Folk to Attio: how to switch from Folk CRM in 2026

Folk is one of the cleanest "first CRM" tools on the market. Connect Gmail, install the LinkedIn extension, and within a weekend a small team has a working contact database with pipelines and a few automations.

That same simplicity is the reason teams outgrow it. The data model is mostly fixed. Custom objects are limited. Workflow automations stop short of where ops teams need them. AI features focus on email drafting, not field-level enrichment or agent-style research.

When the bottleneck shifts from "we need any CRM" to "we need a CRM that bends to our process and runs AI on every record", Attio is usually the next stop. This guide is the playbook for that move.

For the broader case for Attio, see Why Attio is the CRM I recommend in 2026. For a side-by-side on AI capabilities, see Attio AI features and credits in 2026.

Why teams move from Folk to Attio

Five reasons show up on almost every Folk-to-Attio call.

• Custom objects. Folk has Contacts, Companies, and Pipelines. If your business needs Investors, Properties, Cohorts, Loans, or Deals-with-substages, Folk pushes those into pipelines or tags. Attio gives them their own object with their own attributes and their own automations.
• AI at the field level. Folk's AI is mostly email assistance and meeting notes. Attio's AI runs on every record as fields you can read, filter, and report on. Research Agent, Classify, Summarize, Prompt Completion. The CRM becomes a place where every record gets researched and scored without anyone clicking a button.
• Workflows that branch. Folk automations cover the basics. Attio's workflow builder runs branches, conditions, AI steps, and webhooks. Stage-change alerts that route to different Slack channels by ICP segment. Lead intake that researches the company, classifies the segment, and assigns the right owner. This is the part Folk does not reach.
• Reporting beyond the pipeline view. Folk's reports are strong on pipeline movement and weak on cohort, segment, and time-series questions. Attio reports run on any object with any filter, including AI-derived attributes.
• API and platform depth. Teams that want to ship internal tools, sync data with the product, or run Attio as the system of record for an internal app need an API and an MCP server. Attio has both. Folk's API is narrower.

If none of those five reasons apply, Folk is fine and the migration is premature. The list below is the test.

When Folk is still the right call

Honest answer: stay in Folk if all of the following are true.

• You are a team of 1 to 5 doing relationship-led sales.
• Your data model is "people, companies, one pipeline".
• You write outbound emails one at a time, not in workflows.
• You do not run reports beyond "deals in each stage".
• You do not need to push CRM data into a product database or another internal tool.

Folk's design choices reward that profile. Attio's flexibility is overhead you do not need yet.

What you keep, what you change

A Folk to Attio move is mostly a translation. Most concepts have a direct equivalent. The shape changes.

The piece that does not have a one-to-one map: Folk's "Pipelines" sometimes hold non-deal data (partners, candidates, investors). Those should move to Lists or to a dedicated custom object in Attio, not to the Deals object.

Step 1: Decide the data model before you import

This is the step Folk users tend to skip because Folk did not require it. Attio rewards a half hour of planning here more than any other migration step.

Before any data moves, answer three questions on a piece of paper or a whiteboard.

1. What objects do you actually need? Companies, People, Deals are the spine. After that, ask which Folk pipelines were actually different processes (Investors, Partners, Candidates, Properties). Each "different process" pipeline becomes a list or a custom object, not a Deal stage.
2. What attributes matter on each object? For each, write the 5 to 10 attributes a view or report will read in the next 30 days. Folk users tend to have many one-off custom fields. Most should not move.
3. What relationships matter? A Person belongs to a Company. A Deal connects to a Company and one or more People. If you also have "Person introduced by Person", "Company part of Group", or "Deal sourced through Partner", write those down explicitly. They become linked records in Attio.

The result almost always reduces the field count by a third, and reframes one or two Folk pipelines as their own object. That is the win, before any data moves.

Step 2: Export your Folk data

Folk supports CSV export per object and per pipeline. The path is Settings, then Export, then pick the object or pipeline. Export each separately.

For a clean export:

• Export Contacts as a single CSV.
• Export Companies as a single CSV.
• Export each pipeline as its own CSV. Keep the pipeline name in the filename.
• Export Tasks and Notes if you need history. These are often skipped on first migration and brought across manually for the records that matter.

If you used Folk's groups heavily, also export the group definitions. Groups become Lists or multi-select tags in Attio.

For larger workspaces (over 10,000 contacts), export in batches by group or by created date so the files stay under Attio's import size ceiling.

Step 3: Clean the export

An hour of cleanup saves a day of fixes after import. Do this in a copy, not the export itself.

• Standardize stage names across pipelines. Folk lets each pipeline have its own stage labels. If two pipelines should be one in Attio, pick one canonical stage list and remap.
• Resolve duplicate contacts. Folk dedupe is decent. Cross-pipeline duplicates still happen. Match on email first, then on name plus company.
• Normalize phone, email, and dates. Pick one format per column and convert.
• Drop test rows and dead pipelines. The "Q3 2024 outbound test" pipeline does not need to come.
• Split mixed-content fields. Folk's "Notes" field on a contact often holds a mix of role context, meeting notes, and reminders. Decide whether each becomes a Note record, an attribute, or gets dropped.
• Tag rows with the original pipeline. Add a column called source_pipeline so you can rebuild the right list or status after import.

Step 4: Build the Attio workspace

Set up the structure in Attio before any import.

1. Create the workspace. Free plan is fine for the first import. Upgrade when you need automations or Pro AI.
2. Build the Companies object with the attributes you decided to keep.
3. Build the People object with the same exercise. Match field types: single-line text, email, phone, select, multi-select, date, checkbox.
4. Build the Deals object. Recreate the stages from the canonical pipeline as a status attribute.
5. Add custom objects for any Folk pipeline that was not actually a deal pipeline (Investors, Partners, Properties, Candidates).
6. Set up Lists for any Folk groups you want to recreate as curated views.
7. Connect Gmail or Outlook so historical email activity attaches to the right people from import day forward.

Do not build automations yet. Get the data in first.

Step 5: Import the CSVs

Attio supports direct CSV import in the UI. Map columns to attributes during the import flow.

Import in this order:

1. Companies first.
2. People next, linked to Companies by domain or by Company name.
3. Deals last, linked to both Companies and People. Use source_pipeline to set the right status or list membership.
4. Custom objects (Investors, Partners, Properties) after Deals.
5. Notes and Tasks last, only for the records where history matters.

Reverse this order and the relationship fields will not find their parent records.

For each import, spot-check 10 to 20 records before moving to the next file. Folk exports occasionally include an "owner" column with email addresses that need to be mapped to Attio workspace members. Catch this on the first file, not the fifth.

For datasets larger than a few thousand records, or for parallel imports of related custom objects, the Attio API is faster and cleaner than the UI.

Step 6: Wire up the automations Folk did not reach

This is where the move pays back the migration time. Three automations cover most of what teams missed in Folk.

Lead intake with research. A new Person added to Attio (from a form, an email forward, or a manual create) triggers a workflow that finds or creates the Company, runs the Research Agent on the Company, classifies the segment with an AI attribute, and assigns the right owner. Folk could not run that whole chain in one workflow.

Stage-change routing by segment. When a Deal moves to Demo Booked or Proposal Sent, a Slack message goes to a different channel based on the ICP segment. Folk's stage automations are linear. Attio's branch on attributes.

Stale-deal flag with AI summary. Any open Deal with no activity in 14 days surfaces in a "Needs follow-up" view with an AI-generated one-line risk note. Replaces the manual pipeline review.

These take an hour total inside Attio's automation builder.

Step 7: Add AI on top

Folk's AI is mostly email drafting. Attio's AI is field-level on every record. Three to ship in the first week after migration.

1. AI Research attribute on Companies. "What does this company sell?", "Most recent funding round", "Tech stack". Runs a web search and writes back the answer on the record. Replaces the 20-minute pre-call lookup.
2. AI Classify attribute on Deals. Pick from your ICP segments. Run it once on every open Deal. Every view can now filter by ICP fit.
3. AI Prompt Completion attribute on People. "Draft a one-line opener based on this person's company and role". Reads the linked Company. Writes a sentence. Replaces the manual personalization step before every outbound email.

For the full breakdown of what each AI feature costs in credits, see Attio AI features in 2026.

Folk patterns and how they map

Five patterns I see on every Folk migration. The fix is rarely to copy the pattern.

Pattern: a pipeline per use case. "Outbound Q1", "Inbound Q1", "Investor outreach", "Partnerships". The fix: outbound and inbound are stage-difference on one Deals object. Investor and partner outreach become custom objects or lists. The CRM does not care which Folk pipeline a record came from once status and segment are attributes.

Pattern: groups standing in for segments. Folk users often build groups like "ICP A", "ICP B", "Cold". The fix: turn ICP into a select attribute on the Company. Use views with filters instead of static groups. AI Classify can fill the attribute automatically.

Pattern: tags on contacts holding company-level info. "Series B", "AI/ML", "US-based" tagged on a contact actually describes the company. The fix: move those to attributes on the Company record so they apply to every linked person.

Pattern: notes used as a CRM history. A "Notes" cell with months of context. The fix: notes become Note records attached to the record, each with its own timestamp and author. Searchable across the whole CRM.

Pattern: the LinkedIn extension as the only enrichment. Folk users rely on the Chrome extension for adding contacts. The fix: Attio's Chrome extension does the same job, plus AI Research can fill in firmographic data after the contact is created.

Traps to avoid

• Migrating every Folk custom field. A clean Attio workspace has fewer attributes than Folk had fields. Prune hard.
• Putting non-deal pipelines on the Deals object. "Investor pipeline" is not a deal pipeline. It belongs on its own object or a list.
• Skipping the data model step. Folk did not require one. Attio rewards one. An hour saves a week.
• Importing People before Companies. The link will not stick.
• Building automations before the import is done. Imports trigger automations. You will create yourself a mess.
• Cancelling Folk too early. Keep the Folk workspace read-only for 30 days as a frozen reference.
• Treating Attio AI like Folk AI. Folk AI is mostly assistive. Attio AI is field-level. The setup work is in prompt design and field schema, not in turning features on.

How long does a Folk to Attio migration take?

For a team of 2 to 5 with under 10,000 records and 3 to 5 pipelines:

• 1 hour for the data model exercise.
• 1 to 2 hours for the export and cleanup pass.
• 1 hour to set up the Attio workspace and objects.
• 30 to 60 minutes for the imports.
• 1 hour for the first three automations.
• 1 to 2 hours for the AI attributes and views.

Realistic timeline: half a day to a day end-to-end.

For larger teams with 20,000-plus records, multiple custom objects, and integrations to wire (HubSpot Marketing, ActiveCampaign, internal product DB), plan a full week. Most of the week is not the import. It is rebuilding workflows and AI attributes on top.

How much does it cost?

The migration itself is free. Attio does not charge to import.

Ongoing cost depends on team size and AI usage.

• Free plan: up to 3 users, all the core CRM, limited AI credits. Many small teams never leave this tier.
• Plus plan: $34 per user per month, more credits, full workflow features.
• Pro plan: $69 per user per month, full Call Intelligence and Research Agent.

Folk's Standard plan is in the same range as Plus. Folk Premium is in the range of Pro. The headline price is rarely the deciding factor on this migration. The deciding factor is whether the data model and AI ceiling are blocking work.

For a side-by-side on pricing, see Attio pricing explained.

Frequently asked

Will my email and meeting history come over?

Folk does not export full email and meeting history in a way that reimports cleanly. The practical move: connect Gmail or Outlook to Attio so activity flows in from migration day forward. Historical email exists in your inbox already and remains searchable there.

What about the LinkedIn extension?

Attio has its own Chrome extension that does the same job: add or update People and Companies from LinkedIn into Attio with one click. Install it on day one of the migration so the team's habit moves over with them.

Can I run both Folk and Attio for a period?

Yes, and it is the safer move. Freeze new data entry in Folk on import day, but keep the workspace readable for 30 days. If anything is missing in Attio, the source is still there.

Do I need the API?

No, for the typical Folk migration. Direct CSV import handles everything Folk exports. The API matters when you have over 50,000 records, multiple related custom objects, or you want to wire Attio into a product database.

Will my team adopt it?

This is the real question on every migration. The honest answer: a CRM only sticks if the team uses it for the work they would have done in the old tool anyway. Two things that move the needle: (1) one or two automations that obviously save time in week one, (2) a single view they open every morning that replaces the old "open Folk" habit. Build for those two before anything else.

Sources

• Folk: Export your data
• Attio: Import data via CSV
• Attio: CSV import formatting guide
• Attio: Define your data model
• Attio: Automations
• Attio: AI features
• Attio: Pricing
• Attio: API documentation

Free audit of your Attio workspace

If you want a second pair of eyes on the data model before you cut over, or you want the Folk to Attio migration handled for you, I run a free 48-hour audit. You add me to your workspace as an Attio expert, no extra seat and no billing. I send back a one-page written teardown ranked by impact, the three highest-leverage fixes with the exact setting change, and a 5-minute Loom walking through the top fix. No call, no pitch. 5 slots a week.

Get your free Attio audit