Matching cited
Ranked 312 investors — who to approach, in what order, and why.
Rockmont leads · committed €18M on Aurora, 2024
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✓ Shortlist saved — decision logged PA-OS · Closed beta
The operating system for placement agents.
Your broker-dealer keeps you compliant. Your CRM keeps names. PA-OS runs the raise — search, standardization, matching, campaigns and reporting — as one agent you talk to, with a receipt behind every claim and nothing that sends itself.
Preview of PA-OS: agents handle sourcing, standardization, matching, outreach, and reporting — each one proposes a change with cited evidence that a person approves before anything is written.
Founder’s systems in production at
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Your CRM was built to store names. It was never built to run a raise.
A raise turns on five jobs, and every one of them is done today in a spreadsheet, an inbox, or someone’s head. PA-OS is the layer that runs them — and can show its work.
The raise today
- Your book lives in a CRM export, a partner’s memory, and four spreadsheets.
- A deck becomes a mandate by hand, and the numbers get retyped.
- Who to call next is a judgment nobody can reconstruct a month later.
- The monthly client report is assembled by hand the week it is due.
- The relationship memory walks out with whoever leaves.
With PA-OS
- One typed book, built from your own exports, with every mapping decision remembered.
- Drop the deck — the mandate comes back typed, each field quoted to its source page.
- A ranked shortlist with the argument and the evidence behind every name.
- The report generates from the same living book — versioned, under your firm’s logo.
- The memory is the firm’s, on any broker-dealer, exportable in full, any time.
One agent. The five jobs of a raise.
Not five tools bolted together — one operating memory the whole firm works from, in plain language, on top of the systems you already run.
- 01
Search — investor sourcing and enrichment
Live search agents find and enrich investors for a specific mandate, with every claim traced to a source you can open.
The system works the mandate continuously — coverage stays current instead of decaying the way a bought list does.
- 02
Standardization — from messy book to typed records
CSV exports, transcripts, and meeting notes become a typed, relational investor book through suggest-then-approve proposals.
The book feeds the CRM you already run instead of replacing it, and every mapping decision you make is remembered.
- 03
Matching — mandate to ranked shortlist
Mandates and deals crossed against your investor book produce qualified shortlists: who to approach, in what order, and why.
Hard exclusions and do-not-contact rules are deterministic; the ranking reasoning stays visible and reviewable.
- 04
Campaigns — personalized outreach, drafts only
Mailbox-native outreach drafted from the record, personalized per relationship, staged for your review.
Nothing sends without your approval — compliance-by-construction inside your broker-dealer rails, never around them.
- 05
Reporting — the work, made visible
White-label activity and progress reports where every number traces to the record behind it.
What justifies and defends the retainer — generated from the same living book the other pillars keep current.
Agentic where it helps. Deterministic where it counts.
An agent inside a regulated workflow is only useful if it cannot surprise you. Four rules are enforced in code, not in a prompt.
- The send button stays yours. Outreach is drafted into your own mailbox and waits there for you to send. PA-OS has no send path of its own.
- Every claim carries its source. A claim you cannot open the evidence for is withheld, and the run tells you how many were withheld and why.
- Your judgment is the gate. Every change the agent wants stops in For review with the old value beside the new one.
- Your book, your exit. PA-OS sits on top of the CRM and broker-dealer you already have. Everything exports in full.
“Servando is unusually strong at operating in ambiguity. He can take raw, unfiltered business context, understand the underlying need, and move into execution fast without needing a lot of process, templates, or back-and-forth clarification. What makes him especially valuable is that he combines high agency, real business understanding, and strong AI engineering capability. He quickly grasped the complexity of MVV's business and delivered not only software that addressed it, but also a true 1:1 partnership and technical guidance across every stage of development.”
Misha Vasilchikov Managing Partner · MVV Capital Partners
“Servando has a great knack for data science and for building ML systems that stand up in real-world environments. We worked on recommendation systems together.”
Ed Mattia Head of AI · Outerbounds (acq. by Anaconda)
“Servando developed from scratch a AI-matching solution that formed the core of our data processing capabilities, demonstrating strong technical skills and a structured approach that directly contributed to project success. The solutions lead to 70%+ reduction in duplicates and +1 FTE worth of manual effort eliminated.”
Kamil Kapturkiewicz Founder · Databris
You don’t roll this out alone.
PA-OS lands on a real book — a messy export, half-typed mandates, ten years of relationship memory in one partner’s head. We deploy it with you, on your data, with the founder on the call.
- We load the book with you Your CRM export, your decks, your pipeline history. The first import is a working session, not a support ticket.
- The first mandate, together First standardization, first ranked shortlist, first client report — run beside you on live deals.
- Weekly until it holds Short sessions while the book beds in, then whenever the next raise needs it.
My approach is built on high agency, deep ownership, and a bias toward tangible outcomes. Responsibility means staying with the work until there is a real result: something shipped, improved, clarified, automated, or made useful. I do not believe in vague promises or work that sounds impressive but never becomes value someone can actually feel.
The best companies are becoming living knowledge bases. Every conversation, transcript, Slack message, decision, and customer interaction becomes part of the shared context that can help the product improve. My job is to capture that context and turn it into useful artifacts, workflows, and products that compound over time.
See it run on your own book.
Thirty minutes, screen share, founder-led. Bring a CRM export and a live mandate — or watch it on ours first.
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