Deepline for Revenue Operations

Who this is for

RevOps leads and Sales Ops practitioners at B2B companies who need reliable enrichment but are tired of spreadsheet-tool instability, surprise credit burns, and 50K row limits. 75% of RevOps professionals cite data inconsistencies as the most frustrating part of their tech stack — Deepline fixes the enrichment layer.

The 3 problems RevOps teams deal with every week

These aren't edge cases — they're patterns from thousands of community discussions. The average RevOps team spends more time debugging their enrichment tool than actually enriching data.

How Deepline solves the RevOps enrichment stack

Four workflows that replace dozens of manual steps, formula columns, and fragile Zapier automations.

deepline enrich --input stale-contacts.csv --output clean.csv \
  --with-waterfall "email" \
  --with 'apollo=apollo_people_match:{"first_name":"{{First Name}}","last_name":"{{Last Name}}"}' \
  --with 'pdl=peopledatalabs_enrich_contact:{"first_name":"{{First Name}}","last_name":"{{Last Name}}"}' \
  --end-waterfall \
  --with 'verify=leadmagic_email_validation:{"email":"{{email}}"}'

deepline enrich --input inbound-today.csv --output enriched.csv \
  --with 'company=crustdata_companydb_autocomplete:{"field":"company_name","query":"{{Company}}"}' \
  --with 'verify=leadmagic_email_validation:{"email":"{{Email}}"}' \
  --with 'score=call_ai:{"prompt":"Score this inbound lead 1-10 against our ICP: {{company}}. Return JSON with score, tier, reason.","json_mode":true}' \
  --rows 0:1  # pilot on 2 rows first

deepline enrich --input target-accounts.csv --output scored-accounts.csv \
  --with 'contacts=apollo_org_search:{"organization_name":"{{Company}}"}' \
  --with 'enriched=peopledatalabs_enrich_company:{"name":"{{Company}}"}' \
  --with 'score=call_ai:{"prompt":"Score this account for enterprise fit: {{contacts}} {{enriched}}","json_mode":true}'

deepline sequence --provider instantly --campaign outbound-q1 --input scored-accounts.csv

# Or push to multiple sequencers
deepline sequence --provider lemlist --campaign email-q1 --input scored-accounts.csv
deepline sequence --provider heyreach --campaign linkedin-q1 --input scored-accounts.csv

Billing that doesn't surprise you

Deepline uses an explicit execution model. Nothing auto-runs. Every command shows you exactly what it will do before it does it.

Your data stays in PostgreSQL — not a vendor's proprietary tables

Every Deepline workspace includes a PostgreSQL database. Your enriched data lives in your own schema — queryable with SQL, exportable without fees, and owned by you. Full audit trail of every enrichment operation: which provider returned which field, when, and at what cost.

-- Query your enriched data directly
SELECT email, company, icp_score, enriched_at
FROM dl_resolved.contacts
WHERE icp_score >= 8
ORDER BY enriched_at DESC;

-- Full audit trail
SELECT provider, action, cost_credits, created_at
FROM dl_resolved.enrichment_history
WHERE contact_email = 'vp@target-account.com';

No vendor lock-in. No export charges. No 50K row caps on your own data. Compare that to enrichment tools where your data lives in their proprietary tables and you pay to get it out.

Works alongside your existing tools

Deepline doesn't replace your CRM. It enriches the data that goes into it. Use Deepline to clean, enrich, validate, and score your leads — then push them to Salesforce, HubSpot, or your sequencer of choice.

Trusted by GTM teams

Frequently asked questions

Learn more

• Deepline vs Clay — full feature comparison
• What is GTM data enrichment?
• Deepline for GTM Engineers
• Install Deepline