Smart Recover increases AI message acceptance rate from 24% to 77% with PromptHub
Smart Recover wanted to use AI to generate human-like SMS messages for lead re-engagement, but their Airtable-based prompt testing quickly became unmanageable: it was cluttered, change tracking was nearly impossible, multi-model testing was out of reach, and there was no way to measure whether prompts were improving.
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 · Lead abandons website
Smart Recover targets leads who abandon websites as the starting point for SMS re-engagement.
Tools used
PromptHubAirtableSlack
Outcome
After adopting PromptHub, Smart Recover's AI message acceptance rate rose from 24% to 77% — an increase of more than 220% — with batch testing enabling confidence that prompt improvements held at scale rather than producing a few lucky outputs.
What failed first
An Airtable database used to test prompts quickly got out of hand: it grew cluttered after minimal use, couldn't track how changes affected outputs, made multi-model and multi-parameter testing nearly impossible, and offered no reliable measurement of prompt quality over time.