Evidence-backed data visualization and sharing workspace

For

Data and analytics teams in mid-sized companies who build and share dashboards

Solves

Teams rent several separate tools to prepare data, build charts, and share results, so work and access rules are scattered across subscriptions.

Delivers

Approved interactive visualization linked to its evidence

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 tool sprawl while keeping one traceable path from raw data to a shared, reviewed visualization.

1. Connect to external databases, warehouses and files.
2. Clean and restructure data for analysis.
3. Write and edit queries in an interactive editor.
4. Accept natural language questions and translate them into queries.
5. Convert JSON, YAML, CSV and XML into diagrams.
6. Generate charts and diagrams from prepared data.
7. Edit, navigate and debug visual structures in real time.
8. Search and filter nodes within diagrams.
9. Track project changes over time.
10. Set permissions for data and projects.
11. Notify team members about updates.
12. Reuse interface components and knowledge resources.
13. Show traceable logic trails behind each insight.
14. List datasets for exploration or exchange.
15. Convert and edit data from the browser.
16. Save and share diagrams in cloud storage.
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 approved interactive visualization linked to its evidence with source references and unresolved questions.

Everything these tools do, in one app

• Data visualization Creates charts, diagrams, or other visual representations from data.Found in Hex, Rose.ai, ToDiagram
• Data transformation Cleans, restructures, or prepares data for analysis.Found in Hex, Rose.ai, ToDiagram
• Collaboration and sharing Lets teams work together on data projects and share results.Found in Hex, Rose.ai, ToDiagram
• Data source connectivity Connects to external databases, warehouses, or files to access data.Found in Hex, Rose.ai
• AI-powered assistance Uses AI to help with queries, data filtering, restructuring, or translation.Found in Hex, Rose.ai, ToDiagram
• Natural language queries Allows users to search and analyze data using conversational language.Found in Rose.ai
• Interactive query editor Provides an environment for writing and editing data queries.Found in Hex
• Multi-format support Converts data from formats like JSON, YAML, CSV, and XML into diagrams.Found in ToDiagram
• Interactive diagrams Enables real-time editing, navigation, and debugging of visual data structures.Found in ToDiagram
• Version control Tracks changes to data projects over time.Found in Hex
• Permission settings Controls access to data and projects for secure sharing.Found in Hex, Rose.ai
• Real-time notifications Alerts team members about updates or changes.Found in Hex
• UI components library Offers reusable interface elements and knowledge resources.Found in Hex
• Logic trees Provides traceable logic trails behind data insights.Found in Rose.ai
• Data marketplace Allows users to explore, purchase, or sell datasets.Found in Rose.ai
• Chrome extension Integrates with the browser for quick data conversion and editing.Found in ToDiagram
• Cloud storage Saves and shares diagrams online for easy access.Found in ToDiagram
• Search and filter Locates and highlights specific nodes within diagrams.Found in ToDiagram

What goes in, what comes out

The workflow

1. In
   Start with

   Connected data sources, query logic, transformation steps and team permissions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected data sources

4. 3

   Query logic

5. 4

   Transformation steps and team permissions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Approved interactive visualization linked to its 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 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. One approved data model and permission set; final data interpretation and publication checks remain with the data owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data source and query setup, Editable visualization canvas, Review and sharing. Use a thumbnail gallery for projects, a large central canvas for charts and diagrams, and a right-hand panel for query logic, transformation steps, permissions and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant chart or node. Make the task-specific outcome approved interactive visualization linked to its evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, data source credentials, asset versions, team 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

Customer-owned databases, warehouses and files. Cloud storage, browser extension and publishing 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: connect to external databases, warehouses and files; clean and restructure data for analysis. 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 data and analytics teams in mid-sized companies who build and share dashboards use it to solve "teams rent several separate tools to prepare data, build charts, and share results, so work and access rules are scattered across subscriptions"?
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 shared visualizations per analyst hour and corrections after publication.
4. Measure, then decide. Track accepted shared visualizations per analyst 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 data model and permission set; final data interpretation and publication checks remain with the data owner. Implement one approved input format, a bounded representative case set and the first two task modules: connect to external databases, warehouses and files; clean and restructure data for analysis. Support the third module with operator review: write and edit queries in an interactive editor. 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 approved interactive visualization linked to its evidence. Retain the explicit scope boundary: One approved data model and permission set; final data interpretation and publication checks remain with the data owner.

What the build depends on. Data upload and preview, asynchronous query jobs, editable version history, reviewer access and tested export formats. High-fidelity analytics requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved data model and permission set; final data interpretation and publication checks remain with the data owner.

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 data package. Offer a monthly production allowance after repeat demand. Quote complex warehouse or specialist analytics work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded approved interactive visualization linked to its 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 tool sprawl while keeping one traceable path from raw data to a shared, reviewed visualization. Demonstrate a concrete approved interactive visualization linked to its evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Data and analytics teams in mid-sized companies professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample approved interactive visualization linked to its evidence from a small authorized input set, with a transparent calculation of accepted shared visualizations per analyst hour and corrections after publication and no promised savings.

The first 30 days

1. Week 1: interview five data and analytics teams in mid-sized companies who build and share dashboards and inspect a recent example of teams renting several separate tools to prepare data, build charts, and share results, so work and access rules are scattered across 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 shared visualizations per analyst 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: Accepted shared visualizations per analyst 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

Accepted shared visualizations per analyst 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 approved interactive visualization linked to its 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 data models, transformation steps and review examples, together with reliable delivery for a narrow analytics niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for data and analytics teams in mid-sized companies who build and share dashboards. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Hex, Rose.ai and ToDiagram, plus spreadsheets and internal scripts. Compare this product with the buyer's present method on accepted shared visualizations per analyst 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

Query compute, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of approved interactive visualization linked to its evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data lineage, source attribution, access permissions and usage rights. Data owners approve substantive changes and publication scope. One approved data model and permission set; final data interpretation and publication checks remain with the data owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.