TL;DR: Smart Columns are AI-driven enrichment columns you add to any lead list. There are 17 built-in types covering data cleaning, company research, email validation, AI generation, and ICP scoring. Costs range from 0.1 to 3.0 credits per row.
Smart Columns
Smart Columns turn your lead list into a programmable spreadsheet. Add a column, pick its type, and Cleanlist runs the column against every (or every selected) row in the background — using the same provider waterfall and credit pool as the rest of the platform.
What you can do
| Category | Examples |
|---|---|
| Data hygiene | Clean first name, format phone numbers, validate emails |
| Company research | Enrich company, find competitors, find similar companies, website analysis |
| Person research | LinkedIn research, contact timezone |
| Quality checks | Data quality name check, data quality domain check |
| AI generation | Cold intro email, custom AI prompt, custom classification |
| Targeting | ICP fit analysis (against any saved ICP profile) |
Each column writes its result into a new field on every row, queryable via the portal and exportable like any other column.
The 17 built-in column types
| Column type | Cost (credits/row) | What it does |
|---|---|---|
clean_first_name | 0.1 | Normalize a messy first name (capitalization, accents, formatting) |
format_phone | 0.1 | Convert a phone number to E.164 format |
email_validation | 0.2 | Run an extra deliverability check (Emailable / ZeroBounce) |
enrich_company | 0.5 | Pull industry, headcount, description, founded year, social links |
linkedin_research | 0.5 | Summarize a contact's LinkedIn profile (recent activity, role, tenure) |
find_competitors | 0.5 | Identify a company's top competitors |
find_similar_companies | 0.5 | Find lookalike companies |
website_analysis | 0.5 | Summarize a company website (positioning, products, ICP signals) |
icp_fit_analysis | 0.5 | Score a contact / company against a saved ICP profile |
data_quality_name_check | 0.5 | Detect malformed or low-quality names |
data_quality_domain_check | 0.5 | Detect personal / catch-all / disposable domains |
contact_timezone | 0.5 | Infer a contact's local timezone from location data |
custom_ai | 1.0 | Run a custom AI prompt over each row (e.g., "Write a 1-line opener") |
custom_classification | 1.0 | Classify rows into your own categories |
cold_intro_email | 3.0 | Generate a personalized cold outreach email |
All costs come from your organization's regular credit pool. Failed runs cost 0 credits.
How execution works
Smart Columns run asynchronously, the same way enrichment workflows do:
- You create the column (choosing a type and any per-column config)
- Cleanlist queues a Temporal workflow on the
smart-columns-queue - Each row is processed in parallel
- As rows complete, the portal updates live (no refresh needed)
- You can re-run just the failed rows from the column header menu
Rows that already have a value can be skipped to save credits.
When to use Smart Columns vs raw enrichment
| Scenario | Use |
|---|---|
| You need email or phone for a fresh contact | Public API enrichment (partial, phone_only, full) |
| You already have a list and want to enrich the company of each row | enrich_company Smart Column |
| You want to score contacts against an ICP | icp_fit_analysis Smart Column |
| You want a personalized email per row | cold_intro_email Smart Column |
| You need to clean up a messy import | clean_first_name, format_phone, data_quality_* |
Smart Columns are portal-only today — they're not exposed via the Public API. You can still integrate by uploading rows via the portal (or via the public bulk enrichment endpoint, which writes to your DEFAULT list) and then layering columns on top.
Custom AI columns
The custom_ai and custom_classification columns let you run your own prompt against every row. You get:
- Access to every field on the row (
{{first_name}},{{company.domain}},{{primary_email}}, etc.) - Choice of output format (text, JSON, classification label)
- A live preview that runs against 3 rows before you commit
This is ideal for:
- Drafting first-line opener variants
- Tagging rows by buying intent or persona
- Pulling specific facts from a company website summary
- Generating custom enrichment fields not covered by the built-in column types
Tips for keeping costs predictable
- Filter first. Apply a list filter, then run the column on the filtered selection.
- Re-run only failed rows. The column header menu has a Run failed only option.
- Test on a small sample. Right-click → Run on selection to try a column on 5–10 rows before scaling.
- Stack thoughtfully.
cold_intro_email(3 credits) is much cheaper if you've already runenrich_company(0.5) andlinkedin_research(0.5) — the AI has more context to draft with.
Related
- Pricing — full credit pricing
- ICP Profiles — used by
icp_fit_analysis - Action Columns — the difference between Smart Columns and Action Columns
- Smart Columns recipes — copy-paste configurations for common use cases