Plain-language data insight and dashboard workspace

For

Marketing and operations teams that need answers from their own databases without writing SQL

Solves

Business teams depend on analysts or rented BI subscriptions to turn stored data into charts, dashboards and answers.

Delivers

Reviewed dashboards, charts and plain-language answers linked to source queries

Built in

about 5 weeks of creation time, MVP in 5 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 dependence on rented BI tools and analyst queues while keeping data in the client's own environment.

1. Ask questions in everyday language and return SQL-backed answers.
2. Generate charts and tables from returned results.
3. Build customizable dashboards for tracked metrics.
4. Connect SQL, NoSQL, API and spreadsheet sources.
5. Refresh dashboards against live data.
6. Automate recurring data handling and workflow steps.
7. Share dashboards and analyses with named teammates.
8. Generate AI-written interpretations of result sets.
9. Provide an embedded chat panel inside each dashboard.
10. Apply data siloing, access rules and compliance settings.
11. Support self-hosted deployment on client infrastructure.
12. Automate repetitive tasks with a drag-and-drop builder.
13. Version dashboards and workflows with change history.
14. Assign role-based permissions per user and dataset.
15. Schedule automated dashboard and report updates.
16. Keep activity logs of queries, edits and refreshes.
17. Save conversations and queries for later reference.
18. Apply industry-specific analyst context packs.
19. Allow source-code access for customization.
20. Compare the reviewed result with the recorded baseline and value assumptions.
21. Capture corrections and named-owner approval before consequential use.
22. Export a versioned reviewed dashboard set with source references and unresolved questions.

Everything these tools do, in one app

• Natural Language Querying Ask questions in everyday language to get data insights without writing SQL or code.Found in Basedash, Athenic AI, Supaboard AI and 6 more
• Dashboard Creation Create customizable dashboards to track key metrics and visualize data.Found in Basedash, Athenic AI, Supaboard AI and 5 more
• Multi-Source Data Integration Connect to multiple data sources like SQL, NoSQL, APIs, and spreadsheets for unified analysis.Found in Basedash, Athenic AI, Supaboard AI and 6 more
• Real-Time Analytics Analyze live data to get up-to-date insights and performance metrics.Found in Athenic AI, Genie by Databox
• Automated Data Processing Automate data handling and workflow management to improve efficiency.Found in Athenic AI, Conduit.app
• Collaboration Tools Share dashboards, reports, and analyses to support team projects and communication.Found in Athenic AI, Typper BI, NBI.AI and 1 more
• AI-Powered Analysis Use AI to interpret data and generate actionable insights with minimal user input.Found in NBI.AI, MindsDB Anton
• Embedded AI Chat Interact with data through an AI chat interface within dashboards for deeper exploration.Found in Supaboard AI
• Data Privacy and Security Ensure sensitive data is protected with measures like siloing and compliance.Found in Supaboard AI, Basedash Self-Hosted, Onvo AI
• Self-Hosting Options Deploy the platform on your own infrastructure for maximum control and security.Found in Basedash Self-Hosted
• Workflow Automation Automate repetitive tasks and integrate apps with a drag-and-drop builder.Found in Conduit.app
• Version Control Manage versions of dashboards and workflows with features like Git-like control.Found in Onvo AI, Basedash Self-Hosted
• Role-Based Access Control Assign permissions to users to control access to data and dashboards.Found in Onvo AI, MindsDB Anton
• Scheduled Automations Schedule automated tasks and updates for dashboards and reports.Found in Onvo AI
• Activity Logs Monitor workflow performance and track activities with detailed logs.Found in Conduit.app
• Saving Conversations Save queries and results for easy reference and ongoing analysis.Found in Typper BI
• Industry-Specific AI Analysts Leverage AI analysts trained for specific industries to deliver context-aware insights.Found in Supaboard AI
• Open Source Availability Access and modify the source code for customization and transparency.Found in MindsDB Anton

What goes in, what comes out

The workflow

1. In
   Start with

   Connected data sources, metric definitions, access rules and reporting questions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected data sources

4. 3

   Metric definitions

5. 4

   Access rules and reporting questions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed dashboards, charts and plain-language answers linked to source queries

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 fixed data source set and metric dictionary; final metric definitions and access decisions 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 connections, Ask and explore, Dashboard builder, Review and publish. Use a project list for workspaces, a central question box with result table and chart, and a right-hand panel for sources, metric definitions and comments. Let users compare saved queries side by side. Display draft, changes requested and approved states. Provide a shared dashboard link with comments anchored to the relevant chart. Make the task-specific outcome reviewed dashboards, charts and plain-language answers linked to source queries visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source credentials, dataset versions, shared links, approval states, usage allowances, query limits, export history and a rights record for supplied data. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Client-owned databases, spreadsheets, APIs and internal metric dictionaries. Cloud data warehouses, spreadsheet import/export 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: ask questions in everyday language and return SQL-backed answers; generate charts and tables from returned results. 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 marketing and operations teams that need answers from their own databases without writing SQL use it to solve "business teams depend on analysts or rented BI subscriptions to turn stored data into charts, dashboards and answers"?
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 answers per analyst hour and corrections after dashboard approval.
4. Measure, then decide. Track accepted answers per analyst hour and corrections after dashboard 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 fixed data source set and metric dictionary; final metric definitions and access decisions remain with the data owner. Implement one approved source format, a bounded representative question set and the first two task modules: ask questions in everyday language and return SQL-backed answers; generate charts and tables from returned results. Support the third module with operator review: build customizable dashboards for tracked metrics. 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 sources and question volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed dashboards, charts and plain-language answers linked to source queries. Retain the explicit scope boundary: One fixed data source set and metric dictionary; final metric definitions and access decisions remain with the data owner.

What the build depends on. Source connection and preview, asynchronous query jobs, editable version history, reviewer access and tested export formats. High-fidelity reporting requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed data source set and metric dictionary; final metric definitions and access decisions 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 source set. Offer a monthly production allowance after repeat demand. Quote complex multi-source or self-hosted deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed dashboard 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 dependence on rented BI tools and analyst queues while keeping data in the client's own environment. Demonstrate a concrete reviewed dashboard set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and operations teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample dashboard set from a small authorized data source, with a transparent calculation of accepted answers per analyst hour and corrections after dashboard approval and no promised savings.

The first 30 days

1. Week 1: interview five marketing and operations teams that need answers from their own databases without writing SQL and inspect a recent example of dependence on analysts or rented BI 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 answers per analyst hour and corrections after dashboard 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 answers per analyst hour and corrections after dashboard 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 answers per analyst hour and corrections after dashboard approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed dashboards, charts and plain-language answers linked to source queries. 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 metric definitions, source schemas and review examples, together with reliable delivery for a narrow reporting niche. Build a permissioned library of representative question cases, reviewer corrections and verified operating constraints for marketing and operations teams that need answers from their own databases without writing SQL. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Basedash, Athenic AI, Supaboard AI, Typper BI, Conduit.app, NBI.AI, Onvo AI, MindsDB Anton, Basedash Self-Hosted and Genie by Databox. Compare this product with the buyer's present method on accepted answers per analyst hour and corrections after dashboard approval. 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 connectors. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed dashboards, charts and plain-language answers linked to source queries. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data ownership, source attribution, metric accuracy and access permissions. Data owners approve metric definitions and sharing scope. One fixed data source set and metric dictionary; final metric definitions and access decisions 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.