Primary-source acquisition
Route questions into filings, transcripts, PDFs, market data, company news, clinical registries, funding signals, uploaded documents, and proprietary context.
AI investment intelligence
Turn market noise into investment action.
Veyris is an AI operating layer for funds that need primary-source research, model-backed judgment, recurring monitors, and audit-ready answers in one workflow.
Data plane
Instead of asking a model to improvise from memory, Veyris routes each investment question through the sources, tools, skills, and platform actions that can actually move a decision.
Operating system
Route questions into filings, transcripts, PDFs, market data, company news, clinical registries, funding signals, uploaded documents, and proprietary context.
Combine large context models with structured tools, analytical skills, source-quality diagnostics, and optional verification before the result is saved.
Convert analysis into monitors, portfolio reviews, PM workflows, exports, saved follow-ups, and Copilot-operated platform actions.
How it works
Veyris is built around repeatability: capture the mandate, gather evidence, synthesize the judgment, and keep watching the thesis after the memo is done.
Resolve tickers, private companies, portfolio assets, documents, monitor context, and user-supplied data before synthesis starts.
Combine LLM planning with tools, skills, structured extraction, and optional fact checks so the answer is grounded and auditable.
Create monitors, update portfolios, run PM reviews, export IC material, and continue the thread with retained context.
Platform surface
Investment Analysis
Example missions
Run the latest release, transcript, filing, consensus bridge, quote reaction, and source-quality audit before forming the view.
Build watchlists for longevity, diagnostics, healthcare services, climate infrastructure, or software categories with funding and evidence gaps visible.
Attach recurring reviews to assets, track metric changes, preserve source quality, and escalate decision-relevant movement.
Trust layer
Source logs, evidence coverage, model choices, token costs, run history, saved chat context, feedback telemetry, and optional fact checks stay attached to the work.
Enter the platform