Back office ops · Production

How Airtable built Omni: a high-quality AI Q&A assistant for Airtable base data

The problem

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.

Workflow diagram · grounded in source
1
Schema and data tool exploration
ai_action
“Hence we break down data exploration into 2 steps: - A tool to understand schema - A tool to query data”
2
Planning and replanning
ai_action
“we incorporate a planning step, as well as steps to replan upon discovery of new data”
3
Hybrid search with filtering
ai_action
“the filtering step narrows the search scope, while keyword and semantic search help identify results that are relevant for the user's question”
4
Correction fallback
validation
“if no meaningful data was found, we perform the search again on a wider scope from the initial attempt”
5
Inline citation output
output
“The LLM cites the sources alongside each piece of information with inline citation tags”
6
Eval and live feedback loop
feedback_loop
“The evals are very helpful for us to iterate on any aspect of the system quickly and confidently”
Reported 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.

Reported metrics
Latency improvementover 30%
Cost savings15%
Reported stack
AirtableOmniRAGAnthropic's Sonnet 4
Source
https://medium.com/airtable-eng/how-we-built-a-high-quality-q-a-assistant-738ae9efeb7a
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

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% lat…

What tools did this team use?

Airtable, Omni, RAG, Anthropic's Sonnet 4.

What results were reported?

Latency improvement: over 30%; Cost savings: 15% (source-reported, not independently verified).

How is this back office ops AI workflow structured?

Schema and data tool exploration → Planning and replanning → Hybrid search with filtering → Correction fallback → Inline citation output → Eval and live feedback loop.

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