Research evidence analysis and reporting workspace

For

Research teams analyzing calls, videos and datasets to produce evidence-backed findings

Solves

Research data sits in separate tools, so transcription, tagging, pattern analysis and reporting are manual and slow.

Delivers

Reviewer-approved findings linked to source evidence

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

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

Reduce analysis and reporting effort while keeping findings traceable to source.

1. Consolidate calls, videos and datasets in one searchable repository.
2. Transcribe call and video audio into text.
3. Highlight and tag important passages automatically.
4. Group data by theme, sentiment or criteria.
5. Organize findings with digital sticky notes.
6. Apply custom AI templates for research stages.
7. Automate workflow steps with an AI assistant.
8. Record calls for later analysis.
9. Add time-stamped notes during recordings.
10. Create clips of user behavior for focused review.
11. Support team collaboration and embedded sharing.
12. Apply data security controls to research data.
13. Recognize trends and patterns across datasets.
14. Build customizable dashboards for visualization.
15. Connect to approved data sources and software tools.
16. Process incoming data in real time for current insights.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewer-approved findings report with source references and unresolved questions.

Everything these tools do, in one app

• Data Repository Consolidates and organizes all research data in one searchable place.Found in Notably, Looppanel
• AI-Powered Analysis Uses artificial intelligence to analyze data and extract insights.Found in Notably, Looppanel, Profundo
• Transcription Converts audio from calls and videos into text.Found in Notably, Looppanel
• Automatic Highlighting and Tagging Automatically highlights and tags important parts of research data.Found in Notably
• Cluster Analysis Groups data by theme, sentiment, or other criteria to identify patterns.Found in Notably
• Digital Sticky Notes Allows intuitive organization of ideas and findings using digital notes.Found in Notably
• Custom AI Templates Provides a library of templates for various research stages.Found in Notably
• Workflow Automation Automates tasks across the research workflow with an AI assistant.Found in Notably
• Call Recording Records calls for later analysis.Found in Looppanel
• Time-Stamped Notes Highlights key moments during recordings with time-stamped annotations.Found in Looppanel
• Clip Creation Creates clips of user behavior for focused analysis and sharing.Found in Looppanel
• Collaboration Enables teams to work together, share insights, and embed content in other applications.Found in Looppanel, Profundo
• Data Security Ensures research data is kept secure.Found in Looppanel
• Automated Pattern Recognition Automatically identifies trends and patterns in data.Found in Profundo
• Customizable Dashboards Provides personalized data visualization through dashboards.Found in Profundo
• Integration with Data Sources Connects with popular data sources and software tools.Found in Profundo
• Real-Time Data Processing Processes data in real time to provide up-to-date insights.Found in Profundo

What goes in, what comes out

The workflow

1. In
   Start with

   Recorded calls, video files, transcripts and structured datasets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect recorded calls

4. 3

   Video files

5. 4

   Transcripts and structured datasets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved findings linked to source evidence

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. One approved data-handling policy and consent scope; final interpretation and reporting decisions remain with qualified researchers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data repository and import, Editable analysis workspace, Findings report and sharing. Use a thumbnail gallery for studies, a large central analysis canvas, and a right-hand panel for sources, tags and comments. Let users compare coded excerpts side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant excerpt. Make the task-specific outcome reviewer-approved findings linked to source evidence 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

Participant-owned recordings, authorized transcripts and permitted research datasets. Cloud storage, call and video platforms, survey tools and reporting 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

   5 days

   One buyer segment, one recurring use case; first modules: consolidate calls, videos and datasets in one searchable repository; transcribe call and video audio into text. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 research teams analyzing calls, videos and datasets to produce evidence-backed findings use it to solve "research data sits in separate tools, so transcription, tagging, pattern analysis and reporting are manual and slow"?
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 findings per analyst hour and corrections after report approval.
4. Measure, then decide. Track accepted findings per analyst hour and corrections after report approval; 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 approved data-handling policy and consent scope; final interpretation and reporting decisions remain with qualified researchers. Implement one approved input format, a bounded representative case set and the first two task modules: consolidate calls, videos and datasets in one searchable repository; transcribe call and video audio into text. Support the third module with operator review: highlight and tag important passages automatically. 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 findings linked to source evidence. Retain the explicit scope boundary: One approved data-handling policy and consent scope; final interpretation and reporting decisions remain with qualified researchers.

What the build depends on. Asset upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity analysis requires qualified research review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved data-handling policy and consent scope; final interpretation and reporting decisions remain with qualified researchers.

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 study package. Offer a monthly production allowance after repeat demand. Quote complex video, real-time or specialist analysis separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved findings linked to source evidence. 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 analysis and reporting effort while keeping findings traceable to source. Demonstrate a concrete reviewer-approved findings linked to source evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams analyzing calls, videos and datasets 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 findings linked to source evidence from a small authorized input set, with a transparent calculation of accepted findings per analyst hour and corrections after report approval and no promised savings.

The first 30 days

1. Week 1: interview five research teams analyzing calls, videos and datasets and inspect a recent example of research data sitting in separate tools, so transcription, tagging, pattern analysis and reporting are manual and slow.
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 findings per analyst hour and corrections after report approval, 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 findings per analyst hour and corrections after report approval. 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 findings per analyst hour and corrections after report approval; 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 findings linked to source evidence. 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 coding schemes, analysis templates and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for research teams analyzing calls, videos and datasets to produce evidence-backed findings. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Notably, Looppanel, Profundo, spreadsheets and manual review. Compare this product with the buyer's present method on accepted findings per analyst hour and corrections after report approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Transcription minutes, video or data processing, 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 findings linked to source evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve participant consent, source attribution, quotation accuracy and usage permissions. Researchers approve substantive interpretations and publication scope. One approved data-handling policy and consent scope; final interpretation and reporting decisions remain with qualified researchers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.