Multi-source research dataset discovery and stewardship console

For

Research teams, data stewards and analysts working across many external dataset collections

Solves

Datasets from many sources sit in separate catalogs with inconsistent metadata, so finding, comparing and reusing them takes manual effort and repeated exports.

Delivers

A reviewed, queryable dataset library with exportable knowledge artifacts

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 dataset search and preparation time while keeping provenance and review visible.

1. Ingest licensed dataset catalogs and source metadata.
2. Normalize descriptions into uniform metadata fields.
3. Answer plain-language questions over indexed datasets.
4. Aggregate records from multiple sources into one workspace.
5. Provide a no-code query and filter interface.
6. Let users add or remove datasets in a personal workspace.
7. Extract and display key insights from selected data.
8. Generate reusable knowledge artifacts from analyses.
9. Save one-time analyses as repeatable workflows.
10. Offer domain packs for fields such as weather and climate.
11. Export to BI platforms, CSV files and cloud storage.
12. Refresh exported data on a schedule.
13. Mark manually vetted and pre-labelled datasets.
14. Accept community requests for new datasets.
15. State uncertainty instead of inventing values.
16. Discover organization locations and linked review profiles.
17. Connect existing research subscriptions into one view.
18. Expose planned API and CLI access for programmatic use.
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 reviewed, queryable dataset library with source references and unresolved questions.

Everything these tools do, in one app

• Dataset catalog access Provides a centralized collection of datasets from multiple sources that users can browse and access.Found in DataDepot, Lium AI, Datashake Hub and 1 more
• Natural language querying Lets users ask questions in plain English to find information within datasets.Found in DataDepot, Lium AI
• Automated data aggregation Collects and organizes large volumes of data from various sources without manual effort.Found in Datashake Hub
• No-code interface Enables users to work with data without needing programming skills.Found in Datashake Hub
• Customizable workspace Allows users to personalize their view by adding or removing datasets as needed.Found in DataDepot
• Insight extraction Automatically pulls out and displays key insights from data to keep users informed.Found in DataDepot
• Knowledge artifact generation Creates reusable outputs that teams can inspect and build upon for future analyses.Found in Lium AI
• Reusable workflows Turns one-time analyses into collaborative processes that can be repeated and extended.Found in Lium AI
• Domain packs Offers pre-built tools and data connections for specific fields like weather and climate.Found in Lium AI
• Export options Allows data to be sent to other tools like BI platforms, CSV files, or cloud storage.Found in Datashake Hub
• Continuous updates Keeps exported data current by regularly refreshing it.Found in Datashake Hub
• Uniform metadata Provides consistent descriptions for datasets to make discovery and comparison easier.Found in Coldpress AI
• Manually vetted datasets Ensures datasets are reviewed and pre-labelled for quality and usability.Found in Coldpress AI
• Community-driven requests Allows users to request specific datasets and see them added based on demand.Found in Coldpress AI
• Guardrails for accuracy Prevents hallucinations by prompting the system to state uncertainty when appropriate.Found in Lium AI
• Automated location discovery Finds all company locations and their associated review profiles automatically.Found in Datashake Hub
• Integration with existing subscriptions Incorporates users' current research subscriptions and datasets for a unified experience.Found in DataDepot
• Upcoming API and CLI Planned features to allow programmatic access and command-line interaction with datasets.Found in Coldpress AI

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed dataset catalogs, source metadata, usage terms and team queries

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed dataset catalogs

4. 3

   Source metadata

5. 4

   Usage terms and team queries

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A reviewed, queryable dataset library with exportable knowledge artifacts

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. Final data interpretation, licensing decisions and publication scope remain with qualified reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source and rights intake, Searchable dataset library, Query and artifact workspace, Export and refresh console. Use a filterable catalog list, a large central query and preview canvas, and a right-hand panel for metadata, provenance, usage terms and comments. Let users compare datasets and artifact versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant dataset or artifact. Make the task-specific outcome a reviewed, queryable dataset library with exportable knowledge artifacts visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, dataset versions, source rights, client comments, approval states, usage allowances, query 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

Authorized dataset catalogs, permitted research subscriptions and approved source APIs. Cloud storage, BI platforms, CSV export destinations and refresh schedulers. 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 licensed dataset catalogs and source metadata; normalize descriptions into uniform metadata fields; answer plain-language questions over indexed datasets. 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, data stewards and analysts working across many external dataset collections use it to solve "datasets from many sources sit in separate catalogs with inconsistent metadata, so finding, comparing and reusing them takes manual effort and repeated exports"?
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 dataset retrievals per steward hour and reuse of approved artifacts.
4. Measure, then decide. Track accepted dataset retrievals per steward hour and reuse of approved artifacts; 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 source format and a bounded representative dataset set; final data interpretation and licensing checks remain with qualified reviewers. Implement one approved input format, a bounded representative case set and the first three task modules: ingest licensed dataset catalogs and source metadata; normalize descriptions into uniform metadata fields; answer plain-language questions over indexed datasets. Support later modules with operator review: aggregate records from multiple sources into one workspace; extract and display key insights from selected data; generate reusable knowledge artifacts from analyses. 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 a reviewed, queryable dataset library with exportable knowledge artifacts. Retain the explicit scope boundary: One approved source format and a bounded representative dataset set; final data interpretation and licensing checks remain with qualified reviewers.

What the build depends on. Dataset upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity research use requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source format and a bounded representative dataset set; final data interpretation and licensing checks remain with qualified reviewers.

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 dataset package. Offer a monthly production allowance after repeat demand. Quote complex multi-source or specialist research integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, queryable dataset library with exportable knowledge artifacts. 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 dataset search and preparation time while keeping provenance and review visible. Demonstrate a concrete reviewed, queryable dataset library with exportable knowledge artifacts using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Research teams, data stewards and analysts working across many external dataset collections professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, queryable dataset library with exportable knowledge artifacts from a small authorized input set, with a transparent calculation of accepted dataset retrievals per steward hour and reuse of approved artifacts and no promised savings.

The first 30 days

1. Week 1: interview five research teams, data stewards and analysts working across many external dataset collections and inspect a recent example of datasets from many sources sit in separate catalogs with inconsistent metadata, so finding, comparing and reusing them takes manual effort and repeated exports.
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 dataset retrievals per steward hour and reuse of approved artifacts, 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 dataset retrievals per steward hour and reuse of approved artifacts. 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 dataset retrievals per steward hour and reuse of approved artifacts; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, queryable dataset library with exportable knowledge artifacts. 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 metadata mappings, source rights records 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, data stewards and analysts working across many external dataset collections. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

DataDepot, Lium AI, Datashake Hub and Coldpress AI, plus manual catalog spreadsheets and separate subscription portals. Compare this product with the buyer's present method on accepted dataset retrievals per steward hour and reuse of approved artifacts. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Ingestion and indexing compute, storage, reviewer hours, client revision rounds and licensed source datasets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a reviewed, queryable dataset library with exportable knowledge artifacts. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, dataset licensing, usage permissions and uncertainty statements. Named reviewers approve substantive interpretations and publication scope. One approved source format and a bounded representative dataset set; final data interpretation and licensing checks remain with qualified reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.