Morph — What It Is

The Problem Morph Solves

Every customer has data trapped in documents and spreadsheets. Getting it onto the platform shouldn't require a data entry project.

Without Morph

1. Receive fund admin statement (PDF/Excel)
2. Open the platform's import template
3. Manually re-key every field into the template
4. Fix validation errors one by one
5. Upload, pray, repeat next quarter

With Morph

1. Upload the document as-is
2. Morph reads the shape & proposes a mapping
3. Review the result in a visual graph view
4. Approve and import — entities go live
5. Next time? Same-shape file = one click

How It Works

Six stages. Click any stage to learn more.

Step 1

Upload

Drop your files

Step 2

Classify

What is this?

Step 3

Extract

Pull structured data

Step 4

Transform

Map to entities

Step 5

Validate

Check & approve

Step 6

Import

Entities go live

What Morph Is — and Isn't

Morph is

A document-to-entity pipeline — files in, platform records out
Fund admin agnostic — reads the data shape, doesn't need pre-built connectors
Human-in-the-loop — you review and approve before anything lands
Self-learning — mapping profiles are saved and reused automatically
Deterministic where it counts — AI proposes rules, a pipeline executes them with no hallucination

Morph is not

A point-to-point integration (no "Citco connector" or "Apex connector")
A scheduled ETL job — it's interactive, user-driven
A replacement for platform validation — CoreOS still decides what's valid
A reporting or analytics tool — Morph gets data in, not out
Limited to fund admin data — any document the user has is fair game

Key Properties

The design decisions that make Morph trustworthy at scale.

Deterministic

Same file + same rules = same output. Every time. The AI proposes mapping rules; a Python pipeline executes them row by row with zero model calls.

Reusable Profiles

First file from a new source costs one mapping session. Every subsequent file with the same shape reuses the profile — zero AI cost, one click.

Cost Scales with Schema

AI cost is bounded by column headers + sample rows. A 50,000-row file costs roughly the same to map as a 500-row file.

Resumable

Every stage transition is checkpointed. Browser crash, server restart — pick up exactly where you left off.

Self-Correcting

Validation errors route back to rule extraction. The AI amends the rules, the pipeline re-runs, and validation re-runs — no re-upload needed.

Fully Auditable

Every transformed value carries provenance back to the source row and the rule that produced it. Nothing is a black box.

Use Cases

Morph is horizontal — it serves any workflow where someone has data trapped in documents that should become live platform entities.

Prior Fundraise Migration

Drop a stack of subscription documents from a previous raise. Morph materializes investors, contacts, commitments, accreditation status — everything the docs contain.

Investors Contacts Commitments

Cap Table Import

Upload an Excel cap table from your fund admin or internal tracker. Get investor-deal relationships with allocation amounts and ownership percentages.

Allocations Ownership % Relationships

Deal Setup from PPM

Upload a private placement memorandum. Morph extracts deal structure, terms, key dates, and fee schedules into a ready-to-publish deal.

Deal Terms Fee schedule

Fund Admin Statements

Upload quarterly capital account summaries or NAV reports. Powers the As-Reported mode in Portfolio for LP reporting without clean transaction data.

NAV Capital accounts Performance

CRM Migration

Export from HubSpot, Salesforce, or a homegrown tracker. Morph maps contacts, organizations, and relationship metadata onto the platform.

Contacts Organizations Relationships

Wire / Payment Matching

Upload wire confirmation PDFs or bank export CSVs. Morph creates payment records and matches them to outstanding capital calls.

Payments Call matching