Every private markets firm is running the same experiment right now: hand Claude to deal teams and see what happens. The results are often impressive — faster research, quicker document summaries, sharper first drafts. But ask most managing partners a follow-up question and the conversation changes tone fast: where exactly is our LP, deal, and portfolio data going when someone pastes it into an AI tool?
That question isn't paranoia. A 2026 Cambridge Judge Business School report on AI in financial services named data privacy and unreliable model outputs as the two risks that industry participants, vendors, and regulators raise most often. Navatar built its new governed AI framework directly in response to that gap — giving private equity and M&A advisory firms a structured way to use frontier-model reasoning without losing control over sensitive information.
Selective, not all-or-nothing
Navatar's framework rejects the false choice between "open-ended AI model" and "locked-down static CRM." Instead, it makes AI usage selective, structured, and governed by design, splitting the work across four layers:
- Navatar CRM - the structured, relational system of record for funds, LPs, relationships, pipeline, and portfolio activity.
- Navatar AI - the always-on operational layer: proactive signals, pre-built private-markets actions, and persistent shared context on every record.
- Salesforce Agentforce - the governed agent layer, applying the firm's access controls, data permissions, and workflow guardrails inside Salesforce.
- Claude - the on-demand reasoning layer, invoked selectively for cross-pipeline analysis, scenario modeling, competitive research, and other work that benefits from broad, freeform reasoning.
Agentforce sits at the center of the control model. Within Salesforce, it determines what an agent can retrieve, which actions it's allowed to take, and what stays inside the firm's controlled environment. Salesforce's Trust Layer sits between agents and any large language model, with configurable data masking and zero data retention available for third-party model providers. Navatar layers private-markets-specific rules on top — fund structures, LP relationships, deal pipelines, and portfolio records — so the guardrails reflect how a PE or M&A firm actually operates, not a generic enterprise workflow.
The problem this solves
Left unmanaged, AI adoption tends to follow a familiar and costly pattern: individual employees using general-purpose tools with inconsistent prompts, disconnected data sources, and no shared record of what AI recommended or what action was actually taken. Navatar's framework is built to prevent exactly that — keeping AI-assisted intelligence tied to the underlying deal record so the whole team benefits from it, not just the person who asked the question.
That matters most at the moments firms can least afford inconsistency: investment committee prep, LP reporting, and deal handovers between team members. A defensible, reviewable operational record isn't a nice-to-have in those situations — it's the whole point of having a CRM in the first place.
Control that firms actually set
The result is control by design. Firms — not the AI vendor, not the model provider — decide which workflows stay inside Navatar and Agentforce, which data is eligible for external-model reasoning, and when Claude gets used at all. That's a materially different proposition than "turn AI on and hope for the best," and it's the one private markets leadership teams have been asking for since generative AI adoption started accelerating industry-wide.
Available now
Navatar's governed AI framework is live today, with dedicated resources for both sides of the business:
- Private equity firms
- M&A advisory firms
Book a demo to see how it works on your own pipeline.