Airtable automation that makes it your operational hub

Where Airtable's built-in automations stop

Airtable's native automations handle tidy in-base triggers well: when a record changes, send a Slack message, update a field, fire a webhook. They run thin the moment you need multi-step logic, data from an external system, more than a handful of runs at volume, or an AI step to read and classify. Teams fill the gap manually, someone exports a CSV, enriches it elsewhere, and pastes it back, and the base drifts from the truth between updates.

We run that logic outside Airtable, using its API and an automation layer, and write clean results straight back into the base. Your team keeps working in the Airtable views they know, while enrichment, syncing, and processing happen underneath, on schedule or on trigger, without the manual export-and-repaste loop.

• Multi-step logic and branching beyond single-trigger automations
• Higher-volume processing than the built-in automation limits comfortably allow
• Pulling in and pushing out data from systems Airtable doesn't natively sync
• AI steps to classify, extract, and summarise records

Keeping Airtable in sync with the rest of your stack

The biggest risk with Airtable is drift: it becomes a second copy of the truth that slowly disagrees with your CRM, your billing, or your product database. We build two-way sync so a change in one place propagates to the other, on the schedule and with the conflict rules you need. A closed deal in your CRM updates the Airtable tracker; a status change in Airtable updates the source system; nobody reconciles by hand.

Because Airtable is so often the human-friendly front end over messier systems, this sync is what makes it trustworthy. Your team gets Airtable's lovely interface to work in, and the underlying systems stay authoritative, with the automation quietly keeping them agreed.

• Two-way sync between Airtable and your CRM, billing, or product database
• Scheduled imports and exports with clear conflict and de-duplication rules
• Webhooks so downstream systems react the instant an Airtable record changes
• A single source of truth instead of a base that silently drifts

AI inside the base: enrich, extract, summarise

Airtable is a natural home for AI-processed data. We drop an LLM into the flow to enrich a new company record, categorise inbound submissions, extract structured fields from an attached document or a long text field, or summarise a linked set of records into a readable digest, then write the result straight into the right columns.

This is the same grounded, validated pattern behind our document-processing and data-entry work: the model does the reading, checks run before anything is written, and low-confidence items are flagged for a human rather than guessed. The base fills itself with clean, structured data instead of waiting on someone to type it.

Yours, documented, and monitored

Everything runs in your Airtable workspace and your own automation accounts, documented so your ops team can read and adjust it. We build in validation and monitoring so a failed sync or a changed schema surfaces as an alert, not as a base full of quietly wrong records. And you own it outright, no dependency on us to keep it running, which matters more with Airtable than most tools because it so often becomes load-bearing without anyone deciding it should.