged to a database, Sequel can query them to compare distributions across time windows. Ask 'how has the distribution of feature X changed month over month?' or 'what's our average prediction confidence by week?' without any additional instrumentation.

How do we share model performance reports with non-technical stakeholders?

Sequel produces charts alongside every result — bar charts, line graphs, tables — that non-technical stakeholders can read without understanding the underlying query. You can share results directly or embed them in reports. This bridges the gap between your modeling work and the business context your stakeholders care about.

How does Sequel handle queries on very large ML datasets?

Sequel pushes computation to your database engine — it never moves your data. On columnar warehouses like Snowflake, BigQuery, or ClickHouse, analytical queries over hundreds of millions of rows complete in seconds. Query performance scales with your database infrastructure, not with Sequel.

Can Sequel help with experiment analysis for A/B tests on ML models?

Yes. If your experiment assignments and outcomes are in your database, Sequel can compute conversion lift, p-values, confidence intervals, and per-segment breakdowns in a single question. You can compare model variants side-by-side and surface statistically significant differences without writing a stats notebook.

Stop writing SQL for data exploration

Connect your data warehouse and ask your first model question in under two minutes.

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