HubSpot + OpenAI: clean call logs and next steps
Turn messy call transcripts into clean HubSpot call logs and a clear follow up task…
Turn messy leads into sales-ready records. Enrich emails, company data, and intent signals, then score and route prospects so your team focuses on the right conversations.
A workflow watches for new leads (form, spreadsheet, webhook, CRM). It then enriches the record by looking up missing fields like company name, website, role, location, and social profiles, and can verify email deliverability. Next, it normalizes data (clean casing, split names, dedupe) and applies your rules or AI scoring to decide fit and intent. Finally, it routes results: update HubSpot, write to Google Sheets/Airtable, and notify sales in Slack or Gmail.
Usually, no. Most Flowpast workflows are plug-and-play: connect your accounts, choose where leads come from, and set simple scoring rules. If you can manage a spreadsheet and a CRM, you’ll be fine.
If you’re manually researching companies, copying LinkedIn details, and cleaning spreadsheets, automation can easily save about 2 hours a day for a single person. The bigger win is consistency: every lead gets enriched the same way, instantly, with fewer mistakes. Sales also stops chasing bad fits because low-quality or unverifiable contacts can be flagged or filtered out. Over a month, that’s a lot of regained focus for both marketing and SDRs.
Start with your lead destination (HubSpot, Airtable, or Google Sheets) and one reliable lead source (a form, webhook, or imported list). You’ll also want API keys for enrichment or search, plus an AI model like OpenAI if you’re scoring or summarizing. Make sure you define a few fields up front: ideal customer profile, required contact fields, and what “qualified” means for your team. Then run a small test batch and review results before turning it on.
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