Turn Customer Data into Revenue with AI

The answer depends on how differentiated your relationship data model is. If your sales process fits the standard contact-account-deal schema, extending Salesforce or HubSpot with custom fields, automations, and integrations is faster and cheaper — a focused customization project runs $20K–$80K. If your entities don't map to the standard model (investor syndicates, property portfolios, franchise relationships, church membership structures), a purpose-built CRM on your own stack is almost always cheaper to operate long-term than bending a packaged tool. We'll give you an honest comparison in the scoping call.

Data migration is where most CRM projects lose months. We run a pre-migration audit: deduplication by email/phone/domain, field-mapping from your source system, identification of orphaned records, and a conflict-resolution ruleset for records that match on multiple identifiers. We migrate in dry-run batches to staging first, measure match rates, then promote to production with a documented rollback path. Expect two to four weeks for a full migration of 100K+ records with complex relationships.

Yes, and we do this regularly. The critical design decision is write ownership: which system is authoritative for each entity type and which direction wins on conflict. We implement this as an event-driven integration (webhooks + queue) rather than polling, with a conflict log and alert on every resolution. This makes the sync auditable and debuggable — you can see exactly what happened when a field disagreed rather than guessing why records don't match.

A focused MVP — contacts, companies, pipeline, basic activity log, one external integration — runs $60K–$150K over 12–18 weeks. Full-featured platforms with multi-team access control, document management, email sync, reporting, and multiple integrations run $150K–$400K+. We scope by phase: the first phase delivers something your team can use and validate before committing the full budget.

Yes. We build AI layers on top of existing CRM data without requiring a rebuild. Common patterns: GPT-based call and email summarization writing back to the contact record, ML lead-scoring on historical conversion data, next-best-action recommendations surfaced in the CRM UI, and automated firmographic enrichment from company websites and third-party data APIs. These can be added to a custom CRM or integrated alongside Salesforce/HubSpot.