tablize
Tablize provides a production pipeline that lets agents (LLMs) produce new structured columns from raw data in resumeable, auditable batches — with a server-side ledger, an acceptance gate, and CLI/agent tooling for durable, production-grade data extraction.
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Quick Overview
Best for: AI Agents
What it does
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Best fit
AI Agents
Pricing snapshot
Free from $0
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tablize
Tablize is a production-focused pipeline that turns LLM/agent outputs into durable, joinable columns on your tables. It operates batch-by-batch with server-side state so runs resume across sessions and agents, and it records every attempt in a ledger so accepted output can be traced back to raw evidence. The product supplies a CLI and an agent skill (it does not supply models or runtimes) and offers modes with an acceptance gate (golden-file checks) to enforce or observe verification before rows are marked accepted.
AI platform transforming data into dashboards and custom apps effortlessly.
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Server-side resumable batches
Runs keep server-side state so a batch continues from where it left off rather than restarting; agents lease one batch at a time and rows not being processed stay on the server.
Batch ledger and audit trail
Every attempt is recorded with a batch id, counts, model used, submission attempts, and verdicts (accepted/rejected) so you can trace who produced what and under which rule.
Acceptance gate with golden-file checks
An independent check (golden file) can be supplied; when the gate is set to enforce a single miss refuses the whole batch and refusals remain on the books.
CLI and agent skill
Installable CLI and bundled agent skill (npm i -g @tablize/cli; tablize install) to run pipelines locally while persisting state to the server.
Pairing-based auth
Browser pairing flow for CLI login so agents can obtain working credentials without exposing long-lived keys in shell history or clipboard.
Self-host and enterprise deployment
Enterprise option for private, always-on deployment on your own infrastructure; the site states 'Self-host · your infra, your data'.
Pricing
Start free: shared, quota-capped server with full CLI and agent skill (Free $0).
Free
$0- Shared multi-tenant server
- Quota-capped usage
- Full CLI + agent skill + MCP
Pro (Usage-based)
Usage-based — pay only for what runs- Same engine as Free (no feature gates)
- Persistent workspaces
- Pay only for what runs (ledger counts delivered columns)
Enterprise (Self-host)
Talk to us- Always-on private deployment
- Self-host on your infrastructure
- Your infra, your data
Use Cases
Repeated, resumeable extraction jobs
Workflows that run in batches and repeat over time where runs must resume across sessions or agents and keep a durable record of attempts.
Agent-driven structured data extraction
When a coding agent or LLM is writing or cleaning structured data (e.g., extracting columns like manufacturer/trader) and the output must be produced at scale and land downstream.
Workflows needing independent verification
Cases where checks must be independent of the producing agent (golden-file gate) and a wrong row would contaminate downstream systems.
Production data pipelines requiring auditability
Teams that require a ledger of who produced what, the model and prompt used, and the attempt history for traceability and governance.
Integrations
LLM providers (user-supplied model key)
Tablize supplies no model or runtime; agents run with your model key and any LLM supplier can be used.
Command-line tooling
Works via the @tablize/cli and bundled agent skill; examples include batch pull/submit and tablize transform commands.
Benefits
Limitations
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Step 1: Install the CLI and agent skill: npm i -g @tablize/cli; tablize install
- 2 Step 2: Pair the CLI with your account in the browser: tablize login --url https://api.tablize.com and approve the code
- 3 Step 3: Run tablize doctor to check versions, skill, and server connectivity and fix any issues
- 4 Step 4: Inspect the project and propose the raw evidence contract and extraction result schema before creating a pipeline
- 5 Step 5: For the first run create the extraction edge with no golden file to run at mode `none` (unverified rows land unaccepted)
- 6 Step 6: Work in small batches using batch pull, compute locally, then batch submit; never load the whole table into your context
- 7 Step 7: Use tablize transform show <addr> to read gateMode and tablize transform gate <addr> --mode enforce to move the gate when ready
Support
docs
Guides and product pages on the Tablize website (How it works, Guides, Install, Pricing).
CLI doctor
tablize doctor provides automated checks and exact fix commands when something fails.
browser pairing / account
Sign-in pairing flow via the browser to approve CLI clients and manage API keys in Profile → API keys.
API
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