Category: AI in Private Equity

The Fundraise Can Be Lost Before It Begins

Posted on October 5, 2026 by - AI & Automation, AI Adoption & Enablement, AI in Private Equity, Analytics & Reporting, Data Readiness for AI, Data Strategy & Analytics, Fundraising Efficiency, Fundraising Strategy, Fundraising workflows, Investor Relations & LP Experience, Uncategorized

The Fundraise Can Be Lost Before It Begins

Part 2 of 4: AI Strategy in Private Markets

By Sebastian Holst, Chief Product Officer

In the first post of this series, we argued that every AI strategy has to answer four questions: what advantages does AI create, what are the risks, what is the integration plan, and in what sequence should that plan be executed? 

The first two questions, what the advantages are and what the risks are, have answers specific to each firm. This post expands on the advantages and risks for the relationship that matters most to a GP: the one with the LP.

What AI can do for the GP/LP relationship

The obvious answers to the question of AI advantages are already familiar:

  • Draft an LP email
  • Read a year of interactions and estimate how healthy a relationship is.
  • Summarize a call and flag an overdue follow-up 
  • Fill in the fields nobody filled in

These are useful, and every serious platform will offer some version of them. They are also generic AI use cases with a private markets vocabulary applied. A firm that stops there has bought a faster version of work it already does.

The strategic advantage is the firm’s capacity to hold LP relationships at depth. Today that capacity is set by headcount and memory. A team of three can know forty relationships well, and the rest get the newsletter. Institutional knowledge like who cares about what, what was promised in the last meeting, and which contact is the actual decision-maker, lives with the people who happen to cover the account. And then it’s lost when they leave the firm. 

The advantage AI can deliver, if it’s built for this purpose, is that this deep relationship context, the kind of context that isn’t typically found in an account record, is housed and understood across the firm, all the time. Partner-level judgment can be applied on any of them at the moment it matters. 

Why LP relationship depth matters more between fundraises

Capital is concentrating with managers LPs already know. Bain’s 2026 Global Private Equity Report finds that 53% of LPs are constrained from making new commitments because capital they already pledged has not been called, up 15 points in a year. The number of buyout funds reaching a close fell 23%. In this market, the relationship a firm carries into the fundraise decides most of the outcome before the first meeting.

The benefits of a deeper relationship compound in ways that show up across the private markets business:

  • An LP who knows the firm re-ups, and at a larger allocation
  • Diligence moves faster, because the DDQ confirms what is already established rather than examining it
  • First closes get anchored, which pulls the rest of the roster forward
  • Co-investment and separate account conversations get a serious hearing
  • A continuation vehicle has real support behind it when the firm needs one
  • A difficult quarter comes with room, and with people who speak well of the firm to the consultants and peers who shape the next fundraise

Every one of those outcomes is earned in the quarters when no fund is in market and where relationship capital is built through interactions like reporting delivered on time, questions answered fully, and follow-ups kept.

How LP relationships are lost between fundraises

It’s in these same quarters where relationships are lost, and they’re lost quietly. Most of the LPs a firm loses are still on the list when the fundraise opens. They took the calls, they received every report, there’s nothing in the file that indicates anything changed. Then the commitment comes back smaller, or as a polite note that this cycle is a pass. 

This kicks off an internal process where the team tries to dissect the moment it went wrong. Was it a quarterly report that landed late during the LP’s reallocation? A champion who moved to a different mandate and a successor who never got the call? A follow-up from the annual meeting that stayed in a drafts folder?

Commitments recede before anyone declines. A firm can lose an LP before the fundraise begins and still has to earn every commitment when it does.

Why CRM and a disciplined IR team stopped being enough

Ten years ago, a good CRM and a disciplined IR team was a system that worked. Both still matter, what’s changed is the shape of the business around them. Firms added continuation vehicles, co-investment sleeves, separately managed accounts, and new strategies to stay relevant to the same LPs. Each addition brought its own reporting calendar, its own LP segment, and one more place for the story to diverge. 

In Altvia’s interviews with IR teams this year, the recurring theme was learning about an important LP change later than they wanted and not having a system that would have surfaced it. They all knew the data was somewhere within the firm, but nobody had time to find it.

The risk question: the LP relationship answer

The generic risks are leakage and fabrication, and every vendor addresses them. The specific risks within an LP relationship define what the tools have to do.

Under the SEC Marketing Rule, a communication that offers advisory services to prospective or current private fund investors can be considered an advertisement and carries the obligations that follow. The line between an ordinary LP update and an advertisement is not always obvious. A system that drafts for IR therefore has to stop before the send and wait for a person to take the next step.

Additionally, every figure carries the firm’s reputation, so a number with no visible source is a number nobody can defend in the meeting. Partners want to know what the model can access and where the data goes. In one beta conversation this year a partner returned to that question three separate times. 

And the IR and fundraising function is starting from behind: in Altvia’s forthcoming 2026 Private Markets Survey, investor relations and fundraising respondents report the lowest AI maturity of any function, a mean of 3.1 on a 0 to 5 scale, and the lowest confidence in AI accuracy, 29% against 47% of all respondents. A strategy that hands them a general-purpose assistant and a training session has misjudged both the advantage and the risk.

A Standard Emerges

Put the advantage and the risks together and a standard emerges:

  1. AI for the LP relationship holds the full relationship in one place, with the deal and fund context that shapes it, so the firm’s knowledge of an LP no longer depends on who covers the account. 
  2. It captures that relationship with less manual effort each quarter, because a system that depends on someone remembering to update it inherits the failure it was meant to fix. 
  3. It brings the right slice of the relationship to the right person at the right moment, with the source shown. 
  4. And it proposes while a person approves.

That standard is what Altvia means by a GP Engagement Platform. Stay tuned! The next post in this series delves into the third question: what’s the plan, in practice?

Sources

Bain & Company, Global Private Equity Report 2026, Fund-raising section (published February 22, 2026). https://www.bain.com/insights/outlook-gaining-traction-global-private-equity-report-2026/

U.S. Securities and Exchange Commission, Investment Adviser Marketing, Release No. IA-5653 (the Marketing Rule, Rule 206(4)-1 under the Investment Advisers Act of 1940). https://www.sec.gov/rules-regulations/2020/12/investment-adviser-marketing

Four Questions Every Private Markets AI Strategy Has to Answer

Posted on September 28, 2026 by - AI & Automation, AI Adoption & Enablement, AI in Private Equity, Analytics & Reporting, Data Readiness for AI, Data Strategy & Analytics, Investor Relations & LP Experience, Uncategorized

Four Questions Every Private Markets AI Strategy Has to Answer

By Sebastian Holst, Chief Product Officer, Altvia

Part 1 of 4: AI Strategy in Private Markets

The short answer

An AI strategy in private markets has to answer four questions. What advantage does AI create in productivity, innovation, and quality? What risks come with it? What is the specific plan to integrate it? And in what sequence should that plan be executed, and how fast? For a general partner (GP), the answers turn on fundraising, investor relations, and deal sourcing, because that is where the firm’s longest relationships are and its highest compliance exposure.

The four questions an AI strategy has to answer

Every effective AI strategy answers four questions for its stakeholders:

  1. What advantages can AI offer in productivity, innovation, and quality?
  2. What risks and vulnerabilities come with integrating it, across the full range of use cases a firm might attempt?

Given honest answers to the first two:

  1. What is the specific plan to integrate AI?
  2. And in what sequence should that plan be executed, and how fast?

The four questions apply to any organization in any industry. The answers do not. Where private markets differ is in how the answers affect fundraising, investor relations, and deal sourcing.

Question 1: What are the advantages in productivity, innovation, and quality?

Sure, the generic answer centers around faster drafting, summarized records, and cleaner pipelines. Those are helpful, they’re tablestakes, and they improve the workflow without improving the decisions. The advantage that matters most to a GP is continuity of context across a fund cycle. 

Bain’s 2026 Global Private Equity Report finds that 53% of limited partners (LPs) are constrained from making new commitments because capital they already pledged has not been called, up 15 points in a year, while the number of buyout funds reaching a close fell 23%. In a market that tight, an LP going quiet is expensive, and it is hard to see day to day. 

It shows up as an LP that receded quietly during the quarters before launch — a commitment lost before the work began. Nobody decides to disengage; the engagement just thins. What changes the outcome is a system that reads the drift, explains what is driving it, and puts it in front of the person who covers the relationship while there is still time to do something about it.

Context retention is the difference between AI that works and AI that does not

MIT’s NANDA initiative found in its 2025 State of AI in Business report that 63% of executives want AI that retains context, and it named the ability to build on what happened before as the core barrier separating successful deployments from the rest.

Bring that into a GP’s world. A successful deployment retains the context of an LP relationship that spans years, vehicles, and people. It also learns from that relationship, and gets sharper with every interaction.

The measurable gain is less rework

In conversations I have had with Heads of IR, the advantage they actually measure is the reduction in rework and double-checking. An AI system that produces a figure a partner then has to go and verify has not improved the decision, and it has not saved anyone any time.

Question 2: What are the risks of AI in private markets?

Here too, the generic answer holds true: data leakage and fabricated output. And every AI vendor addresses those. But the risks specific to private markets are regulatory and reputational before they are technical. Data leakage and fabricated output apply to every industry, and every vendor addresses them. GPs must also assess three risks specific to their own firm:

  • Regulatory (SEC Marketing Rule). A page that reaches an LP is an advertisement, and it carries the obligations that come with that.
  • Compliance. An LP email, a quarterly letter, or a data-room summary sent without a person reviewing it is a compliance event.
  • Reputation. Every figure that reaches an LP carries the firm’s reputation, and a number with no visible source is a number nobody can defend in the meeting.

Partners repeatedly ask what the AI model can access and where the data goes. In our 2026 Private Markets Survey, security becomes the top barrier once firms reach production, displacing the skills and integration concerns that dominate earlier stages.  In private markets, that barrier has a compliance officer’s name on it.

The structural risk: the function that owns the LP relationship is the least equipped to evaluate AI

There is a structural risk at play as well. In Altvia’s forthcoming 2026 Private Markets Survey, investor relations and fundraising professionals trust AI accuracy less than any other function: 29% against 47% overall. They name a lack of in-house skills as their top barrier. A strategy that hands this function a general-purpose tool and a training session has misjudged the risk.

Why general-purpose AI answers are not sufficient for private markets

Horizontal CRM, document, and marketing platforms answer the first two questions generically for every industry at once. A GP’s answers rely on things those platforms do not account for: commitments that attach to a subsidiary’s vehicle and roll up to a parent, side letters whose obligations come due on their own calendar, feeders and look-through investors, the reporting cycle, and the person who covers the relationship. The benefit of a system, and its risk, lives in those specifics.

This is where fundraising, IR, and deal sourcing have room to move ahead. The best answers to the first two questions will come from people who understand the LP relationship, working with tools built around it, on the platforms the firm already runs.

Question 3: What is the specific plan to integrate AI?

A plan is not a list of tools. It settles three things: who builds it, where it runs, and what it has to guarantee regardless of the first two. The first two are execution decisions. The third is a property of the system itself, and it is the one most plans leave out. Once the first two questions have honest answers, each of the three has a fairly obvious one.

Four questions a GP has to answer for themselves

1. Who can build and continue to develop the right solution? The NANDA study found that purchased solutions from specialized vendors, paired with partnerships, succeeded about 67% of the time, while internal builds succeeded about one-third as often. The sharpest gap was in financial services. For a firm whose IR function already names in-house skills as its top barrier, building in house compounds the very problem the project is meant to solve. 

2. Where’s the data coming from? AI-ready data does not mean a new warehouse. It means one current representation of the LP relationship spanning the CRM, the investor portal, the data room, and fund reporting all captured with less manual effort each quarter and read by the AI where it already lives. Gartner projects that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data. Most of those projects will not fail at the model. They will fail at the data underneath it.

3. Who’s accountable? It’s a trick question, you are! That’s why it’s so important that every output shows provenance and its paper trail. The system proposes; a person approves. This is not a feature to negotiate at procurement. It is the condition that lets a compliance officer, a partner, and an IR associate say yes to the same system.

4. Build or Buy? The build case fails on economics. The cost that sinks it sits underneath the model. Provenance on every output, entitlements that match the firm’s sharing rules, an audit trail a compliance officer will accept, and change control on models and prompts maintained every quarter thereafter. None of that differentiates one GP from another, and all of it has to be right before a single output reaches an LP. A specialist carries that cost across many firms and improves it with every deployment. A single firm carries it alone, for as long as the system runs.

Building is the right call when three conditions hold together: the workflow is genuinely proprietary, the governance stack already exists and is actively maintained, and the team is staffed to keep it current as models change. Few firms clear all three. When two hold and the third does not, the third is what fails, and it fails after the system is already producing work that reaches LPs.

Question 4: In what sequence should the plan be executed, and how fast?

Sequence first because most firms have it backward, and because the sequence is what sets the pace.

Our survey shows the top barrier shifting as firms mature: skills at the informal stage, integration during pilots, security in production, and return on investment only once AI is already embedded. ROI arrives last. Which means most firms are years into the work before they can say what the work was worth.

Reverse the order.

  1. Start with the return. Choose one use case with an outcome you can measure, and define the measure before the pilot begins rather than after it. If you cannot say what success would look like as a number, you do not have a pilot. You have an experiment.
  2. Require the vendor to demonstrate approval rules, audit trail, and data boundaries. Settle approval rules, the audit trail, and data boundaries while the stakes are still small, so security shows up as a solved problem rather than a blocker at the worst possible moment.
  3. Select a system that already sits where the work happens, so integration is not a project you run. Run inside the systems the team uses now — CRM, investor portal, data room — rather than beside them. Integration stops being a project.
  4. Let the skills barrier resolve itself. The skills gap that stalls informal-stage firms is largely an artifact of building rather than partnering. Answer Question 3 correctly and this one mostly disappears.

That sequence also answers the velocity question. Move quickly through the pilot and deliberately into production. A pilot’s only job is to produce a number, and that should take weeks, not quarters. Production is where the compliance exposure becomes real, and that deserves time. Firms that invert this, like a cautious year-long pilot followed by a fast rollout, take the longest to learn anything and carry the most risk when they finally do.

Pilot-stage firms are already choosing their anchor. In our same survey, 82% of firms at the pilot stage rank LP relationship visibility among their top three objectives, ahead of deal flow, compared with 56% of all other firms.

That is where the industry is placing its first serious AI bet. And because the pilot becomes the system a firm scales, the pilot stage is where that bet is effectively decided.

What is next in this series

Over the next several weeks we will take these questions in turn: the problem the LP relationship poses and the standard AI has to meet, the plan in practice, and what our survey says about sequence. The framework stays the same throughout. The answers are ours, and they are specific to private markets.

Frequently asked questions

What should a private markets AI strategy answer first? It should answer what advantage AI creates in productivity, innovation, and quality, and what risks come with it. Only after those two are answered honestly does a plan, and a sequence for executing that plan, become possible. For a GP, both answers are specific to fundraising, investor relations, and deal sourcing.

What are the biggest AI risks for a private equity firm? Beyond data leakage and fabricated output, three risks are specific to private markets: SEC Marketing Rule exposure, because a page that reaches an LP is an advertisement; compliance exposure from a draft sent without human review; and reputational exposure from any figure that reaches an LP without a visible source.

Should a GP build AI in house or buy from a specialist? MIT’s NANDA research found that organizations that purchased solutions from specialized vendors , paired with partnerships, succeeded about 67% of the time , while internal builds succeeded about one-third as often, with the widest gap in financial services.

What does AI-ready data mean in private markets? AI-ready means one current representation of the LP relationship — across the CRM, the investor portal, the data room, and fund reporting — captured with less manual effort each quarter and read by the AI where it already lives. Gartner projects that through 2026, organizations will abandon 60% of AI projects that lack AI-ready data.

In what order should a GP roll out AI, and where should it start? Start with one use case whose outcome you can measure, and define the measure before the pilot begins. Govern before production, run the pilot inside the systems the team already uses rather than beside them, and partner rather than build so the skills gap does not become the gate. In Altvia’s 2026 Private Markets Survey, 82% of pilot-stage firms rank LP relationship visibility among their top three objectives.

About the author

Sebastian Holst is Chief Product Officer at Altvia, where he leads product strategy for the platform private markets firms use to run fundraising, investor relations, and deal sourcing. This is part one of a four-part series on AI strategy in private markets.

Methodology: Altvia 2026 Private Markets Survey, 187 respondents. Pilot-stage figures rest on 38 respondents and investor relations and fundraising figures on 21. Treat both as directional. All percentages are shares of respondents.

Adoption Was Never the Constraint

Posted on September 25, 2026 by - AI & Automation, AI Adoption & Enablement, AI in Private Equity, Data Readiness for AI, Investor Relations & LP Experience, Uncategorized

AI Adoption Blog

A year of AI conversations in private capital, and the question that is still unsettled.

By Ryan Keough, Altvia

I have spent the past year in a lot of rooms. Customer calls, user sessions, conferences, panels, and more industry reports than I would like to admit reading end to end. AI came up in every single one. What I have not heard, in any of them, is a definitive answer to the bottom line question: where does AI earn its keep?

First, let’s kill the framing that keeps showing up in survey data: adoption is not the constraint. Nearly every firm I talk to is doing something with AI, and most are increasing what they spend on it next year. The interesting variance is not between the firms that have started and the firms that have not. It’s between firms that have deployed very different things under the same word.

Broadly, I see three approaches.

1) Tooling that improves an existing workflow. Summarize the call notes. Draft the first pass of the update. Clean the list. These are real gains, and I do not want to be dismissive of them, a lot of time gets recovered here. But the ceiling is what it is. You are doing the same job, in the same sequence, slightly faster.  

2) Connected models / MCP. This is where most work is happening, and where results vary the most. Connecting a model to your systems is the easy part. What determines the answer is everything underneath: whether the model knows what your fields actually mean, whether it is allowed to see what it is reading, and whether anyone can trace the number back to the record it came from.

Most of what I have seen is built without those three things. A model with open access to a CRM guesses at field names, ignores who is permitted to see what, and returns a figure with no provenance. The output is directionally correct and unverified. Useful for exploration. Not something you take into an LP conversation.

Most of the market is somewhere in one of the first two scenarios. A third approach is:

3) Reimagining the work itself

This is a different approach entirely. Not a faster version of what you do now. This is the hardest to build, and the only one that changes what an IR team’s week looks like.

When thinking about the third approach to AI, creating a different process entirely, you have to start with what the current default costs you. Reporting tells you where you were; it never tells you where you are. A dashboard answers the exact question you thought to ask, not the gist or the inference, and certainly not a follow-up. A chat box is still a blank prompt that is your problem to solve. Each of these waits for someone to go looking, which means coverage ends up being a function of bandwidth, and you end up working the relationships that surfaced this week, not the ones that changed.

Your LP relationships don’t change quarterly. Your visibility shouldn’t either. The re-up conversation shouldn’t be the first time you learn where you stand. When reimagining the best approach, the process needs to be action oriented. It’s always watching, across every relationship rather than the ones on this week’s list, and it notices change when it happens rather than when someone reports it.

Noticing is not enough on its own. Awareness without a recommendation is just another alert, and nobody needs more of those. The bar is a change, an explanation, and a suggested next step, delivered together and in time to matter. Not only what moved, but what the system thinks you should do about it.

From there, the question is whether it can carry that step through. There is a real difference between a system that hands you a recommendation and one that can prepare the outreach, update the record, and move the work forward when you say go. In other words, advice adds to your list, action takes something off of it.

The second major component that is critical to change the work process is…say it with me…data! Listen, the data exists, we know that. It’s a matter of where it exists and with whom. Today, no one person has the whole relationship, it’s spread across your organization and with your CRM, the inbox, the data room, a spreadsheet someone maintains, and a colleague’s memory. The firm as a whole should have it even when no individual does. Institutional memory, without anyone having to remember. That’s what makes the analysis worth acting on, and it is also why this is hard: the data foundation underneath is the real work, and most of the market would rather talk about the model.

When the process changes and the data foundation underneath is working correctly, what changes is both mundane and significant at the same time. Monday morning used to be prep; now it is follow-up. You stop deciding who to call based on who emailed you last. Your team stops being the integration layer between five systems. New hires inherit the relationship instead of starting over. 

How it delivers ROI

Different AI approaches will garner different returns, and it’s up to the firm to decide what they’re looking to accomplish. 

1) Tooling: automate the work, same result. The output is what it always was; it took fewer hours. Real savings, capped savings.

2) Connected models / MCP: Insight without precision. A view you did not have, directionally right, unverified. It informs a decision. It does not make one .

3) Change how the work happens. Always watching, proactive about what it surfaces, and able to take direct action rather than handing the work back as a task.

This approach shows up as work getting done that wasn’t getting done before. Coverage stops depending on how many relationships your team has bandwidth to actively work. 

This approach enables you to be always watching, proactive about what it surfaces, and able to take direct action rather than handing the work back as a task.

The first two save you time. The third gives you coverage you did not have and that is where the return is found, because it shows up in the situations the old way never surfaced at all.

An LP whose engagement has been quiet for two quarters and on nobody’s list because nothing happened.  An opportunity is noticed such as a co-invest appetite visible in the pattern of what someone has been reading, while there is still time to act on it. None of these announce themselves. They are only found by looking, and no team has the hours to look everywhere.

The return is a relationship strengthened before it needed repairing, or an opportunity worked while it was still open. 

This approach is also the only approach that has to earn the right to operate that way. The more a system moves first, the more it has to show its work, every figure tied out to the record it came from, every action proposed before it is taken, every step a person can check.

Human-led by default. Enterprise ready, secure, traceable, and auditable. Those are not the brakes on a proactive system. They are the reason a firm can let one move quickly on its behalf at all.

In the end, Investor Relations can focus on the relationship and opportunities.

Where we are

In June, our CMO wrote that no end-to-end, AI-native IR product exists yet.

That was a fair read at the time, and I did not argue with it.

But we’ve been building. More very soon.

The IR AI Balancing Act, Part Two: LPs Are Asking the Questions Now

Posted on July 26, 2026 by - AI & Automation, AI Adoption & Enablement, AI in Private Equity, Data Readiness for AI, Fundraising Efficiency, Uncategorized

A perspective from Annie Eissler, CMO, Altvia

Last month I wrote about what I heard at PEI’s IR Network meeting: I saw a room full of IR professionals working out, in real time, how to use AI without letting it do the thinking for them. A few weeks later I was back in the room, this time at another PEI IR Network meeting. The same core questions were on the agenda, but this time was different.

Last month’s conversation was mostly internal: how do we use AI? How do we not get it wrong? What will our LPs think if they find out? Last week, the conversation had shifted. LPs aren’t just forming opinions about AI use anymore. They’re asking directly how GPs are using it, and that means GPs have to think differently about their AI strategy.

Here’s what stood out.

Personalized LP communications are the clearest AI use case, and also the trickiest one

The most concrete idea I heard came from a conversation about turning deal memos into short, personalized notes for LPs, rather than generic updates. It’s a good use case: the raw material already exists, and AI is well-suited to reshaping it into something that reads like it was written for one person rather than a distribution list.

But the group was candid about where the line sits. There’s a real distinction between the low-hanging fruit (a quick capsule summary) and what would actually be meaningfully impactful. This almost always requires pulling data from a lot of different sources and takes real work to get right: IC summaries, LP meeting notes, and reporting all live behind different systems, often in different formats. Nobody in the room felt like they’d fully solved this. As one person put it, the real bottleneck is data quality: garbage in, garbage out. The hard part isn’t the AI, it’s getting information into a usable format in the first place. It’s a theme that came up again and again last week: technology is rarely the constraint. The underlying data infrastructure is.

Confidentiality is a harder line than it was six months ago

This came up more specifically at last week’s meeting. Sub docs and other sensitive materials can’t just get pushed into a general-purpose LLM. That’s not a compliance nicety; it’s a real operating constraint that shapes which tools a firm can actually use and how. It’s consistent with what Deloitte found in its 2025 GenAI in M&A Survey of 1,000 corporate and PE dealmakers: data security was the single most-cited barrier to GenAI adoption, ahead of every other concern. For private capital firms, where sub docs, LPA terms, and side letters are about as sensitive as documents get, that distinction isn’t theoretical.

At the same time, LPs want more from their GPs, not less. More data and more transparency. So GPs are stuck trying to do both at once: securely share more, but never make it feel like a bot did it. One comment from the room stuck with me: “Nobody wants their IR comms to read like ‘a bot.'” That’s the same reputational risk I wrote about in my last post, except now LPs are actually asking the question, not just wondering about it.

LPs are starting to ask GPs directly how AI is used in the firm

It’s no longer hypothetical that an LP might wonder about a GP’s AI adoption. LPs are now asking. The data backs this up: Private Equity International’s LP Perspectives 2026 Study found that nearly half of LPs surveyed are closely monitoring how their GPs adopt AI in investment and operational processes, and almost as many report mixed feelings about it, largely driven by risk concerns. The ILPA DDQ framework, which the majority of institutional LPs now use as a baseline for manager evaluation, continues to expand with each cycle, and it would be reasonable to expect AI governance to become a standard section, rather than an ad hoc follow-up question.

If your firm doesn’t yet have a clean, honest answer to “How do you use AI, and what guardrails are in place?” it’s time to build one. Not because someone will send it back with a red pen if you get it wrong, but because you should probably be able to answer that question clearly, regardless of who’s asking.

The fee conversation nobody wants to have, but needs to

If firms are getting more efficient because of AI, will LPs start asking why fees haven’t come down? It was framed as hypothetical, but it’s not disconnected from what’s actually happening in the market. Bain’s 2026 GP Outlook already points to real downward pressure on headline management fees, particularly at scale. A recent bfinance poll found that a large share of LPs report fee reductions for like-for-like private market strategies, with softer fundraising and disappointing distributions shifting real pricing power toward investors.

AI efficiency gains and fee pressure are two separate trends right now. But it’s not hard to see an LP putting them next to each other, and GPs should probably have a point of view before that question shows up in a meeting. The best response is to show where the efficiency actually goes: faster answers, better reporting, more time for the relationship itself. That’s a different story than “we cut costs and kept the fee the same.”

MCP servers came up, unprompted

Someone in the room made an offhand comment: if you already know what an MCP server is, you’re ahead of the curve. It’s a small signal, but it’s consistent with where the infrastructure conversation is heading. Most firms are still thinking about AI at the level of individual tools and prompts. Fewer are thinking about the underlying protocols that let those tools connect to a firm’s systems and data securely. That gap between “using an AI tool” and “having AI infrastructure” is expected to keep widening.

Where this leaves us

The first meeting focused on where and how IR teams should utilize AI. The second made it clear the conversation has moved past that. LPs are asking direct questions, fee dynamics are shifting underneath the AI conversation whether anyone planned for that or not, and the confidentiality line between what can and can’t touch a general-purpose LLM is getting firmer, not softer.

The through-line from both meetings hasn’t changed, though: the firms that feel most at ease are the ones with real infrastructure and clear answers, not the ones with the most impressive prompt. If anything, last week’s session raised the stakes on that point. An LP asking how you use AI in a DDQ isn’t looking for enthusiasm. They’re looking for governance.

Altvia Connects Private Markets Data to the AI Tools Firms Already Use

Posted on May 14, 2026 by - AI in Private Equity, CRM & Data Management, Data Strategy & Analytics, Deal & Pipeline Management, IR Technology, Private Equity Technology, Tech Stack Optimization

FOR IMMEDIATE RELEASE

Altvia Connects Private Markets Data to the AI Tools Firms Already Use

Altvia Integration Platform Adds MCP Support Across Fundraising, Investor Relations, and Deal Sourcing Workflows

DENVER, Colorado, May, 14, 2026–Altvia, the engagement platform for alternative investment firms, today announced MCP support in the Altvia Integration Platform, connecting private markets data to the AI tools GP teams already use.

MCP is the open standard introduced by Anthropic in November 2024 and subsequently adopted by OpenAI, Google DeepMind, and Microsoft. It enables firms to use whichever AI tool their team already trusts and have it work directly against their live Altvia data. Support is now native across Claude, ChatGPT, Microsoft Copilot, Gemini, and other major AI platforms.

“The private markets are rapidly evolving their business models to successfully navigate the alternatives industry transformation,” said Ryan Keough, CEO, Altvia. “Our support for MCP gives teams governed access to their capital raise, investor relations, and deal sourcing data to surface trends, identify opportunities, and make faster, better-informed decisions without leaving their preferred AI tool. This launch is the first of several AI announcements we’ll be making this year as we continue to deepen the intelligence layer Altvia provides for alternative investment firms.”

Designed for Private Markets Workflows

This new addition to the Altvia Integration Platform makes the platform’s core workflows available as standardized MCP capabilities across three domains central to alternative investment operations:

Fund and Portfolio Data: Retrieve fund-level and portfolio-level information in the context of broader AI workflows.

Fundraising and Investor Relations: Query LP contact history, track commitment activity, surface relationship context, and prepare for investor meetings without leaving your  preferred AI tool.

Deal Sourcing: Access pipeline data, deal stage history, and sourcing activity through natural language, enabling faster origination research and deal review.

About Altvia

Altvia is the engagement platform for alternative investment firms. Founded in 2006, Altvia provides General Partner teams with workflow solutions for fundraising, investor relations, deal sourcing, and reporting. Trusted by hundreds of private equity, venture capital, private credit, and real assets firms, Altvia helps teams deepen relationships, move faster, and operate with clarity across the fund lifecycle. Learn more at visit altvia.com.

Media Contact
Annie Eissler
CMO
annie@altvia.com