When you export Affinity, you get names, emails, and companies. What you lose is the relationship graph—the web of who knows whom, how deep each connection runs, and what stage each relationship sits in. For wealth advisors and relationship-driven sales teams, that graph is the business. A flat CSV is just a contact list. This playbook identifies the three data gaps that kill context, then maps a 60-day rebuild sequence using Orin's CRM, built-in AI, and automations . You'll recover connection metadata, relationship stage, and interaction history without starting from zero. The three data gaps Affinity export leaves behind Affinity stores relationship depth in its database architecture, not in fields you can export. When you pull a CSV, you lose three critical layers: 1. Connection metadata (who knows whom) Affinity tracks secondary, tertiary, and indirect relationships. You know Alice in Singapore works at Bank X. Affinity also knows Alice introduced you to Bob at Bank X three years ago, and Bob later referred you to Carol at Investment Fund Y. That chain is relationship capital. The export gives you Alice, Bob, Carol as separate rows. It does not give you the introduction chain or the introduction date. You lose the map of influence flow and the timing of how relationships matured. 2. Relationship stage and implicit scoring Affinity infers relationship strength from interaction frequency, recency, and the depth of your message history with each contact. A contact you email weekly scores higher than one you email quarterly. A contact you've known for 10 years scores higher than one you've known for 10 weeks. The export does not capture these implicit rankings or stage gates. You'll see interaction count (if Affinity synced email), but you won't see Affinity's own relationship scoring logic or the decision rules that Affinity applied to rank contacts by influence or readiness. 3. Interaction history and context tags Affinity lets you tag interactions (e.g., "investor interest," "warm introduction," "follow-up needed"). The export includes email body text if you've synced, but it does not include Affinity's metadata tags on those interactions—the reason you tagged them, the urgency you flagged, or the action you set as a follow-up. You get the raw email thread. You lose the business logic you applied to that thread. The export is not a backup of your relationship graph. It's a list of contacts with email addresses. Everything else—introduction chains, strength scores, decision tags—lives only in Affinity's app. Step 1: Audit what you're actually exporting (days 1–5) Before you move, know what you have and what you're leaving behind. This saves rework later. Export your Affinity data as CSV. Go to Settings > Data > Export. You'll get contact fields, company fields, and email sync data if enabled. Download the file and open it in a spreadsheet. Inventory the columns you actually use. Most teams use 20% of the available fields. Mark which ones carry decision logic: decision stage, source, relationship type, last interaction date, next action. Columns like "notes" or "tags" often contain unstructured relationship context—flag these for manual review. Identify the contacts with the deepest relationship metadata. Sort by interaction count or by date added. Your oldest and most-contacted relationships have the most context in Affinity. These are your rebuild priorities. Check for email sync completeness. If you synced Affinity with email, you'll have email thread data embedded in Affinity's database. Export those threads separately (if Affinity lets you) or note which contacts have sync enabled. Without sync, you're rebuilding from memory. Document the relationship scoring you used verbally. Sit with your team: How did you actually rank relationships? What made someone "warm" vs "cold"? What triggered a follow-up? Write this down—it's your rebuild rulebook. Step 2: Map and rebuild connection metadata in Orin (days 6–25) Import your CSV into Orin's CRM , then rebuild the introduction chains and relationship topology layer by layer. Import and deduplicate Upload your Affinity CSV to Orin. Orin's import tool will flag likely duplicates (same email, similar name, same company). Review and merge. Duplicates sink relationship depth, so spend time here—do not auto-merge without checking. Recreate introduction and referral pathways Orin's CRM supports linked records and custom relationship fields. For each contact, you can define how they entered your network: Direct (you met them directly) Introduced by [contact name] on [date] Referred by [contact name] Inbound inquiry Conference or event Go through your imported contacts in cohorts of 50. For each, ask: "Who did I know first here, and who introduced me to whom?" Update the introduction field. This takes 2–3 hours per 50 contacts for a team that's kept notes, or 5–6 hours if you're rebuilding from memory and email history. Layer in secondary relationships Create a custom