You have 547 contacts in Affinity. Their deal velocity, warm intro chains, interaction history, and note context are the actual competitive advantage. Then you export to CSV, import into Pipedrive or Orin, and what arrives is a skeleton: names, emails, company, maybe a deal stage. The relationship graph—the thing that made Affinity useful—is gone. This is not a bug. It's how relational databases work. Affinity stores relationship depth in linked records, interaction timelines, and contextual notes. A CSV export flattens all of that into columns. Some fields port cleanly. Others orphan entirely, and you have 30 days to decide: rebuild by hand, script a recovery, or accept the loss. I've walked through real Affinity exports (500+ contacts, 12+ months of interaction history) and mapped which fields vanish, which survive mangled, and how to recover the relationship depth that drives your pipeline. Here's the playbook. The nine fields that don't survive Affinity export Affinity's interface shows you a rich contact graph. Its export gives you this: Interaction history (timestamps, context, who initiated). Affinity records every email, call, and manual touchpoint. Export flattens this to a single text field or drops it entirely. You lose the timeline that tells you whether a lead is warming or cooling. Warm introduction chains. Affinity's core feature is mapping who knows whom. It stores these as relationship objects with direction, strength, and mediator. CSV export has no column for 'introduced by Person X on Date Y via Email Z.' You get contact names and maybe a note saying 'warm intro.' That's it. Note context and tagging. Notes in Affinity are queryable, date-stamped, and tagged. On export, they become a single text dump per contact. No timestamps, no tags, no indication which note triggered a deal move. Deal-to-contact relationship type. Is this contact a champion, economic buyer, influencer, or blocker? Affinity stores this as a relationship property. Most exports flatten it to 'associated with deal X.' Interaction frequency and recency. Affinity computes how recently and how often you've touched a contact. This drives pipeline health scoring. Export gives you the raw data (if at all), not the computed signal. Email thread nesting and replied-to-by direction. Affinity knows whether you initiated contact or they did, and traces reply chains. Export loses this. You see an email timestamp, not the conversational depth. Account hierarchy and subsidiaries. Some contacts are children of parent orgs in Affinity. Export flattens all of them to 'Company Name,' losing roll-up and multi-entity deal routing. Mutual connection data and intro strength. Affinity scores warm intros by degrees of separation and mutual-connection overlap. CSV has no mechanism to represent this. Custom relationship fields and org role attributes. If you've built a taxonomy of role types, seniority, or buying-committee membership, these are custom fields. They export as text only, losing any linked-record semantics. What actually survives the export (and how it arrives broken) Not everything dies. But what survives often arrives mangled: Contact names, emails, phones: Survive cleanly. No recovery needed. Company name and domain: Survive cleanly, though you may lose parent-org context. Deal name, amount, close date, stage: Survive, but stage names may not map to your destination tool's pipeline stages. You'll need a translation table. Notes and descriptions: Survive as text, but lose timestamps, tags, and interaction context. A note that reads 'customer said budget cycles Feb' is now indistinguishable from 'decision pending' without the date. Custom fields: Export if they're simple scalars (text, number, dropdown). Linked relationships become text references only. Tags: Often export as a comma-separated string in one column. Rebuild-heavy. Interaction dates: If Affinity exports a 'last interaction' timestamp, it arrives as a single date, not a history. You lose the narrative. Why 30 days is your real deadline The moment you go live on Pipedrive or Orin's CRM , your team starts entering new notes and logging new interactions into the new tool. After that, merging in Affinity's old relationship data becomes messy: which note is newer? Did this deal move forward after the migration, or was it a stale Affinity record? Your window to backfill relationship depth is the first month. After 30 days, the cost of manual reconciliation (and the risk of duplication) outweighs the value of the recovery. The 60-day rebuild map: Which fields to recover first Week 1–2: Audit and prioritize. Export your Affinity database to CSV. Open it in a spreadsheet. Count how many contacts have empty 'interaction history' columns. Count contacts with multiple notes. Flag deals that are stage-critical or high-ACV (above ₹15L). These are your recovery priorities, not all 547 contacts. For a 500+ contact base, assume you'll manually reconstruct: All warm introductions for deal