All your marketing data.
One dashboard your AI can read.
Search Console, Google Ads, GA4 — each in its own tab, none of them adding up. TableBI pulls them into one set of numbers and one live dashboard that refreshes itself. Nothing goes on your site: no tracking script, no tag — it reads each platform’s own data. And your Claude Code can query all of it in plain language.
Paste it into your Claude Code or Codex — it installs itself and walks you through connecting your first source. Read the prompt first · Prefer the web app?
$0 inference · your own Claude Code is the brain
Illustration — the numbers in this animation are made up. See a dashboard on real data →
Your agent installs its own hands.
Paste this into Claude Code or Codex. It installs the CLI, plants the skill, and connects your account — you just answer a prompt or two.
$0 inference · your agent is the brain
Set up TableBI as my Claude Code's data backend — so you can connect my marketing sources, query them, and publish live dashboards for me. 1. Install: npm i -g @tablebi/cli 2. Run tablebi install — it adds the tablebi skill so you'll know to use these commands next time. 3. Run tablebi login — it opens the browser to authorize; the first login also creates my account. Walk me through it. 4. Run tablebi context — confirm you're connected and show my sources + metric definitions. Then tell me what's ready — always read the numbers from the CLI (never invent them), and ask before spending money or emailing a real person.
Claude Code is flow, not stock.
It's brilliant in the moment — and gone when you close the tab. It can't keep a source connected, can't refresh, can't keep the analysis, can't hand it to a client. And across five tools with five definitions of "conversion," it will compute a blended number — confidently, and wrong.
Can't hold the sources
OAuth, rate limits, schema drift across Google Ads, Meta, GA4, Search Console. The dirty work your agent shouldn't own.
Gets the numbers wrong
No shared metric definitions, so blended ROAS and CPA across platforms come out confidently miscalculated — the bug you can't see.
Can't keep it
The analysis evaporates: no refresh, no history, no link to send. Flow, never stock — so you redo it every Monday.
Connect once. Ask in two altitudes. Pin it live.
One backend under your agent. One trusted set of numbers runs through all three — so the number you ask is the number you pin is the number that refreshes. No LLM in the loop: reproducible and free.
Connect + unify
Search Console, GA4 and Google Ads today; Meta Ads in beta; any other channel as a CSV upload. TikTok Ads and Google Sheets are on the roadmap. OAuth once, kept synced. Normalized into one trusted set of numbers, with the rows each platform returns kept next to them.
Two altitudes
Your Claude Code queries unified metrics (trusted, cross-channel) and raw (single-platform, full-granularity). $0 inference — your agent is the brain.
Publish live
dashboard publish → a hosted read-only URL. Stores the query, not a snapshot — so it refreshes itself.
It's a CLI. tablebi install plants a skill into Claude Code / Codex — your agent learns the commands and your metric definitions, then drives everything in plain English. No prompt-engineering. Read the five-step quickstart in the docs →
Trusted numbers. Full granularity. Both.
Most tools make you pick: a clean blended number you can't drill into, or raw data your agent miscomputes. TableBI keeps both — your agent moves between them in one breath. The rows each connector pulls are kept as the platform reported them.
One set of numbers you can trust.
One definition of cost, conversions, ROAS — reconciled across every platform. The number you quote the client.
The platform's own fields, intact.
The native columns each connector pulls — Meta's reach and frequency, GA4's channels and AI sources, Search Console's queries and pages. Read-only SQL, one platform at a time.
Same files underneath. Unified metrics for the cross-channel story; raw when you need the platform's own metric. Your agent reads context to see what each altitude offers, then picks — you never trade trust for power.
No prompt-engineering. It interrogates the data.
An agent only knows what's in its context. So TableBI hands it the map, then lets it read the territory — install plants the skill, and a handful of bounded commands let it learn your metrics and your data before it ever queries.
Plant the skill
Drops a skill into Claude Code / Codex so the agent knows TableBI exists and when to reach for it. Binary on PATH ≠ agent knows.
Rehydrate in one call
Metric definitions, connected sources, freshness, dashboards, command signatures — --sample adds a few real rows. The live manifest the agent reads first.
Interrogate the data
query asks for a metric by name and returns rows, the SQL it generated and the caveats; ask runs read-only SQL for anything the grammar can't express. Always bounded — safe to read into context.
The moat is trust + persistence, not AI.
"We added AI" is a moving target — everyone's bolting it on. The hard, load-bearing layer is the boring one underneath: the cleaning and unified definitions that make a number defensible, and the persistence that keeps it alive. That's what we built first.
| Instead of | Their gap | TableBI |
|---|---|---|
| Claude Code on a raw DB just point your agent at the data |
Multi-source breaks; the metrics get miscomputed; nothing persists. Raw SQL is powerful and dangerous. | Trust without losing granularity (raw), plus persistence (Pin) and managed connectors. |
| Dashboard tools Looker Studio · AgencyAnalytics |
Hand-built, GUI-first; you decode the charts yourself. | Agent-driven, two altitudes, trusted numbers + the agent's read. Entry is the agent, not a GUI. |
| Traditional BI | Heavy, expensive, needs a data model before it does anything. | Agent-native, metric definitions ready, opens to value — marketing connectors out of the box. |