The edge institutions take for granted — for everyone below the desk.
The tools that give professional investors their edge — the Bloomberg Terminal and its peers — cost tens of thousands of dollars a year per seat and are built for trading desks. Everyone below that tier runs their book in spreadsheets and a broker portal, with research scattered across PDFs, emails, and memory. Nodetheory closes that gap: the information flow, the analysis, and the institutional discipline — for the professional who isn't an institution.
The data moat, unbundled.
The institutional data terminals — Bloomberg, S&P Capital IQ, LSEG Workspace (Refinitiv), FactSet — bundle data, analytics and a workstation into one locked contract, estimated at roughly US$12,000–32,000+ per user, per year. Nodetheory unbundles it: subscribe only to the data feeds you actually want — from the providers you choose, at retail prices — and pipe them straight into your book and your second brain through the Receive pipeline. No per-query vendor fees, no lock-in, and the AI runs on your own Claude subscription.
- Bloomberg Terminal — ~US$24–32k
- S&P Capital IQ Pro — ~US$13–30k
- LSEG Workspace (Refinitiv) — ~US$12–24k
- FactSet — ~US$12k+
Plug in affordable retail and prosumer data subscriptions — from the likes of Koyfin, Polygon.io, EOD Historical Data or Alpha Vantage, often tens of dollars a month — straight into one cockpit. Pay for what you use, own your stack, and let your own AI do the analysis.
None of these vendors publish official pricing; the figures are widely cited third-party estimates (per seat, per year), shown to illustrate the cost gap — not Nodetheory prices. Real contracts vary with content sets and negotiation.
One cockpit, eight surfaces.
Positions, an append-only trade ledger, live value, P&L and allocation by sector and conviction.
A whole-book morning triage — what it's worth, what's concentrated, and what needs you today.
A per-stock deep dive: investment case, financials, a living model, technicals, management, earnings, evidence.
Every document you own, content-addressed with full provenance and lineage — nothing lost or silently overwritten.
Where your book disagrees with the market: cost basis vs mark, stale-price flags, benchmark drift.
A live knowledge map of your book — holdings, the thesis behind each, and the documents that support them.
A co-pilot that answers in plain English from your own data, with citations and no fabrication.
Drop in a document and watch it ingest, cleanse, and amalgamate into your financials, live.
Four things it gives its users.
Information flow
Live prices, P&L, concentration and coverage alerts — the whole book triaged every morning. And you bring your own feeds: plug in the market-data providers you choose, at retail prices, straight into your book.
Analysis
Fundamental and technical depth on every name, plus a per-ticker living financial model with a spreadsheet driver grid — your assumptions, re-evaluated as they change.
AI integration
Thirteen research workflows — earnings, risk, quant, analyst, competitor, board & client packs — and a grounded co-pilot, all running on your own Claude subscription. No per-query fees, no data leaving to a vendor.
The second brain
A tamper-evident, content-addressed memory of every holding, trade, thesis and document — searchable semantically and answerable in plain language.
Grounded, private, and honest by design.
- Grounded, not guesswork. Every AI answer cites your own documents, and the model never invents a number — absent data is flagged as an assumption, not fabricated.
- Your research compounds. An append-only trade ledger and a content-addressed registry with lineage mean nothing is lost or quietly overwritten.
- Your data, your price. Subscribe to the data providers you want at retail rates and feed them straight in — a fraction of what an institutional terminal costs.
- Private by architecture. Self-hosted embeddings keep document text on the platform; per-tenant isolation is enforced by the database kernel, not by convention.
- Honest by design. Stale prices are flagged, coverage gaps are surfaced, and models degrade honestly rather than bluffing a clean answer.
Built and validated end-to-end.
Nodetheory has been built and run end-to-end — its registry, ingestion, research surfaces, living models and grounded co-pilot all proven against an 11,000-plus document corpus, with the full journey passing green. (The portfolio and figures on screen are demo data entered to exercise the system, not a real book.) It is roughly 75% complete in the CORAZ lab and not yet live on Daxo; it joins the module family as a premium research tier.
Give the professional, non-institutional investor the same quality of information, analysis and memory a funded desk takes for granted — and let their own AI do the heavy lifting.
Inside Nodetheory.
Click any screen to enlarge.
Screens show the lab build with demo data entered to exercise the system — not a real portfolio and not a performance claim. Status as of October 2026; lab-stage, pending deployment to Daxo.