Cited filing and transcript research workspace

For

Equity analysts, portfolio teams and investor-relations staff who answer finance research questions from company documents

Solves

Finance research answers are scattered across filings, transcripts and news, and the source behind each number or quote is hard to verify.

Delivers

Reviewer-approved cited research answers linked to source documents

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce the time to a verifiable answer while keeping every claim traceable to its document.

1. Search company filings, transcripts and press releases.
2. Link every answer back to the original document.
3. Extract insights across several documents at once.
4. Explain how the AI reached its result.
5. Pull near real-time SEC EDGAR filings.
6. Alert users to new or relevant filings and screen updates.
7. Assess tone changes and executive-versus-analyst sentiment.
8. Highlight trending topics across the covered universe.
9. Monitor news that may affect investment decisions.
10. Track insider transactions.
11. Analyze institutional ownership data.
12. Summarize ETF and mutual fund holdings.
13. Screen stocks on selected criteria.
14. Visualize complex data sets.
15. Export to Excel and Google Sheets.
16. Expose an API for programmatic access.
17. Provide a mobile-native research view.
18. Restrict answers to verified investor-relations material.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewer-approved cited research answer with source references and unresolved questions.

Everything these tools do, in one app

• Document search Lets users search company documents such as filings, transcripts, and press releases to find relevant information.Found in AlphaResearch, PentaCue, Quartr AI chat
• Source citations Links answers or results back to the original document so users can verify quotes and data points.Found in PentaCue, Quartr AI chat
• Multi-document analysis Extracts insights from several documents at once to speed up research.Found in PentaCue
• Transparent AI explanation Explains how the AI reached its result so users can check the methodology.Found in PentaCue
• Real-time data access Provides near real-time access to the latest SEC EDGAR filings.Found in PentaCue
• Document alerts Notifies users about new or relevant filings and lets them screen filings for updates.Found in AlphaResearch
• Sentiment analysis Assesses tone changes in transcripts and detects sentiment differences between executives and analysts.Found in AlphaResearch
• Trending topics Highlights trending topics to help users understand market dynamics.Found in AlphaResearch
• News monitoring Keeps users updated on news developments that may affect investment decisions.Found in AlphaResearch
• Insider transactions Tracks insider transactions for investment research.Found in AlphaResearch
• Institutional ownership Analyzes institutional ownership data.Found in AlphaResearch
• Fund holdings insights Provides insights into ETF and mutual fund holdings.Found in AlphaResearch
• Stock screening Screens stocks based on selected criteria.Found in AlphaResearch
• Data visualization Visualizes complex data sets to make them easier to interpret.Found in AlphaResearch
• Spreadsheet add-on Integrates with Excel and Google Sheets for research workflows.Found in AlphaResearch
• API access Allows programmatic access to the platform's data and features.Found in AlphaResearch
• Mobile interface Provides a mobile-native experience so research can be done on the go.Found in Quartr AI chat
• Verified IR sources Limits answers to verified investor relations material to reduce noise from third-party content.Found in Quartr AI chat

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed filings, transcripts, press releases, ownership data and news

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed filings

4. 3

   Transcripts

5. 4

   Press releases

6. 5

   Ownership data and news

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved cited research answers linked to source documents

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Answers stay inside verified investor-relations material; final investment judgments remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Research question and source set, Editable answer preview, Review and delivery. Use a thumbnail gallery for saved questions, a large central answer canvas, and a right-hand panel for citations, constraints and comments. Let users compare answers side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant citation. Make the task-specific outcome reviewer-approved cited research answers linked to source documents visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, document 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

Company filings, transcripts, press releases, ownership data and news. Cloud document storage, spreadsheet import/export and research destinations. Start with file exchange and validate destination specifications before promising direct publishing. 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: search company filings, transcripts and press releases; link every answer back to the original document. 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 equity analysts, portfolio teams and investor-relations staff who answer finance research questions from company documents use it to solve "finance research answers are scattered across filings, transcripts and news, and the source behind each number or quote is hard to verify"?
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: Verified answers per analyst hour and corrections after review.
4. Measure, then decide. Track verified answers 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 covered universe and one document set; final investment judgments remain human. Implement one approved input format, a bounded representative case set and the first two task modules: search company filings, transcripts and press releases; link every answer back to the original document. Support the third module with operator review: extract insights across several documents at once. 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 cited research answers linked to source documents. Retain the explicit scope boundary: One covered universe and one document set; final investment judgments remain human.

What the build depends on. Document upload and preview, asynchronous search jobs, editable version history, reviewer access and tested export formats. High-fidelity research requires specialist finance QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One covered universe and one document set; final investment judgments 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 document package. Offer a monthly research allowance after repeat demand. Quote complex data, API or specialist coverage separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved cited research answer linked to source documents. 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 the time to a verifiable answer while keeping every claim traceable to its document. Demonstrate a concrete reviewer-approved cited research answer linked to source documents using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Equity analysts, portfolio teams and investor-relations staff 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 cited research answer linked to source documents from a small authorized input set, with a transparent calculation of verified answers per analyst hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five equity analysts, portfolio teams and investor-relations staff and inspect a recent example of finance research answers scattered across filings, transcripts and news, and the source behind each number or quote being hard to verify.
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 verified answers 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: Verified answers 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

Verified answers 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 cited research answers linked to source documents. 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, source mappings 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 equity analysts, portfolio teams and investor-relations staff. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

AlphaResearch, PentaCue and Quartr AI chat, plus manual filing review and generic search tools. Compare this product with the buyer's present method on verified answers 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

Document licensing, data feeds, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved cited research answers linked to source documents. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Analysts approve substantive conclusions and distribution scope. One covered universe and one document set; final investment judgments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.