back_office_ops · saas · workflow
How Airtable built Omni: a high-quality AI Q&A assistant for Airtable base data
Building a reliable Q&A agent on top of large, complex Airtable bases is difficult because LLMs tend toward unpredictable reasoning, premature conclusions, compounded mistakes, and hallucinations—issues further amplified by large schemas or vague user questions.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Schema and data tool exploration
Data exploration is broken into two steps: a tool to understand schema and a tool to query data.
Tools used
AirtableOmniRAGAnthropic's Sonnet 4
Outcome
By applying contextual schema exploration, chain-of-thought planning, hybrid search with a correction mechanism, and token-efficient citation encoding, Airtable delivered a production-ready assistant with over 30% latency improvement and 15% cost savings.
Results
Volumeover 30%
Cost replaced15%
Running sinceJune 2025
Grounding & classification
Source type: technical build writeup
25 fields verified against source quotes.
agentic workflowdata extractionknowledge searchragsummarizationknowledge basebuilder submittedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecost reductioncycle time reductiontechnical build writeupback office opsagentic task executionrag answering