Navatar has launched an AI-powered CRM operating model on Salesforce, aimed at alternative asset management.
At the center is what Navatar calls a single AI Deal Engine, which is designed to run continuously across the entire investment lifecycle, from sourcing to fundraising, without relying on constant manual data entry. The firm is targeting private equity, private credit, real assets, infrastructure, real estate, and secondaries managers.
Alternative asset managers have spent years customizing their CRM systems to track relationships, deals and investors, only to find themselves still buried in spreadsheets, emails and manual updates. Navatar anticipates that the next evolution of CRM in private markets looks less like a database and more like an operating model.
How AI-Powered CRM Operating Models Can Reshape Dealmaking
CRM has long been positioned as the system of record for client and deal data. But across financial services, and private markets especially, that promise has been hard to realize. Data is fragmented across teams, strategies, and tools, and CRM adoption often drops once deal activity heats up.
Recent research from McKinsey shows that managers in private markets firms still
“work within siloed data environments with no comprehensive, fit-for-purpose, front-to-back platform, making it difficult to integrate diverse data sources.”
Getting the most value out of AI will require coherent, end-to-end operating models instead of patchworks of individual tools, the report added.
Navatar’s approach aligns with a broader shift underway in CRM, as businesses move from static record-keeping to systems that actively deliver insights and next steps.
Recent research from McKinsey & Company notes that many private markets firms are still “working within siloed data and operating environments,” and argues that AI only delivers real value when it’s embedded in coherent, end-to-end operating models rather than bolted onto individual tools.
AI Deal Engine is designed to capture intelligence as it’s created, whether in meetings, emails, deal reviews, or portfolio discussions. It maintains institutional context and aims to advance work automatically across sourcing, diligence, execution, portfolio management, and investor engagement, without relying on manual CRM updates or spreadsheets.
Unlike generic AI assistants, the model is built around the specific realities of alternative asset strategies:
Private equity and growth equity teams get earlier visibility into relevant opportunities, with AI linking new situations to investment theses and past deals.
Private credit teams can unify borrower, sponsor, intermediary, and market data, while AI benchmarks structures and flags emerging risks.
