Evidence-backed equity research and portfolio workspace

For

Independent investors, analysts and small advisory teams researching listed equities

Solves

Stock research is scattered across several subscriptions, so evidence, scores, alerts and portfolio risk are hard to trace back to sources.

Delivers

Reviewer-approved research notes and portfolio risk views linked to source citations

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce research time while keeping every insight traceable to a cited source.

1. Stream licensed real-time prices, news and market data.
2. Run AI analysis of financial data and trends.
3. Answer plain-English questions over financial data.
4. Generate customizable research reports.
5. Track portfolios and assess associated risks.
6. Present a centralized dashboard comparing stocks and competitors.
7. Explain financial metrics and jargon in plain English.
8. Highlight frontier sectors such as AI, space and robotics.
9. Offer index-like diversified growth products.
10. Provide a mobile research experience.
11. Display market data as boards and cards.
12. Identify overlooked risks and opportunities from market signals.
13. Attach citations and links to every insight.
14. Monitor markets and alert on significant changes.
15. Route trade orders to a licensed broker with confirmation controls.
16. Assign AI scores and rankings to stocks.
17. Extract metrics from filings and transcripts.
18. Screen stocks on technical and fundamental criteria.
19. Show macroeconomic indicators such as CPI and unemployment.
20. Run regression and value-at-risk analysis.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned reviewer-approved research note with source references and unresolved questions.

Everything these tools do, in one app

• Real-time market data Provides up-to-date stock prices, news, and market information as it happens.Found in Stock Market GPT for Investment Research, Revv Invest, MyLens Stock Market and 2 more
• AI-powered stock analysis Uses artificial intelligence to analyze financial data and generate insights on stock trends and potential performance.Found in Stock Market GPT for Investment Research, Revv Invest, Danelfin and 3 more
• Natural language queries Allows users to ask questions in plain English to explore complex financial data easily.Found in Stock Market GPT for Investment Research
• Customizable research reports Enables users to generate reports tailored to their specific investment goals and preferences.Found in Stock Market GPT for Investment Research
• Portfolio tracking Helps users monitor their investment portfolios and assess associated risks.Found in Stock Market GPT for Investment Research
• Centralized dashboard Presents actionable insights and comparisons between stocks and competitors in one interface.Found in Gorilla Terminal
• Plain English explanations Translates complex financial metrics and jargon into easy-to-understand language.Found in Revv Invest
• Frontier sector focus Highlights growth opportunities in emerging industries like AI, space, and robotics.Found in Revv Invest
• Dynamic index products Offers diversified investment options in promising growth companies through index-like products.Found in Revv Invest
• Mobile app Provides a polished and intuitive mobile experience for investing on the go.Found in Revv Invest
• Visual data presentation Displays market data and insights in visual formats like boards and cards for quick understanding.Found in MyLens Stock Market
• Hidden signal identification Reveals overlooked risks and opportunities by analyzing market signals.Found in MyLens Stock Market
• Verifiable sources Backs all insights with citations and links to original sources for transparency and auditability.Found in MyLens Stock Market, Driven, Hudson Labs
• Automated monitoring Continuously watches market movements and alerts users to significant changes or opportunities.Found in Driven
• Trade execution Enables users to place trades directly, with confirmation controls and automation options.Found in Driven
• AI score and rankings Assigns scores to stocks based on potential to outperform the market and provides rankings.Found in Danelfin
• Metric extraction Extracts specific financial metrics from various documents like filings and transcripts.Found in Hudson Labs
• Advanced stock screening Filters stocks based on technical and fundamental criteria to identify investment opportunities.Found in AInvest
• Macroeconomic data Provides access to key economic indicators such as CPI, unemployment rates, and consumer sentiment.Found in Gorilla Terminal
• Risk analysis tools Offers advanced analytical tools like regression and value-at-risk analysis to manage investment risks.Found in Gorilla Terminal

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed market data, filings, transcripts, macro indicators and portfolio holdings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed market data

4. 3

   Filings

5. 4

   Transcripts

6. 5

   Macro indicators and portfolio holdings

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved research notes and portfolio risk views linked to source citations

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Market data is licensed and delayed or real-time per agreement; scores, rankings and signals are model outputs, not advice. Final investment decisions and trade authorization remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Watchlist and market overview, Research and evidence workspace, Portfolio and risk view. Use a thumbnail gallery for watchlists and saved screens, a large central analysis canvas, and a right-hand panel for citations, metrics and comments. Let users compare stocks and versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant claim. Make the task-specific outcome reviewer-approved research notes and portfolio risk views linked to source citations visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Licensed market data feeds, filings and transcript providers, macro data sources and broker order routing. Cloud storage, spreadsheet and document import/export, and portfolio accounting destinations. Start with file exchange and validate destination specifications before promising direct execution. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: stream licensed real-time prices, news and market data; run AI analysis of financial data and trends. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will independent investors, analysts and small advisory teams researching listed equities use it to solve "stock research is scattered across several subscriptions, so evidence, scores, alerts and portfolio risk are hard to trace back to sources"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted research notes per analyst hour and corrections after review.
4. Measure, then decide. Track accepted research notes per analyst hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One licensed market data feed and one filing source; final investment decisions and trade authorization remain human. Implement one approved input format, a bounded representative case set and the first two task modules: stream licensed real-time prices, news and market data; run AI analysis of financial data and trends. Support the remaining modules with operator review: answer plain-English questions, generate reports, track portfolios and assess risk. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved research notes and portfolio risk views linked to source citations. Retain the explicit scope boundary: One licensed market data feed and one filing source; final investment decisions and trade authorization remain human.

What the build depends on. Asset upload and preview, asynchronous analysis jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires licensed data and qualified review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One licensed market data feed and one filing source; final investment decisions and trade authorization remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined research package. Offer a monthly research allowance after repeat demand. Quote complex portfolio, risk or execution work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved research note. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce research time while keeping every insight traceable to a cited source. Demonstrate a concrete reviewer-approved research note using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Independent investors, analysts and small advisory teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved research note from a small authorized input set, with a transparent calculation of accepted research notes per analyst hour and corrections after review and no promised returns.

The first 30 days

1. Week 1: interview five independent investors, analysts and small advisory teams researching listed equities and inspect a recent example of stock research scattered across several subscriptions.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted research notes per analyst hour and corrections after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted research notes per analyst hour and corrections after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted research notes per analyst hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved research notes and portfolio risk views linked to source citations. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved research templates, screening rules and review examples, together with reliable delivery for a narrow finance niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for independent investors, analysts and small advisory teams researching listed equities. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Stock Market GPT for Investment Research, AllMind AI: Your Personal Stock Analyst, Revv Invest, Poe Apps, MyLens Stock Market, Driven, Danelfin, Hudson Labs, AInvest and Gorilla Terminal. Compare this product with the buyer's present method on accepted research notes per analyst hour and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Market data licensing, model calls, storage, reviewer hours, client revision rounds and licensed source documents. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved research notes and portfolio risk views linked to source citations. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, data licensing terms and audit trails. Named reviewers approve substantive claims and publication scope. One licensed market data feed and one filing source; final investment decisions and trade authorization remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.