We Promised AI for Private Markets. It’s Here.
By Andrey Volosevich
A little over a year ago, I shared thoughts on what the application of AI could mean for private markets — and previewed the first feature we piloted. I ended that post with a simple note: “stay tuned.”
Since then, we’ve been focused on turning our vision into a working product. We’ve been building, refining, and partnering with some exceptional clients to test, tune, and validate these new capabilities across multiple workflows. Now, InvestorFlow AI Assistant is live, fully incorporated in production, and delivering tangible value across the fundraising, deal, and investor relations teams.
InvestorFlow AI is a deeply embedded assistant — purpose-built to automate low-value tasks, surface insights, and quietly boost productivity by delivering exactly where users already engage in meetings, emails, pipelines, and CRM workflows.
What It Does — and What Changes Because of It
InvestorFlow AI marks a shift in how your team works, prepares, and communicates. We built InvestorFlow AI on a simple premise: the best way to introduce AI to users is to make it effortless. Adoption starts with trust — earned by adding value and meeting users where they already are, without asking them to change how they work.
Whether you’re a managing partner looking for firmwide visibility, a fundraiser engaging with prospective LPs, an investment professional reviewing potential deals, or an analyst prepping for meetings, InvestorFlow AI fits naturally into your flow. InvestorFlow AI is designed to feel like an extra team member: quietly and diligently preparing summaries and agendas for meetings, capturing insights, updating CRM records, and surfacing insights that matter — all without interruption or added effort.
This is AI that shows up where the work happens — in your meetings, notes, tasks, and pipelines — and provides additional focus as well as time back every day. Here are some examples of what it does, and the difference it makes:
📅 Meeting Intelligence & Preparation
Before: Analysts and associates scramble to manually compile company profiles, interaction histories and recent news the night before meetings — extremely time-consuming and often incomplete.
Now: InvestorFlow AI automatically generates a 360° meeting prep summary — combining firm data, CRM history, past correspondence, and external signals — and delivers it straight to your inbox. Each brief includes context on the meeting’s purpose, relationship status, pending follow-ups, attendee-specific notes, and even how this meeting has been discussed in prior interactions. You’re reminded of the moments that matter — strategic risks, open questions, a personal touchpoints — so you and your colleagues walk into the meeting aligned, prepared, and focused. For high-profile meetings, a human-in-the-loop workflow ensures the summary is reviewed and refined before delivery.
📝 Notes → Insights
Before: Everyone takes notes in their own format using different tools. Details get missed; structure is inconsistent, and someone is left trying to stitch it all together later. CRM updates are often delayed or skipped entirely. As a result, valuable proprietary data and insights stay buried, scattered, or lost over time.
Now: Notes are structured and parsed automatically. LP investing preferences, target company financials, and key events are extracted and logged automatically from any scrap of meeting note or email into the system. The result is better and more actionable information for teams:
- Fundraisers can generate segmented pipelines with a click — whether launching a new fund, exploring co-investments, or re-engaging interested LPs.
- IR and marketing teams tailor outreach based on captured preferences, ensuring communications are relevant — and avoiding missteps like pitching products to sponsors who've expressed disinterest.
- Deal teams gain portfolio-wide visibility, enabling better reporting and unlocking new use cases — such as profiling for secondaries or identifying targets for new strategies. Deal flow is shaped by facts recorded during interactions.
- Firms as a whole benefit from reduced communication friction. Data moves across teams, asset classes, and verticals — enabling coordinated, data-driven collaboration.
↻ From Notes to Next Steps
Before: Follow-ups are ad hoc. Tasks fall through the cracks. CRM records often lag well behind.
Now: InvestorFlow AI suggests follow-ups based on the meeting notes — populating next steps and nudges for the team. Due dates are automatically and intelligently assigned.
📊 Executive Visibility: Fundraise 360
Before: Managing directors rely on hallway chats, spreadsheets, and dashboards that are days out of sync. Visibility is fragmented and nuance gets lost.
Now: InvestorFlow AI delivers a weekly briefing covering the full fundraising story — interest shifts, blockers, and strategic opportunities — without anyone needing to chase updates.
🔌 Embedded in the Workflow
Before: Jumping between multiple browser tabs. Manual copy/paste. Lost momentum.
Now: Everything flows where you already work:
- In Outlook or Gmail
- Inside CRM record views
- Within your familiar UI embedded in SmartLists, FlexLists, and pre-meeting briefings
For technical teams: No overhaul or complex implementation required — InvestorFlow AI integrates with your existing infrastructure and workflows out of the box.
For users: No training videos. No remembering commands or prompts. Just results.
Under the Hood: Real Private Markets Workflow AI Takes More Than an LLM and a RAG pipeline
Delivering real business impact takes more than just calling an LLM (large language model) and supplying it data from your CRM system. It requires precision, domain fluency, and seamless integration into workflows that are already time-pressured and highly bespoke. With this in mind, we built InvestorFlow AI as a trusted assistant that understands the context, cadence, and complexity of the private markets.
We’ve been deeply focused on real-world edge cases — analyzing thousands of meeting notes, tuning extractors for hybrid structures, and building heuristics that mirror how deal, fundraising and investor relations teams process information.
Here’s what it takes under the hood:
- Private markets-specific data schema: Tailored data model that represents complex entity types like LPs, GPs, consultants, funds, commitments, asset classes, financials, investment theses — and their relationships with one another.
- Proprietary extractors: Designed to detect and normalize capital preferences, timeline indicators, fundraising signals, and financial metrics — with contextual awareness drawn from CRM, calendar, past engagements and external data providers.
- Multi-layered prompt architecture with pre- and post- processing: Outputs aren’t generated in one shot — they’re orchestrated through structured pipelines with domain-specific augmentation, validation, and fallback logic.
- Semantic matching across entities and intent: Goes beyond what an LLM does out of the box. It captures intent, aligns adjacent terms, and ties everything back to structured CRM hierarchies for accurate recall and feature activation.
- Confidence-based routing and fail-safes: When a model isn’t sure, it flags or defers instead of guessing — preserving trust and protecting data quality.
- Agent-level intelligence: The assistant behaves differently depending on the user context — whether in a context of deal origination, fundraising or investor services — adapting language, summaries, and prompts to match how each role works.
And perhaps most importantly, every insight is grounded in real user behavior and feedback. It was shaped in close partnership with clients, through the lens of live workflows, actual meetings, notes, and deals as well as the daily needs of professionals in the field.
What We Learned Along the Way
We didn’t create InvestorFlow AI in a vacuum. Our early access customers shaped every aspect, from which data points matter for signaling compelling events and key metrics to what “summary” really means in an industry context. (Hint: It’s much more than a summary — it’s a strategic snapshot that captures progress, opportunities, risks, next steps, key developments, and ideas, all tailored to the user’s role and business context.)
We learned that AI is only useful if it earns your trust:
- By not missing essential details
- By not interrupting the flow
- By not adding cognitive overhead
- By making each team’s job easier, prioritizing substance over style, and clarity over complexity
White-glove onboarding wasn’t a luxury — it was essential and remains so. We’re continuing to treat each deployment like an outcome-based partnership rather than a plug-and-play product activation.
Final Thoughts: From Tool to Teammate
When we envisioned this, our goal was to help dealmakers, fundraisers, and relationship managers spend less time typing — and more time doing and getting results. With InvestorFlow AI, that’s now becoming a reality.
To get a sense of how it could fit into your team’s workflow, reach out to your Client Success Manager or book a demo — we’d love to show you what it looks like in action.
InvestorFlow AI is here — your new team member has arrived! Let’s get to work.
Appendix: Early Adopter Case Study
One of our early adopters achieved the results below after activating a focused subset of capabilities — value realized across teams and workflows has been substantial.
📈 ~10× Increase in Data Entered into CRM
Over the past year, teams across capital formation, capital deployment, and investor relations had consistently taken detailed meeting notes — but much of that proprietary insight never made it into the CRM. It was effectively buried in free text, disconnected from workflows and lost to follow-up. After deploying InvestorFlow AI, an early adopter saw the rate of structured data captured and logged into the system increase tenfold.
Key elements like investment preferences, co-invest interest, and industry focus are now automatically extracted and mapped to the right records. This surfaced hidden signals, made more relationships searchable, and dramatically improved cross-team visibility and institutional memory. The structure of the data enabled automatic construction of a fundraising pipeline based on investor profiles.
💰 ~5.5× Increase in Financial KPIs Logged
Deal professionals routinely gather financials — ARR, EBIDTA, margins, employee and revenue growth — in conversations with portfolio and target companies. However, this information is recorded in meeting notes and emails, and the CRM fields intended to store this data often stay empty. InvestorFlow AI now captures and structures these financial and performance metrics automatically, increasing coverage by more than 5× for one early adopter.
One notable result: a newly launched investment strategy team was able to identify and prioritize candidates based on these proprietary KPIs, something they couldn’t do before. The application of InvestorFlow AI transformed scattered information into a strategic asset and enabled a new targeting strategy.
📅 ~15× Increase in Actionable Insights Extracted
Timing is everything in deal origination — compelling events set deals in motion — but key timing signals often sit locked inside meeting notes. One firm saw a 15× increase in “actionable dates” automatically extracted and logged via InvestorFlow AI, allowing for smarter engagement decisions and better-targeted follow-ups.
Instead of relying on memory or reading back through notes, the team now auto-generates prioritized target lists based on timing cues like “considering options early next year,” “expecting a liquidity event this summer,” or “open to conversations post-earnings.” The risk of overlooking a critical signal and missing a deal is significantly reduced.
⏱ ~18 Weeks of Work Offloaded to AI
In just 10 months, InvestorFlow AI offloaded the equivalent of 18 weeks of manual effort — from compiling prep materials to structuring notes and logging data — for one client.
Deal teams can now spend that time building relationships, developing strategy, and executing — not copying text into fields. The Assistant works silently in the background, capturing value without adding process overhead.