Bring whatever you have. The platform will make sense of it.
Every customer has data trapped in documents and spreadsheets. Getting it onto the platform shouldn't require a data entry project.
Morph is horizontal — it serves any workflow where data is trapped in documents that should become live platform entities.
A stack of subscription documents from a previous raise. Morph materializes investors, contacts, commitments, accreditation status — everything the docs contain.
An Excel cap table from a fund admin or internal tracker. Investor-deal relationships with allocation amounts and ownership percentages.
A private placement memorandum yields deal structure, terms, key dates, and fee schedules — ready to publish.
Quarterly capital account summaries or NAV reports. Powers As-Reported mode in Portfolio for LP reporting without clean transaction data.
Exports from HubSpot, Salesforce, or a homegrown tracker. Contacts, organizations, and relationship metadata mapped onto the platform.
Wire confirmation PDFs or bank export CSVs become payment records matched to outstanding capital calls.
Connect to a third-party data room. Parse and classify documents, then route them logically into the platform — by deal, investor, document type.
Bulk exports from a prior platform or internal system. Morph maps heterogeneous data shapes into platform entities to accelerate go-live.
Same engine, different operating contexts. The mode determines how data arrives and how much human involvement the workflow needs.
A user brings data to the platform directly. Drag-and-drop, file picker, or paste. Conversational — the agent walks through classification, mapping, and review in real time.
Data arrives programmatically — a fund admin drops files on an SFTP site, a client's system pushes to an S3 bucket, a scheduled job polls a remote data room. Batching, queuing, and monitoring become critical.
Morph is an engine, not a screen. It can be invoked from the central agent or embedded directly into platform workflows — and either direction works.
The Helm / Delio AI chat interface. Good for ad-hoc requests — "help me make sense of this file" — and as a router that identifies what the user is trying to do and delegates to the right specialized workflow when the task gets specific.
Morph capabilities embedded directly in platform screens. Drag-and-drop targets, structured review tables, provenance audit trails, approval flows — all tailored to the specific workflow context. The user may never see a chat interface.
The ingestion, classification, extraction, transformation, validation, and import pipeline. Runs the same whether invoked by the agent, a localized workflow, or an automated trigger. Stateless with respect to how it was called.
Six stages. Click any stage to learn more.
The design decisions that make Morph trustworthy at scale.
Same file + same rules = same output. Every time. The AI proposes mapping rules; a pipeline executes them row by row with zero model calls.
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.
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.
Every stage transition is checkpointed. Browser crash, server restart — pick up exactly where you left off.
Validation errors route back to rule extraction. The AI amends the rules, the pipeline re-runs, and validation re-runs — no re-upload needed.
Every transformed value carries provenance back to the source row and the rule that produced it. Nothing is a black box.