Ship to product teams
We make a software team productive on day one. No long onboarding, no rebuild. Two lines of code, on your domain.
Company
Meaning, in every table.
tableArth.ai is the embeddable AI analytics layer for B2B software. We believe every table in software should be able to answer a question — and that building that layer shouldn’t cost product teams a year.
Why we built it
Software is full of tables. They should talk back.
Every B2B software product is built on tables — orders, users, revenue, tickets, events. When a customer has a question, their options today are tickets to engineering or exporting to a spreadsheet. We thought we could do better.
tableArth.ai is the answer layer. Two lines of code, four privacy modes, six core capabilities. Built so a software team ships AI analytics next sprint — instead of next year.
What we ship
- → Drop-in widget for any table
- → REST API for custom UI
- → Chrome extension for legacy apps
- → Four privacy modes
- → Budget and usage controls
- → Auto-built dashboards
Principles
How we build.
Privacy is a default
Four modes aren’t a feature — they’re the design. You pick the tradeoff per customer, per table. We never train on your data.
Cost has to be predictable
Hard caps, soft alerts, or pay-as-you-go. Per widget, per API, per customer, per user. AI spend shouldn’t be a surprise.
Company
Who is behind tableArth.ai.
tableArth.ai
Founded 2026. We build AI products for B2B software teams.
Noida, India
D-5 Sector-59, Noida, Uttar Pradesh. Customers across US, EU, APAC.
Talk to us
Founder
Who's behind it.
tableArth.ai was founded in 2026 by Udit Agarwal to give B2B software teams a way to ship AI analytics to their customers without a year-long build. The team builds AI products for software companies out of Noida, India, serving customers across the US, EU, and APAC.
Founder
Udit Agarwal
Founder, tableArth.ai
Building the embeddable AI analytics layer for B2B software — so every table in a product can answer a question.
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Work with us
Ship AI analytics to your product.
Start with one dataset. One widget. One question.