Build the first useful assistant for Blackford-style IR work.
A live page for sitting down with a Blackford Capital contact: learn his email voice, create one reusable drafting skill, then pressure-test Juniper Square / Headless GPX use cases for investor relations and fund operations.
The two-session agenda
Do not start with “AI can do anything.” Start with one personal win, then one Blackford-specific systems conversation.
Connect Outlook / email context
Use sent emails only at first. Learn voice, tone, greeting style, level of detail, and common follow-up patterns.
Create the voice profile
Generate a small voice document: how he writes, what he never says, how formal he is with LPs, colleagues, founders, and internal updates.
Run one email drafting skill live
Pick one real thread. Draft only. Human approves. Capture the correction so the skill gets better.
Shift to Juniper Square
Ask what data lives in Juniper: LP records, fundraising pipeline, interactions, documents, reports, investor-center activity.
Pick one safe Juniper skill
Start read-only: tearsheet, LP briefing, fundraising pipeline summary, investor follow-up draft, or data hygiene report.
He leaves with one voice profile, one email skill he can reuse in Claude/ChatGPT, and a short list of Juniper Square use cases worth testing next.
Build the Blackford email voice skill
This copies the Brandon/Jim pattern: connect email, learn voice from sent mail, draft replies only, then save the prompt as a reusable skill.
LP follow-ups, meeting recaps, diligence asks, internal reminders, and scheduling replies.
Everything is a draft. No commitments, numbers, terms, or forward-looking claims without human review.
When he changes a draft, save the rule: too formal, too long, wrong greeting, missing context, wrong level of certainty.
Different tone for LPs, internal team, founders/sellers, advisors, and portfolio-company leaders.
You are helping create my private email voice profile for investor-relations and private-equity work. Your job is to learn how I actually write from examples, then create a reusable voice guide. Inputs: - 10-20 recent sent emails or drafts - The audience for each email if known: LP/investor, internal team, founder/seller, advisor, portfolio company, other Steps: 1. Separate real writing from one-word replies, forwards, newsletters, auto-responses, and throwaway messages. 2. Identify patterns by audience: greeting, sentence length, formality, level of detail, sign-off, common phrases, and how direct I am. 3. Extract what makes my writing sound like me. 4. Identify things I do not want: generic AI language, over-polish, fake warmth, unsupported claims, or commitments I did not make. 5. Ask me what feels wrong before saving the profile. Output: - My voice in one paragraph - Audience-specific rules - Do / do not list - Example phrases to reuse - Phrases to avoid - Rules for drafting follow-up emails Important: - Draft only. Never send. - Do not invent facts, dates, numbers, commitments, or fund/company claims. - If a fact is missing, mark it [VERIFY]. - Keep sensitive investor and fund information private.
The real systems wedge
Juniper Square describes Headless GPX as an AI operating layer for private markets, available to MCP-compatible clients like Claude, Copilot, ChatGPT, Gemini, and custom tools. Treat this as the structured-data layer, not just another CRM.
If Outlook teaches the assistant how he communicates, Juniper Square teaches it who the investors are, where each relationship stands, what documents exist, and what the next fund/LP action should be.
Headless GPX appears to be an official Juniper Square MCP/API product, but not a public self-serve package with open install docs. The right next step is to ask Juniper to enable Headless GPX or developer API access for the account, then test read-only workflows first.
Summarize one LP: relationship history, commitments, recent activity, docs viewed, last touch, open questions, next best action.
Show stages, stale prospects, next actions, missing materials, and likely blockers before the next IR meeting.
Draft a note grounded in LP profile, prior interactions, fund materials, and approved language.
Flag missing tags, stale contact data, inconsistent notes, missing follow-ups, or investor records without owner/action.
Confirm current reports/notices are named, categorized, and available to the right audience.
Draft internal explanations for capital calls, distributions, and reporting events. Human/legal review required.
Start read-only. Do not let a general chat agent write to investor records, send LP emails, change fund documents, or expose confidential data until permissions, audit logs, and approval gates are explicit.
Where AI likely fits Blackford
Blackford is a Grand Rapids-based lower-middle-market private equity firm focused on majority-control investments in founder/family-owned companies, especially manufacturing, distribution, and services. Public materials point toward practical needs in LP communication, deal flow, portfolio updates, and operational reporting.
Draft quarterly updates, KPI commentary, portfolio narratives, and variance explanations from source docs.
Answer LP questions from approved fund docs, past updates, CRM notes, and investor-center materials.
Summarize CIMs, teasers, and broker emails against Blackford criteria: sector, revenue, EBITDA, ownership, cash flow, and add-on fit.
Turn monthly financials/operating notes into management-ready commentary: drivers, risks, margins, customers, working capital.
Rank targets by geography, service line, strategic rationale, and likely fit with platform companies.
Create pre-read summaries, agenda drafts, decision logs, follow-up lists, and cross-portfolio pattern notes.
- Blackford Capital firm overview: blackfordcapital.com/firm
- Investment criteria / investor page: blackfordcapital.com/investors
- Portfolio: blackfordcapital.com/portfolio
- Nhat Vu profile: blackfordcapital.com/people/nhat-vu
Private markets are moving from chat to workflow
The best live demos are not “ask AI anything.” They are narrow analyst/IR workflows with source material, approval gates, and clear output.
AI is being used to screen CIMs, banker materials, company data, and predefined criteria for faster go/no-go triage.
Summarize files, flag missing disclosures, detect inconsistencies, and draft diligence questions from sanitized materials.
Draft first-pass LP updates, DDQ responses, bespoke investor responses, and follow-up emails from approved source docs.
Prepare LP/founder meeting briefs, identify stale relationships, capture next actions, and improve pipeline discipline.
Turn KPI sheets and operating notes into trend summaries, board questions, and follow-up actions.
Claude financial-services agents, Excel/PowerPoint/Word add-ins, and CRM agents are converging around the exact PE workflow layer.
Do not use real LP names, commitments, side letters, fund terms, live data rooms, or automated investor email sending in a first meeting. Use mock records or approved public/sample material for demos.
- KPMG PE origination advantage: KPMG PDF
- Blueflame dealmaking agent: Blueflame Amp
- Blueflame investor relations workflows: Blueflame IR
- Anthropic finance agents: Anthropic finance agents
- FTI private equity AI radar: FTI 2026 PE AI Radar
Run these live
These are designed for the room: easy to paste, easy to discuss, and useful even before a full integration exists.
1. Email drafting skill
You are my private email drafting assistant for private-equity / investor-relations work. Use my voice profile and the full email thread. Classify the email: - LP / investor - internal team - founder / seller - advisor / banker / broker - portfolio company - scheduling / admin - other Then produce: 1. One recommended draft response in my voice. 2. A shorter version if appropriate. 3. Any facts I need to verify before sending. 4. Any follow-up task that should be tracked. Rules: - Draft only. Never send. - Do not invent facts, dates, fund terms, performance data, commitments, or promises. - Use [VERIFY] for missing facts. - Keep confidential data out of the response unless it was already in the thread and is appropriate for the recipient. - If the email is sensitive, recommend human review instead of drafting.
2. Juniper Square use-case discovery
You are helping me identify safe, high-value AI workflows around Juniper Square / Headless GPX. First, interview me before recommending anything. Ask about: - What data we store in Juniper Square - Which workflows are most painful: LP reporting, fundraising pipeline, investor follow-up, document management, investor onboarding, capital activity, CRM hygiene - Which actions are read-only vs. write/send/change - What permission boundaries and approval steps are required - What a successful first 30-minute demo would look like Then recommend: 1. Three read-only workflows we can safely test first. 2. One email-drafting workflow that stays human-approved. 3. One CRM hygiene workflow. 4. One workflow we should avoid until permissions/audit logs are clear. 5. The exact first prompt to run against sample or demo data.
3. Investor tearsheet / meeting prep
Create an investor tearsheet for this LP/prospect. Use only the source material I provide or connected, permissioned records. Output: - Relationship snapshot - Last touch and next best action - Current fundraising / fund status - Documents or reports they may care about - Open questions or concerns - Personalized talking points - Suggested follow-up email draft - Items to verify before sending Rules: - Separate verified facts from inference. - Do not invent fund performance, terms, or commitments. - Mark missing data as [VERIFY]. - Draft only. Human approves all outreach.
4. Deal intake triage
Review this inbound deal material against Blackford-style criteria. Use the source document only. Output: - Company summary - Sector - Revenue / EBITDA if stated - Ownership / founder-family fit if stated - Why it may fit manufacturing, distribution, services, or add-on strategy - Red flags - Missing diligence questions - Recommended next step: pass, ask for more info, or discuss internally Rules: - Do not infer numbers that are not stated. - Separate source facts from analysis. - Keep the summary concise enough to forward internally after review.
“The first win is not replacing judgment. The first win is making every investor email, meeting prep, and CRM note start from better context.”
Simple workspace shape
This mirrors the Brandon/Jim pattern: one folder, a reference layer, and reusable skills.