Unstructured research data stewardship console

For

Research teams and data stewards managing mixed text, image and audio collections

Solves

Unstructured research material sits in disconnected stores, so teams cannot search it, label it consistently or trace how conclusions were reached.

Delivers

Reviewer-approved structured records, topics and searchable embeddings linked to their sources

Built in

about 5 weeks of creation time, MVP in 6 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 time spent locating, labelling and reconciling unstructured research material while keeping provenance and review intact.

1. Ingest text, image and audio files with permission records.
2. Convert permitted material into structured records with source links.
3. Generate embeddings for semantic search and retrieval.
4. Detect topics and themes across a collection.
5. Support collaborative labelling with reviewer roles.
6. Score sentiment in text where the study requires it.
7. Build no-code agent workflows for recurring research tasks.
8. Configure a knowledge assistant over the approved collection.
9. Store all material with access boundaries and retention rules.
10. Retrieve records quickly by meaning, label or filter.
11. Filter and segment records by criteria and metadata.
12. Visualise topics, clusters and coverage interactively.
13. Answer data questions with a guided data agent.
14. Compare the reviewed result with the recorded baseline and value assumptions.
15. Capture corrections and named-owner approval before consequential use.
16. Export a versioned reviewer-approved structured record set with source references and unresolved questions.

Everything these tools do, in one app

• Unstructured Data Handling Processes and organizes unstructured data such as text, images, and audio into structured formats for easier analysis.Found in Nomic Atlas, Relevance AI, Quivr
• Multi-modal Analysis Analyzes different data types including text, images, and audio to extract insights.Found in Nomic Atlas, Relevance AI, Quivr
• AI-Powered Embeddings Uses AI models to create vector representations that capture semantic meaning for better search and retrieval.Found in Nomic Atlas
• Topic Modeling Automatically identifies topics and themes within large datasets to aid understanding.Found in Nomic Atlas
• Data Labeling Enables users to label data collaboratively to improve dataset organization and model training.Found in Nomic Atlas
• Sentiment Analysis Evaluates the emotional tone in text data to gauge opinions and feedback.Found in Relevance AI
• AI Agent Workflows Allows building and deploying AI agents that automate tasks like research and follow-ups.Found in Relevance AI
• No-Code Customization Lets users design and customize AI agents using natural language without coding.Found in Relevance AI
• Data Storage Stores various types of unstructured data securely for later access.Found in Quivr
• Rapid Data Retrieval Enables quick access to stored data when needed.Found in Quivr
• Secure Data Control Ensures data privacy and protection through robust security measures.Found in Nomic Atlas, Relevance AI, Quivr
• Open-Source Platform Provides transparency and flexibility for users to customize the platform.Found in Quivr
• Seamless Integrations Connects with existing tools, APIs, and databases to fit into current workflows.Found in Nomic Atlas, Relevance AI, Quivr
• Personal AI Assistant Offers a configurable AI that learns from company data to act as a knowledge assistant.Found in Quivr
• Interactive Visualization Provides a visual interface to explore and collaborate on datasets.Found in Nomic Atlas
• Data Agent Assists with answering queries and guiding users on data actions.Found in Nomic Atlas
• Collaborative Features Facilitates team collaboration on data labeling and dataset management.Found in Nomic Atlas
• Filtering Options Allows users to filter data based on specific criteria for focused analysis.Found in Nomic Atlas

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted documents, recordings, images and metadata

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted documents

4. 3

   Recordings

5. 4

   Images and metadata

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved structured records, topics and searchable embeddings linked to their sources

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 collection scope and permitted media types; final interpretation and publication checks remain with the research team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Collection intake and permissions, Searchable library console, Review and labelling queue, Topic and embedding explorer, Export and audit log. Use a left-hand collection tree, a central record list with filters, and a right-hand panel for source preview, labels, topics and comments. Let users compare record versions side by side. Display draft, changes requested and approved states. Provide a read-only reviewer link with comments anchored to the relevant record. Make the task-specific outcome reviewer-approved structured records, topics and searchable embeddings linked to their sources visible beside its evidence, review state and value baseline.

Accounts and administration

Collection ownership, asset versions, reviewer comments, approval states, usage allowances, retention 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

Institutional repositories, authorized interviews and permitted research sources. Cloud object storage, file import/export and publication 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

   6 days

   One buyer segment, one recurring use case; first modules: ingest text, image and audio files with permission records; convert permitted material into structured records with source links. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 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 research teams and data stewards managing mixed text, image and audio collections use it to solve "unstructured research material sits in disconnected stores, so teams cannot search it, label it consistently or trace how conclusions were reached"?
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: Reviewed records per steward hour and corrections after publication.
4. Measure, then decide. Track reviewed records per steward hour and corrections after publication; 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 collection scope and permitted media types; final interpretation and publication checks remain with the research team. Implement one approved input format, a bounded representative case set and the first two task modules: ingest text, image and audio files with permission records; convert permitted material into structured records with source links. Support the third module with operator review: generate embeddings for semantic search and retrieval. 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 structured records, topics and searchable embeddings linked to their sources. Retain the explicit scope boundary: One approved collection scope and permitted media types; final interpretation and publication checks remain with the research team.

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 specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved collection scope and permitted media types; final interpretation and publication checks remain with the research team.

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 collection package. Offer a monthly stewardship allowance after repeat demand. Quote complex audio or image processing separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved structured record set. 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 time spent locating, labelling and reconciling unstructured research material while keeping provenance and review intact. Demonstrate a concrete reviewer-approved structured record set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams and data stewards professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant academic or practitioner events.

Lead magnet

A reviewed sample reviewer-approved structured record set from a small authorized input set, with a transparent calculation of reviewed records per steward hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five research teams and data stewards managing mixed text, image and audio collections and inspect a recent example of unstructured research material sitting in disconnected stores.
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 reviewed records per steward hour and corrections after publication, 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: Reviewed records per steward hour and corrections after publication. 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

Reviewed records per steward hour and corrections after publication; 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 structured records, topics and searchable embeddings linked to their sources. 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 schemas, labelling conventions 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 and data stewards managing mixed text, image and audio collections. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Nomic Atlas, Relevance AI and Quivr, plus manual spreadsheet tracking and shared drives. Compare this product with the buyer's present method on reviewed records per steward hour and corrections after publication. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Embedding and model calls, audio or image 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 structured records, topics and searchable embeddings linked to their sources. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, consent records, quotation accuracy and usage permissions. Research owners approve substantive interpretations and publication scope. One approved collection scope and permitted media types; final interpretation and publication checks remain with the research team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.