Think like an owner
Start with the business, not tomorrow’s share price.
AI-assisted research for self-directed investors
Know what you are researching.
Understand the business. Inspect its financial history. Test what it may be worth. Choose your assumptions, then make your own decision.
The approach
A research workbench, not a stock picker.
It is intended to sit between basic screeners, fixed scoring systems, automated calculators, generic AI and institutional terminals, without moving into personalised advice or execution.
Value as a gap and range
Show scenarios and assumptions, not false precision.
Use the right framework
Match the questions and valuation checks to the business.
Keep the evidence visible
Trace material conclusions to dated sources.
A typical proposed workflow
From a research question to your own conclusion.
A guided, repeatable process keeps the filters, evidence, assumptions, calculations and ranking visible at each step.
Set the research frame
Choose the company universe, filters, factors, thresholds and valuation methods.
Build a candidate list
Turn a plain-English query into visible, editable filters and explain each match.
Know the business
Review the economics, financial history, risks and contradictory evidence with source links.
Test the valuation
Compare bear, base and bull scenarios, edit assumptions and inspect model limits.
See what drives the result
Inspect every iScore contribution, confidence penalty and change in a selected company list.
Decide and monitor
Record your independent conclusion and follow neutral evidence, filing and model-exception alerts.
Who it is for
Self-directed listed-company investors.
For people who want more analytical depth than a conventional retail screener, prefer to retain control and accept responsibility for the final investment decision.
The intended market segment generally has approximately US$100,000–US$1 million of investable assets, excluding real estate. That is a market-positioning based on current user feedback to date, not an input for ranking securities, personalising research or determining suitability.
Individual-company researchers
Investors who own or investigate listed businesses rather than delegate the work.
Long-term business owners
People who focus on economics, governance, valuation and evidence instead of short-term price prediction.
Assumption-led decision makers
Users who want to inspect and change the framework behind a result, then reach their own conclusion.
DataUnlisted
One platform. One transparent methodology.
Discover, understand, value, score, rank and monitor listed companies in connected research workspaces.
Current statusDevelopment stage. Not yet commercially available.
The capabilities shown are proposed, planned or under development unless expressly identified as operational. They have not necessarily been fully implemented, independently validated, launched or approved by a regulator.
Build the evidence base
Find listed companies and learn how their businesses work.
- Natural-language and multi-factor screening
- Source-linked business, management and risk analysis
- Simplified and detailed financial histories
Under development
Test more than one answer
Use valuation methods suited to the company and the available evidence.
- Bear, base and bull scenarios
- DCF, reverse DCF, multiples and sector-appropriate methods
- Editable assumptions, sensitivity and model limitations
Under development
Make the ranking explainable
Apply one visible framework across the companies you select.
- Configurable iScore factors, weights and thresholds
- Contribution, confidence and exception views
- Dated ranking snapshots, watchlists and neutral alerts
Under development
One productResearch only, no personalised-advice, managed-account, stock-pick or broker-execution upgrade.
Proposed accessFree, Standard and Pro tiers use the same research methodology; packaging, limits and pricing remain to be tested.
AI and model controls
AI should expose the work.
It is designed to accelerate evidence gathering and explanation while keeping the sources, calculations, assumptions and uncertainty reviewable.
Source before prose
Generate explanations from retrieved, dated evidence rather than invented quantitative information.
Controlled calculations
Run valuations and scores through numerical engines; use AI to explain the result, not silently replace the arithmetic.
Inspectable inputs
Show source dates, methods, assumptions, factor contributions, user changes and missing information.
Confidence and exceptions
Label evidence confidence and quarantine extreme or unreliable results as indeterminate until investigated.
Versioned research
Make reports reconstructible and distribute dated explanations when material corrections are made.
Questions
Before you explore.
Is DataUnlisted available?
Not yet. It is a development-stage platform; the capabilities shown are proposed, planned or under development unless expressly identified as operational.
Is this a stock picker or personalised adviser?
No. It is intended as a one-to-many digital financial publication, research platform and analytical tool. It is not intended to tell a particular user whether to buy, hold or sell, determine suitability or tailor research to personal financial circumstances.
What can a user control?
Users are intended to choose the listed-company universe, sectors, countries, exchanges, research factors, weights, thresholds, valuation methods and bear, base and bull assumptions. These are security-research choices, not a suitability assessment.
Does the platform use my portfolio or personal finances to rank securities?
It is not intended to use wealth, existing holdings, transaction history, risk tolerance, objectives, time horizon, cash needs or personal circumstances to rank securities or generate action-oriented outputs.
Does the platform connect to a broker or place trades?
No. The intended product perimeter excludes broker connections, order generation, transaction-based recommendations, discretionary management, automated rebalancing and trade execution.
How should I interpret a valuation or iScore?
As a transparent research output, not a promise or recommendation. Material valuations should show ranges, scenarios, assumptions, sensitivity, evidence confidence, source dates and model limitations. An iScore should show every contribution and may be indeterminate when the evidence or model fit is insufficient. User maintains their own scorecard.
Will subscription tiers change the research conclusion?
No. The proposed Free, Standard and Pro tiers may differ in access or usage limits, but they are intended to use the same research methodology and not provide different conclusions or an advice-related upgrade.
Contact us
General enquiries
Contact DataUnlisted about the product, access, technical support, privacy and legal matters, business or media enquiries, or product feedback.
Email DataUnlistedProduct feedback
Share general feedback about the research workflow, explanations, usability or accessibility.
Product and access
Ask general questions about the proposed product, availability, demonstrations or subscription access.
Technical support
Report an access problem, technical issue or error affecting the website or product.
Business and media
Contact DataUnlisted about a business, institutional, partnership or media enquiry.
Research boundary
Please do not submit personal financial information or request portfolio reviews, stock-specific guidance or personalised investment advice. Responses are limited to general matters.
Make your own decision. Know why.
Better questions. Clearer assumptions. Transparent rankings. A research record you can revisit.
Important notice. DataUnlisted is under development. Unless expressly identified as operational, features described here are prototypes, planned capabilities or designs under development. Their inclusion does not mean they have been fully implemented, independently validated, commercially launched or approved by a regulator.
It is intended to be a one-to-many digital financial publication, research platform and analytical tool, not personalised investment advice, discretionary portfolio management, brokerage, trade execution or a recommendation tailored to personal circumstances. Valuations are estimates, investing can result in loss of capital, and no score, model or AI output can guarantee returns. This compliance-by-design description is not a legal opinion or a representation that the product qualifies for an exclusion in any jurisdiction; formal legal advice is required before it is offered.