In this article: We’re sharing four investor relations use cases that have emerged from our MCP early access conversations, including milestone recognition, re-engagement, personalized investor communications, and location-based outreach. For each, we’ll show what information Claude can work with from InvestNext and how you might build the workflow.
Over the past several weeks, we’ve been working alongside InvestNext MCP early access customers to explore a pretty open-ended question:
What becomes possible when AI can work with the investor data already in InvestNext?
Rather than arriving with fully formed AI strategies, many of our customers are experimenting. And they’re bringing us the moments in their day-to-day work where they wish they could move faster, know more, or be more intentional.
One theme keeps coming up: How can we use this to be better at the human side of investor relations?
This doesn’t stop at answering questions faster or eliminating administrative work. Customers are looking at the information they already have about their investors and asking how AI could help them put that context to better use.
- Could we know when an important investor milestone is coming up?
- Could we notice when a longtime investor’s behavior changes?
- Could we make an update more relevant to the person receiving it?
- Could we know who to call when we’re traveling to a particular city?
Those conversations inspired what we’re calling an Investor VIP Kit: four ideas we’ve explored with customers for using Claude and InvestNext MCP to create more opportunities for thoughtful, personal investor interactions that make every investor feel like they’re your first one.
Some work best as a one-time prompt, or an instruction you give Claude in the moment. Others are better suited to skills, reusable instructions that let Claude repeat a workflow without your team rebuilding it each time.
Quick Glossary: AI Terms to Know
- MCP (Model Context Protocol): A standard that allows AI applications to securely connect to external tools and data sources. InvestNext MCP allows supported AI tools to work with information from your InvestNext account.
- Claude: Anthropic’s AI assistant and the tool used in these examples.
- Prompt: An instruction or question you give an AI assistant to complete a specific task.
- Skill: A reusable set of instructions that helps Claude perform the same type of task consistently without rebuilding the workflow each time.
1. Remember the Moments That Matter: Investor Milestone Recognition
One of the simplest ideas we’ve heard is also one of the best: help me remember the moments worth recognizing.
An investor may have been with your firm for five years. Another may have just crossed an important investment milestone. Your team wants to acknowledge those moments. The challenge is consistently spotting them before they pass.
That’s where customers saw an opportunity for MCP.
What Claude can pull from InvestNext:
Investor commitment history, initial investment dates, and investment totals.
How to explore it:
Create a skill that reviews your investor data for milestones you define.
For example, ask Claude to identify investors approaching a 1, 5, or 10-year anniversary within the next 30 days and return:
- Investor name
- Initial investment date
- Upcoming anniversary
- Years invested
The same concept can be adapted for investment milestones by asking Claude to look for investors crossing commitment thresholds that matter to your firm.
From there, your team decides what the moment deserves. Some of our favorite examples so far have been a handwritten note or a small gift. But even just a call goes a long way.
The interesting part isn’t automating the gesture. The value is that this workflow makes it harder to miss key opportunities to reinforce your relationship.
| Try this in Claude: Review our investor records in InvestNext and identify investors approaching a 1 or 5 year anniversary within the next 30 days. Return: – Investor name – Initial investment date – Upcoming anniversary date – Number of years invested Sort the list by upcoming anniversary date. |
2. Notice When a Relationship Changes: Investor Re-Engagement
Another idea that emerged from our early access conversations was almost the inverse.
What if AI could help your team notice when something doesn’t happen?
An investor who regularly participated in previous opportunities but hasn’t joined the last few raises may warrant attention. That pattern can be difficult to spot when you’re managing hundreds of relationships at once.
With InvestNext MCP, customers started thinking about how they could turn investment history into a signal for their IR teams.
What Claude can pull from InvestNext:
Investment and commitment history across previous raises.
How to explore it:
Build a skill that identifies investors whose recent participation looks different from their historical behavior.
For example, ask Claude to find investors who:
- Participated in at least three previous investments
- Have not participated in the last two raises
- Historically committed above a threshold you define
Ask it to return the investor’s name, previous investments, historical commitment amount, and most recent participation date.
That doesn’t tell your team what happened or what to do next, but it does give them a place to look.
Maybe the investor’s priorities changed. Maybe the last opportunities weren’t a fit. Maybe it’s simply been too long since someone checked in.
The context helps your team decide whether a conversation is worth having.
| Try this in Claude: Review investment and commitment history in InvestNext and identify investors whose recent participation differs from their historical behavior. Find investors who: – Participated in at least three previous investments – Have not participated in the last two raises – Historically committed at least [$X] Return: – Investor name – Previous investments – Total historical commitment amount – Average commitment amount – Most recent participation date – Last two raises they did not participate in Do not infer why their participation changed. Just surface the relevant history for our team to review. |
3. Make an Update Feel More Personal: Customized Project Updates
Personalization came up in another form during our customer conversations: Can AI help us communicate with an investor based on what they actually own?
Two investors receiving the same fund update may have very different positions and very different reasons for paying attention.
If Claude can access the investment context already stored in InvestNext, customers saw an opportunity to use that information as a starting point for more relevant communication.
What Claude can pull from InvestNext:
An individual investor’s holdings, investment history, positions, and relevant deal-level information available in InvestNext.
How to explore it:
This is a good place to start with a prompt rather than a skill.
Give Claude the investor’s name and ask it to review their current holdings in InvestNext. Then provide the broader information you need to communicate and ask Claude to draft an update that incorporates context relevant to that investor’s position.
Review the output, verify the details, and decide what information is actually useful before sending anything.
If your team finds a format that works, you can eventually turn those instructions into a skill and reuse the structure across future updates.
The draft gets easier to create. The judgment about what an investor actually needs to hear stays with your team.
| Try this in Claude: Review [Investor Name]’s current holdings, investment history, and relevant position information in InvestNext. Using the update below, draft a personalized version that highlights the information most relevant to this investor’s actual holdings. [PASTE UPDATE] Keep the tone professional and personal. Do not add facts that are not included in InvestNext or the source update. Before drafting, summarize the investor-specific information you are using so I can verify it. |
4. Turn Travel Into Face Time: Location-Based Investor Outreach
One of our favorite early examples started with an incredibly practical question:
We’re going to Dallas. Which investors are nearby?
It’s a small example, but it captures a lot of what has been interesting about watching customers explore MCP.
The information already exists. The opportunity already exists. What’s changing is how easily your team can connect the two.
What Claude can pull from InvestNext:
Investor addresses and location information on file.
How to explore it:
For an occasional trip, start with a prompt.
Ask Claude to identify investors within a defined radius of the city you’re visiting. For example:
“Which of our investors are located within 50 miles of Dallas? Include their name, city, current investments, and total investment history with our firm.”
From there, your team can review the list and decide who it makes sense to contact.
If regional events, investor dinners, or market visits are a regular part of your strategy, the same instructions could become a reusable skill.
The result is simple: when your team is somewhere your investors are, you have a better chance of knowing it.
| Try this in Claude: Review investor location information in InvestNext and identify investors located within 50 miles of [City, State]. Return: – Investor name – City and state – Current investments – Total historical commitments – Most recent investment date Sort the list by total historical commitments from highest to lowest. Do not draft outreach yet. First give me the list so our team can decide who makes sense to contact. Once you’ve chosen the group: Using the investors I’ve selected from this list, draft a short invitation letting them know our team will be in [City] on [date] and would like to meet while we’re in town. Keep it warm and personal. Avoid making the message feel like a mass event invitation. |
What We’re Learning So Far
One of the biggest lessons from early access has been that there’s no single “right” place to start with AI.
Customers aren’t all arriving at the same use case. They’re looking at their own teams, their own investor relationships, and their own workflows and finding different places where better access to context could help.
That’s exactly how we think this should work.
Start somewhere. Stay curious. Move with purpose.
For some firms, that might mean automating one daily task of bulky, administrative work. For others, it might mean spotting an investor relationship that deserves attention before someone on the team happens to notice it.
The examples above point to something we’re particularly excited about: AI can create more opportunities for people to be good at the parts of investor relations that are deeply human.
Claude can help surface the context.
Your team still decides what it means, when it matters, and how to show up.
Keep Your Team in the Loop
These workflows are designed to give your team better access to context, not remove people from the process. Always review AI-generated outputs before acting on them, verify investor and investment information, and use your team’s judgment when deciding when and how to reach out.
