Connect your data. Ask it anything.
Practical guides on connecting Google Sheets, Excel, and databases and asking them questions in plain English — Workbooks, cross-source joins, live data, and talking to your private databases. Plus the fundamentals of natural-language query and, for software teams, embedding analytics in your own product.
Connect your data & ask.
Connecting Sheets, Excel, and databases — and asking them anything in plain English.
How to analyze Google Sheets with AI
Connect a live Google Sheet and get answers in plain English — across every tab, no formulas or pivot tables, and always in sync.
Read → How-toHow to analyze Excel files with AI
Multi-sheet workbooks joined automatically, large files handled reliably, and analysis that runs on live data instead of a snapshot.
Read → GuideHow to talk to your database in plain English
Query PostgreSQL, MySQL, MongoDB, and Druid by asking — grounded answers from a real query, run inside your own environment.
Read → DefinitionWhat is a Workbook?
Connected data that stays in sync — multiple sources in one place, and one natural-language question answered across all of them.
Read → How-toJoin data across sheets and tables
Join tabs, files, and sources without VLOOKUP — the engine works out how related data connects and joins it for you.
Read → ConceptMulti-source analytics
One natural-language question, pulled across every connected source — spreadsheets and databases — for a single unified answer.
Read → How-toHow to query MongoDB in natural language
Ask a document database in plain English — no aggregation pipelines to hand-write, and it runs in your own environment.
Read → GuideLive connected data vs. one-time uploads
Why connected, synced data beats a frozen export — the hidden cost of re-uploading, and when an upload is still the right call.
Read → PerspectiveWhy good data AI asks clarifying questions
The best data AI suggests questions and asks when you're vague — instead of handing back a confident, wrong answer.
Read →Analytics fundamentals.
Natural-language query, text-to-SQL, and — for software teams — embedding analytics in your product.
What is natural language query (NLQ)?
Ask a question in plain English; the engine writes and runs the query, picks the right chart, and answers in seconds. How NLQ works and what separates a demo from production.
Read → DefinitionWhat is text-to-SQL?
How a plain-English question becomes a SQL query, where text-to-SQL breaks, and what makes it trustworthy enough to ship.
Read → DefinitionWhat is conversational analytics?
Asking questions of your data in a back-and-forth instead of reading a dashboard — how it works, how it compares, and where it fits.
Read → How-toHow to add AI analytics to your SaaS product
A practical, step-by-step guide — start from data you already have, choose a widget, REST API, or Chrome extension, make it native, set a privacy mode, and ship in days.
Read → SecurityEmbedded analytics security
Customer-facing analytics touches real customer data. Tenant isolation, permission-aware queries, AI guardrails, and privacy modes — plus a pre-launch checklist to run before you ship.
Read → TrendsEmbedded AI, today
AI is moving out of the separate chatbot tab and into the product itself. What embedded AI means in 2026, the four patterns that matter, and what separates production-grade embedded AI from a bolt-on.
Read → Use caseBefore and after tableArth.ai
From SQL backlogs, dashboard requests, and support tickets to plain-English answers in seconds. A before-and-after look at what changes when customer-facing analytics becomes AI answers on demand.
Read → DefinitionWhat is embedded analytics?
Analytics built directly into the product your customers already use — charts, dashboards, and answers inside the workflow instead of a separate BI tool. What it is, why it matters, and how the AI version changes the bar.
Read → DecisionBuild vs. buy embedded analytics
SQL generation, chart selection, streaming, privacy modes, and cost governance — the real scope of building in-house versus buying a layer. A framework for deciding which path fits your team and timeline.
Read → ComparisonEmbedded analytics tools compared (2026)
A fair, factual look at the leading embedded analytics tools — where each one focuses, the trade-offs to weigh, and how to match a tool to your use case. Verify current specifics on each vendor's own site.
Read →See how tableArth.ai stacks up.
tableArth.ai vs. Explo
Explo centers on building embedded dashboards; tableArth.ai centers on natural-language answers on the tables you already have.
Read → ComparetableArth.ai vs. Luzmo
Two approaches to embedded analytics: dashboard-first visualization versus a drop-in AI layer that answers questions in plain English.
Read → ComparetableArth.ai vs. Embeddable
Where each product puts its emphasis, the trade-offs to weigh, and an honest take on when each may be the better fit.
Read → ComparetableArth.ai vs. Qrvey
Platform-style embedded analytics versus a lightweight AI layer you ship as a widget, REST API, or Chrome extension.
Read → ComparetableArth.ai vs. Julius.ai
A standalone AI data analyst you bring your own data to versus an embedded AI analytics layer you ship inside your product for your customers.
Read → DocsDeveloper documentation
Embed the widget, call the REST API, or ship the Chrome extension. Read the docs to see how tableArth.ai fits into your stack.
Read →Bring AI analytics to your product.
See how tableArth.ai drops into your tables — or get a walkthrough tailored to your stack.