tableArth.ai vs Qrvey

Where tableArth.ai centers: answers, not assembly

tableArth.ai centers on one job: letting your customers ask a question in plain English and get an answer — with a chart and an auto-built dashboard — on top of the data table you already render. There is no dashboard to model and no report to lay out first. You add a script tag and a <table-ai> element pointed at a table, and the engine handles the rest: it writes and runs the SQL, picks the right chart across bar, line, area, pie, scatter, stacked bar, funnel, or a plain table, and streams the result back in under about five seconds. If a generated query fails, the engine self-corrects up to three times before answering.

That is the deliberate emphasis. Qrvey centers on giving SaaS providers a complete embedded BI surface — dashboards, reporting, a data pipeline, and automation — that they own and operate. tableArth.ai assumes those tables and that data already exist in your product, and adds a natural-language brain on top of them. You can read the full capability set on the product page, and the exact attributes in the widget docs.

<script src="https://widget.tablearth.ai/v1.js"></script>
<table-ai
  api-key="pk_live_…"
  target="#revenue-table"
  user="u_8124"
  customer="acme"
  theme="dark" />

Illustrative snippet. Public key is origin-bound to your domain.

Where Qrvey centers: a BI platform in your own cloud

Qrvey's defining strength, as of writing, is that it is an infrastructure-level embedded analytics platform designed to run inside your own AWS environment. It bundles multi-tenant self-service dashboards, reporting, a data pipeline, and automation into a single platform aimed at SaaS providers operating at scale. For teams that want the analytics stack to live in their own account — for data-residency, control, or consolidation reasons — that self-hosted, end-to-end model is a meaningful advantage.

tableArth.ai takes a lighter-weight path. Rather than standing up a platform, it is a drop-in layer: a widget for React, Vue, Angular, or plain HTML; a REST API with Server-Sent Events streaming if you want to bring your own UI; or a Chrome extension that overlays the AI panel on tables in apps you did not even build. The trade-off is clear: you get natural-language answers fast and you skip the platform build, but tableArth.ai is not a self-hosted BI stack the way Qrvey is positioned to be. If self-hosting the whole platform is your requirement, that points toward Qrvey.

Privacy, control, and how data reaches the model

Running the platform in your own cloud is one way to keep data close; tableArth.ai offers a different lever for the same concern. It ships four privacy modes you set per customer, per workspace, or per table:

• Full AI — the model sees the full table for the richest answers.
• Masked data — text is tokenized before the LLM and restored in the output.
• Hybrid / stats only — the model sees only column statistics; rows render locally.
• Local template — pure server-side rendering with zero external AI calls.

Every widget is locked to your domain by an API key and origin guard, you can set hard budget caps per widget, endpoint, customer, or user, and enterprise plans add bring-your-own-LLM key, SSO, SCIM, role-based access, and audit logging. SOC 2 Type II is in progress. The honest framing: if data residency specifically means "the entire analytics platform runs in our AWS account," Qrvey is built around that. If it means "we control exactly what data reaches any model, and we can route AI through our own provider," tableArth.ai's privacy modes and BYO-LLM cover it. See the full detail on the security page.

When Qrvey may be the better fit

We would rather you pick the right tool than the wrong one. Qrvey is likely the stronger choice when:

• You want a complete embedded BI platform running in your own AWS environment, owned and operated end to end.
• You need a built-in data pipeline, reporting, and automation as part of the same product, not just answers on tables you already render.
• Your priority is broad multi-tenant self-service BI infrastructure at scale rather than a focused natural-language layer.
• Self-hosting the full analytics stack in your own cloud account is a hard, non-negotiable requirement.

tableArth.ai is the better fit when you want to add natural-language AI answers — charts, dashboards, and sub-five-second responses — onto the data tables already in your B2B product, with two lines of code and without building or operating a BI platform. Many teams already have tables; far fewer want to run a platform to make them ask-able. If that is you, start with the deploy guide or read build vs buy. You can also see how we frame other tools in embedded analytics tools compared.

We do not state competitor pricing, customer counts, or funding. Verify Qrvey's current details on its own site.