Exports go stale
You upload a file, get an answer, and it's already out of date. Every refresh means exporting and re-uploading all over again.
AI native analytics
Your data.
Your data.
AI native answers.
Connect your business data. Ask better questions. Build dashboards that explain what matters. From tableArth workbooks to compatible AI tools through MCP, put your data at the center of the conversation.
Revenue Workbook
Ask
Dashboard
Sheets
MySQL
You
Top 10 products by revenue this quarter?
Acme Pro leads at $284K,
followed by Acme Cloud at $221K and
Acme Connect at $176K. The top 10 together
drove 64% of quarterly revenue.
Top products · revenue
Q4 · USD
ABCD
EFGH
Works with the data you already have
Google Sheets
Excel
PostgreSQL
MySQL
MongoDB
Druid
Part of leading AI startup programs
The problem
Your answers are trapped in files and databases.
The data that answers your questions is right there — in a spreadsheet, a production database, an events store. Getting an answer out of it is the hard part.
Data lives in many places
The real question spans a sheet and a database and a second tab. Answering it means exporting, flattening, and VLOOKUP-ing it all together by hand.
Only SQL people can ask
If the answer is in a database, the question waits in a queue for someone who can write the query — and the moment has usually passed by the time it comes back.
Cloud cost intelligence
See the signal.
See the signal.
Know what to ask next.
A spike. A missing owner. A growing workload.
Explore the dashboards that turn cloud bills
into better questions and clearer next steps.
tableArth / cloud intelligence
Interactive example
aws
01 – 30 Sep 2026
AWS Billing / Spend & anomalies
Know where every dollar goes.
Month-to-date spend
$64,800
11.1% above baseline
Budget remaining
$5,200
of $70,000 monthly budget
Highest-spend day
$4,320
22 September
Daily spend. One spike stands out.
USD · September 2026
Daily spend
Illustrative baseline
What’s driving the bill?
Share of monthly cost
$64.8k
Total spend
EC2
50%
RDS
24%
S3
15%
Other
11%
Spend signal
A $4,320 day deserves a second look.
Daily spend is 150% above the $1,728 reference. Start with the services behind the increase.
tableArth / cloud intelligence
Interactive example
AZ
01 – 30 Sep 2026
Azure Billing / Allocation & accountability
Give every cost an owner.
Monthly billed cost
$48,200
Across 4 allocation groups
Allocated spend
$41,452
86% assigned to an owner
Ownership gap
$6,748
14% of total monthly spend
Your cloud bill, by team.
Illustrative allocation using owner tags
Core platform
$18,316
38% of spend
Data & AI
$13,014
27% of spend
Customer apps
$10,122
21% of spend
Unallocated
$6,748
14% of spend
Area represents each team’s share of total billed cost.
Ownership coverage
Billed cost with an owner assigned
86%
Spend with an owner
$6,748 still needs an owner
Allocation signal
Find the owner of $6,748.
Review unallocated costs before the next budget conversation. Start with missing or inconsistent owner tags.
tableArth / cloud intelligence
Interactive example
GC
01 – 30 Sep 2026
Google Cloud Billing / Services & project hotspots
Make your next investigation count.
Monthly usage cost
$36,900
Services & project costs
Largest service
$14,760
BigQuery · 40% of the bill
Top project
$14,760
analytics-prod
Start with the biggest cost driver.
Usage cost by service · USD
September spend
Illustrative baseline
Where is the spend landing?
Usage cost by project
analytics-prod
$14,760
customer-app
$9,963
data-platform
$7,380
Other projects
$4,797
Cost driver signal
BigQuery accounts for 40% of the bill.
Compare BigQuery costs by SKU and project, then review the workloads driving the largest charges.
AWS dashboard, 1 of 3.
The solution
Connect it once. Ask it anything.
Connect selected data and give your team one place to ask questions in plain English. Source access, table relationships, and refresh behavior provide the context for each answer.
Step 01 — Connect
Connect your sources
Authorize a Google Sheet, connect an Excel file, or point it at a database — MySQL, PostgreSQL, MongoDB, Druid. Or upload a CSV.
Sheets · Excel · Databases
Step 02 — Sync
Build a Workbook
Bring live databases and imported snapshots into one Workbook. Keep multiple tabs and tables together and configure supported relationships for your analysis.
Live · multi-source
Step 03 — Ask
Ask in plain English
tableArth.ai figures out which sources to use, joins them, runs the query, and answers with the right chart — plus suggested and clarifying questions along the way.
Answers + charts
Data sources
Everything plugs in.
Spreadsheets and databases, connected live or uploaded for a one-off. tableArth.ai reads the structure and you start asking.
Workbook
Connected data, shared context.
A Workbook keeps related sources, questions, charts, and dashboards together. Source type determines whether analysis uses live queries or an imported snapshot. Configure relationships where supported and review the source status before analyzing updates.
- Multiple sources in one place — no warehouse, no ETL
- Refresh behavior matched to your source
- Unified answers across every connected source
Sales pipelineGoogle Sheets
OrdersMySQL
EventsMongoDB
Workbook
Synced
Sales pipeline ✓ live
Orders ✓ live
Events ✓ live
Multi-source answers
One question. All your data.
Ask a question in natural language and tableArth.ai pulls across every connected source — Sheets, Excel, and databases — to return a single, unified answer, chart, and drill-down.
Google Sheets
Excel
SQL databases
MongoDB
tableArth.ai
Answer
APAC revenue ↑ 48% YoY
Chart
Drill-down
128 accounts · Q4
Core features
Everything you need, out of the box.
Natural-language queries
Ask in plain English. tableArth.ai writes and runs the query — SQL or NoSQL — no schema knowledge required.
Multi-sheet joins
Multiple tabs and tables understood as related data and joined automatically — no VLOOKUP, no flattening first.
Auto chart selection
The engine picks bar, line, area, pie, scatter, stacked, or funnel — the right one for each answer.
Smart suggestions
Relevant questions suggested for your data, plus a clarifying question when a request is ambiguous.
Auto-built dashboards
Turn supported business data into dashboard views with KPIs, trends, and breakdowns. Review the fields and calculations before sharing results.
Talk to databases
Query private databases — Postgres, MySQL, MongoDB, Druid — in your own environment, just by asking.
Privacy modes
Four modes. You pick the tradeoff.
Most capable to most private — set it per source or per workspace. Query databases in your own environment, and control exactly what a model is ever allowed to see.
Most capable
Full AI
Rich answers with chart and insight.
Capability—
Privacy
Masked data
Sensitive values tokenized before the model.
CapabilityPrivacy
Hybrid
Stats only
AI sees stats. Rows stay in your stack.
CapabilityPrivacy
Most private
Local template
No answer-composition model call; query planning may still use a model. Pure server-side.
—Privacy
Query databases in your own private environment — your data stays yours.
For software teams
Building software? Embed the same engine.
The engine that answers your questions also drops into your own product. Give your customers plain-English analytics on their own tables with a two-line widget, a REST API, or a Chrome extension — the same natural-language answers, auto charts, and privacy modes, white-labelled on your domain.
/ widget
Drop-in widget
A UI component you embed in two lines. The fastest way to make your product's tables AI-smart.
Embed the widget
/ api
REST API
Bring your own UI and call the API directly to power custom chat, copilots, or in-product flows.
See the API
/ chrome-extension
Chrome Extension
Overlay tableArth.ai on tables in any web app — even ones you didn't build.
Ship as an extension
FAQ
Answers, before you ask.
tableArth.ai is an AI native analytics workspace for your business data. Ask questions in plain English and explore charts and dashboards, using live database queries or source snapshots.
Google Sheets, Excel, CSV, PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, Snowflake, ClickHouse, Apache Druid, Supabase, AWS, Azure, Google Cloud billing, Google Ads, Facebook Ads, and Tally. A private connector reaches approved sources inside your network.
Databases query the source on demand. Sheets, cloud billing, and advertising use refreshed snapshots. Uploaded files and Tally imports stay at their imported state until updated.
Yes. With a multi-source Workbook, you ask a question in natural
language and tableArth.ai pulls data across all your connected
sources to return a single, unified answer.
Yes. You can query external databases such as PostgreSQL, MySQL,
MongoDB, and Druid running inside your own private environment
simply by asking — no exports, and the query runs where your
data lives.
Yes. The same engine ships as an embeddable widget, a REST API,
or a Chrome extension so software teams can give their customers
plain-English analytics inside their product. See the
For Software page.
Get started
Connect a source. Ask your first question.
Start with one sheet or one database. Connect it, ask in plain English, and see an answer and a chart 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.