What ships in production
Lab
The research surface. Plain English in, streaming charts, tables, and cited paragraphs out. The model picks the UI the answer needs.
Reports
Branded PDFs with numbered footnotes, a Sources Table, and a Key Assumptions table. Ready for IC.
Skills
Reusable workflows — DCF, LBO, comps, IC memos, earnings recaps — invoked with
/skill-name from any thread.What ships from Lab
Lab
Plain English in, streaming charts and tables and cited paragraphs out.
Reports
Branded PDFs with numbered footnotes, a Sources Table, and a Key Assumptions table.
SEC + global filings
Every major SEC form structured to section level, plus directory coverage across the global regulators. See coverage below.
Documents
PDFs, spreadsheets, decks, images — parsed and queryable in the same Lab thread.
Skills
Reusable workflows — DCF, LBO, comps, IC memos, earnings recaps — invoked with
/skill-name.Knowledge
Topics, notes, sources, and a force-directed graph that explain what cf0 has compiled and where every claim came from.
SEC and global filings coverage
cf0 ingests and structures every filing form an analyst is likely to pull, across ten regulator-backed markets — SEC EDGAR plus nine international regulators.SEC forms (US)
| Form | What it carries |
|---|---|
| 10-K | Annual report — financials, MD&A, risk factors |
| 10-Q | Quarterly report — interim financials |
| 8-K | Material events — earnings, leadership, M&A |
| S-1 | IPO prospectus |
| DEF 14A | Proxy — exec comp, board, governance |
| 13-F | Institutional holdings (quarterly) |
| N-PORT | Fund holdings |
| N-CSR | Fund certified shareholder reports |
| N-PX | Fund proxy voting records |
Global markets
| Market | Regulator |
|---|---|
| US | SEC EDGAR |
| UK | FCA / Companies House |
| Canada | SEDAR+ |
| Japan | EDINET |
| South Korea | DART |
| India | SEBI |
| Australia | ASX |
| Hong Kong | HKEX |
| China | CNINFO |
| Brazil | CVM |
Trust
- Every figure traces to the exact filing, page, and section. Click to verify.
- Reports include numbered footnotes resolving to a Sources Table, plus a Key Assumptions table before every valuation.
- Threads export as compliance-ready audit trails.
- Org-scoped data, never used to train AI models.
Common analyst questions
Where does the math actually happen?
Where does the math actually happen?
In deterministic code templates, not AI inference. DCF, WACC, multiples, sensitivities, Monte Carlo — all run as tested code paths the model frames the question for and reads the result from. Same question, same inputs, same answer. See Guardrails → the model doesn’t do math.
What if cf0 doesn't have the data it needs?
What if cf0 doesn't have the data it needs?
It says so. From a real generated report: “EV/Sales requires enterprise value; since debt/cash and shares outstanding are not provided here, EV/Sales is not computed.” cf0 surfaces gaps rather than inventing numbers. See refuse to fabricate.
Can I export an audit trail for IC or compliance?
Can I export an audit trail for IC or compliance?
Yes. Every Lab thread exports as a Markdown or PDF audit trail capturing the full conversation, every tool call and its result, every assumption gate and how it was resolved, every citation with source link, timestamps, and the model version per turn. See Citations and audit trail.
Does cf0 train on my firm's research?
Does cf0 train on my firm's research?
No. Customer threads, documents, and reports never enter any training set. AI inference runs inside cf0’s cloud perimeter — your data does not leave the platform to reach a model. See Data governance.
How is the AI orchestrated?
How is the AI orchestrated?
cf0 orchestrates a frontier large language model with specialised sub-agents for retrieval and synthesis. Every turn is fully traced — system prompt, tools called, model inputs and outputs — and the exact model version per turn is captured in the audit trail so you can verify after the fact. See Observability.
How is this different from giving ChatGPT a PDF?
How is this different from giving ChatGPT a PDF?
Four ways. (1) Numbers are computed in deterministic code templates, not generated by the model. (2) Citations trace every figure to the exact filing page. (3) Audit trails export per thread. (4) Compounding context — every shared document, integration, and memory entry sharpens the next answer across sessions. ChatGPT does none of this for institutional research.