Commerce overview
Revenue$267.6KSeptember 2026 · Sample
Understand the revenue mix across markets.
Explore dashboard
AI native · Talk to your database
Your database.
Your database.
Meet your AI analyst.
Query nine supported database engines in plain English. Explore twelve business dashboards for each connection, from commerce and subscriptions to product engagement and operations. The available analysis follows your schema.
PostgreSQL · production
Ask
Query
You
Monthly recurring revenue by plan for the last 6 months?
MRR grew from $182K to $264K over
six months. Business plan drove most of the
gain, up 61%; Pro up 24%; Starter flat.
MRR by month
USD
FebMarAprMayJunJul
SELECT plan, date_trunc('month', started_at) m,
SUM(amount) FROM subscriptions
GROUP BY plan, m ORDER BY m;
SUM(amount) FROM subscriptions
GROUP BY plan, m ORDER BY m;
AI native analytics · Dashboard libraryOne connection.
One connection.
Twelve ways to see more.
Start with a business question.
Open a dashboard. See the value in your data.
Illustrative dashboards using fictional September 2026 data. Available analyses depend on the fields in your source.
Order fulfillment
Orders dispatched21.1KSeptember 2026 · Sample
Put fulfillment workload and timeliness in context.
Explore dashboardSubscription sales
Subscription revenue$194KSeptember 2026 · Sample
Focus subscription reviews on the plans that matter.
Explore dashboardPayment activity
Processed value$448.4KSeptember 2026 · Sample
Understand payment activity before reviewing costs.
Explore dashboardInventory flow
Units moved51.8KSeptember 2026 · Sample
Review the product categories driving warehouse work.
Explore dashboardSupport demand
Tickets created14KSeptember 2026 · Sample
Plan support capacity around actual workload.
Explore dashboardProduct engagement
Product events4.4MSeptember 2026 · Sample
Explore the behaviors behind product engagement.
Explore dashboardAPI traffic
API requests7.3MSeptember 2026 · Sample
Direct reliability reviews toward heavily used endpoints.
Explore dashboardSupplier orders
Purchase value$265.7KSeptember 2026 · Sample
Understand purchasing concentration.
Explore dashboardBooking performance
Booking value$241.3KSeptember 2026 · Sample
Review your booking mix before adjusting channel plans.
Explore dashboardFeature adoption
Feature sessions301.5KSeptember 2026 · Sample
Identify workflows to investigate for product improvements.
Explore dashboardTransaction workload
Transactions201.2KSeptember 2026 · Sample
Understand operational throughput and activity peaks.
Explore dashboardEvery preview opens a full dashboardTake the question into your AI tools
Nine database engines
The database you already use.
Relational, document, warehouse, and event data. Choose a source to explore its setup and a tailored dashboard preview.
Databases
PostgreSQL
Explore orders, customers, and revenue with SQL generated from your questions.
Live queryDatabases
MySQL
Ask questions across related tables and see product and store performance.
Live queryDatabases
MongoDB
Explore document collections and product events through natural-language analytics.
Live queryDatabases
Microsoft SQL Server
Turn operational tables into views of shipments, warehouses, and fulfillment.
Live queryDatabases
Oracle
Explore enterprise finance tables, invoice values, and business-unit reporting.
Live queryDatabases
Snowflake
Ask questions over warehouse data and build views around your business definitions.
Live queryDatabases
ClickHouse
Explore large event tables with time-series charts and traffic breakdowns.
Live queryDatabases
Apache Druid
Analyze time-oriented datasets, event volumes, and dimensions in Druid.
Live queryDatabases
Supabase
Explore the application data in your Supabase Postgres database.
Live queryNo connectors match your search. Try a database name or another category.
Your data stays yours
It runs where your data lives.
tableArth.ai queries databases inside your own private environment. The point is that nothing has to move: no dumps, no copies of your rows shipped off to a third party to get an answer. You keep control of the connection, the access, and exactly what a model is ever allowed to see.
- No exports — query in place against your database
- Read-oriented — point it at a read replica or read-only role
- Four privacy modes — from full AI to fully local, per source
Most capable
Full AI
Rich answers with chart and insight.
Capability—
Privacy
Masked data
Sensitive values tokenized before the model.
CapabilityPrivacy
Hybrid
Stats only
Model sees stats. Rows stay in your stack.
CapabilityPrivacy
Most private
Local template
No answer-composition model call; query planning may still use a model. Server-side only.
—Privacy
How it works
Question in. Real query out. Grounded answer back.
The answer isn't a guess from a language model — it's the result of a query the engine actually wrote and ran against your database.
Step 01 — Ask
Type your question
Plain English, no schema knowledge. tableArth.ai suggests questions and asks a clarifying one if your request is ambiguous.
No SQL required
Step 02 — Query
It writes the query
Schema-aware SQL for relational databases, or the right query for a document store like MongoDB. It self-corrects a failed query up to three times.
SQL & NoSQL
Step 03 — Answer
Runs it, answers you
The query runs in your environment and the result comes back as a readable answer with the right chart — grounded and checkable.
Answer + chart
SQL and NoSQL
Tables or documents — you just ask.
Relational databases
PostgreSQL, MySQL, and Druid. tableArth.ai reads your tables, joins, and column types, then writes real SQL from your question — the same idea as text-to-SQL, grounded in your schema.
Document databases
MongoDB. No aggregation pipelines to hand-write — the engine understands collections and nested documents and builds the right query for you. See querying MongoDB in natural language.
FAQ
Querying your databases.
Which databases can I query in natural language?
External databases such as PostgreSQL, MySQL, MongoDB, and Druid. More connectors are being added. Both relational (SQL) and document (NoSQL) databases are supported.
Does my data have to leave my environment?
No. tableArth.ai queries databases running inside your own private environment. The data stays where it lives — there are no exports and no copies of your rows leaving your network to get an answer.
Do I need to write SQL or an aggregation query?
No. You ask in plain English and tableArth.ai writes and runs the query for you — SQL for relational databases, the right query for document databases like MongoDB — then returns the answer with a chart.
How accurate and safe are the answers?
Answers are grounded in a real query run against your data, so they are verifiable rather than guessed. The engine self-corrects a failed query up to three times, and four privacy modes let you control exactly what the model can see.
Can non-technical users use it?
Yes. Anyone who can ask a question can query the database. tableArth.ai suggests relevant questions and asks a clarifying question when a request is ambiguous, so people get to the right answer without knowing the schema.
Get started
Point it at a database. Ask your first question.
Connect a read replica in your environment and let your team query it in plain English — grounded answers, in seconds.
AI native by design · MCP previewYour data.
Your data.
Your AI workspace.
Keep the conversation where you work. MCP brings authorized tableArth workbooks into compatible clients such as Claude, ChatGPT, and Cursor.
From Tally and spreadsheets to databases, cloud bills, and ad reports: connect the source once, then explore its workbook context through the tools you choose.
Ask your dataExplore dashboardsCreate new views
Explore MCP access Staged preview. Availability depends on your client, deployment, and plan. Creating or updating dashboards requires write permission.
One workbook. More ways to work.MCP
Your connected dataOne authorized workbook
Imagine asking in ClaudeExample
“Explore my workbook, explain what changed, and help me build a dashboard for the next review.”
Workbook context → governed analysis → a dashboard you can return to.