Natural-language database query and reporting workspace

For

Data analysts and operations teams querying production databases without writing SQL

Solves

Non-specialist staff depend on engineers for routine database questions, slowing decisions and creating a queue of ad-hoc query requests.

Delivers

Reviewed query results, charts and scheduled reports

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

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

Reduce the queue of ad-hoc query requests while keeping data access under the owner's control.

1. Connect to MySQL, PostgreSQL, SQLite and other database systems.
2. Accept plain-English questions and generate runnable queries.
3. Keep processing local or on the owner's infrastructure.
4. Provide a conversational chat window for follow-up questions.
5. Suggest schema-aware completions while editing SQL.
6. Support keyboard-first operation with shortcuts.
7. Run queries with minimal delay and show progress.
8. Visualize results as charts and dashboards.
9. Export results to CSV, JSON, Markdown and Excel.
10. Import CSV and Excel files for analysis alongside databases.
11. Upload custom schemas to improve query accuracy.
12. Accept questions in languages other than English.
13. Reuse and modify queries within a session.
14. Show schema and relationships visually.
15. Execute reviewed update, insert and delete commands.
16. Detect query errors and suggest fixes in real time.
17. Analyze performance data to help diagnose database issues.
18. Insert mock data and design table schemas for testing.
19. Generate and schedule reports from database data.
20. Manage user permissions and roles for team collaboration.
21. Run queries and preview results from the terminal.
22. Connect with Claude Code for query construction and interpretation.

Everything these tools do, in one app

• Natural language querying Lets users type plain English to generate and run database queries.Found in Chat2DB Local, TurboSQL, NeoBase - AI Copilot for Database and 5 more
• Multiple database support Connects to several database systems such as MySQL, PostgreSQL, SQLite, and others.Found in Chat2DB Local, TurboSQL, NeoBase - AI Copilot for Database and 5 more
• Local or private processing Keeps data on the user's machine or in the browser without sending it to external servers.Found in Chat2DB Local, TurboSQL, Text2Query
• Chat interface Provides a conversational chat window for interacting with databases.Found in Chat2DB Local, AskYourDatabase Desktop
• Export results Saves query outputs in formats like CSV, JSON, or Markdown for sharing.Found in Chat2DB Local, Meet Macro Terminal
• Fast query execution Runs queries with minimal delay for quick results.Found in TurboSQL
• Keyboard-first workflow Enables shortcut-driven operation without relying on a mouse.Found in TurboSQL
• Schema-aware autocompletion Suggests code completions based on the database schema to speed up writing.Found in TurboSQL
• Data visualization Displays query results as charts or dashboards for easier interpretation.Found in NeoBase - AI Copilot for Database, AskYourDatabase Desktop, Haiva Analytics for SQL Databases
• Open source self-hosting Allows users to run the tool on their own infrastructure for data control.Found in NeoBase - AI Copilot for Database
• Large database handling Works with databases containing hundreds or thousands of tables.Found in AskYourDatabase Desktop
• Mock data insertion Inserts sample data into tables for testing or development.Found in AskYourDatabase Desktop
• Table schema design Helps create and modify database table structures within the app.Found in AskYourDatabase Desktop
• Excel integration Downloads data to Excel for further manipulation.Found in AskYourDatabase Desktop
• Custom schema upload Accepts uploaded database schemas to generate accurate queries.Found in Text2Query
• Multilingual support Allows users to ask questions in languages other than English, such as Spanish.Found in Text2Query
• Session query reuse Lets users modify and reuse queries within the same session.Found in Text2Query
• Schema visualization Shows database schema and relationships visually to aid design.Found in Hoop.dev for Databases
• Automated data manipulation Executes update, insert, or delete commands with minimal manual coding.Found in Hoop.dev for Databases
• Error detection Provides real-time feedback to catch and fix query mistakes.Found in Hoop.dev for Databases
• Context-aware AI agents Uses understanding of schemas and workloads to generate accurate queries.Found in Incerto
• Production troubleshooting Analyzes performance data to help diagnose and resolve database issues.Found in Incerto
• CSV and Excel import Imports data from CSV and Excel files for analysis alongside databases.Found in Meet Macro Terminal
• Terminal-based execution Runs queries and previews results directly in the command line.Found in Meet Macro Terminal
• Claude Code integration Connects with Claude Code to assist with query construction and interpretation.Found in Meet Macro Terminal
• Intuitive query builder Offers a visual interface to build queries without manual SQL scripting.Found in Haiva Analytics for SQL Databases
• Automated report generation Creates and schedules reports automatically from database data.Found in Haiva Analytics for SQL Databases
• Access control Manages user permissions and roles for team collaboration.Found in Haiva Analytics for SQL Databases

What goes in, what comes out

The workflow

1. In
   Start with

   Connected database schemas, uploaded schema files, CSV and Excel imports and access rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected database schemas

4. 3

   Uploaded schema files

5. 4

   CSV and Excel imports and access rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed query results, charts and scheduled reports

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. Read-only access by default; write, update and delete commands require named-owner approval. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Connection and schema setup, Natural-language query workspace, Results and report builder. Use a connection list for databases, a central chat and query canvas, and a right-hand panel for schema, permissions and comments. Let users compare generated SQL against the executed query side by side. Display draft, changes requested and approved states. Provide a shared report link with comments anchored to the relevant chart or table. Make the task-specific outcome reviewed query results, charts and scheduled reports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, connection credentials, schema versions, client comments, approval states, usage allowances, query limits, download 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

Owner-controlled databases, uploaded schema files, CSV and Excel imports and permitted research sources. Cloud asset storage, design-file import/export 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 MySQL, PostgreSQL, SQLite and other database systems; accept plain-English questions and generate runnable queries. 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 analysts and operations teams querying production databases without writing SQL use it to solve "non-specialist staff depend on engineers for routine database questions, slowing decisions and creating a queue of ad-hoc query requests"?
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: Answered data questions per analyst hour and query corrections after review.
4. Measure, then decide. Track answered data questions per analyst hour and query corrections after review; 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 database engine and read-only access; write, update and delete commands remain under named-owner approval. Implement one approved input format, a bounded representative case set and the first two task modules: connect to MySQL, PostgreSQL, SQLite and other database systems; accept plain-English questions and generate runnable queries. Support the third module with operator review: keep processing local or on the owner's infrastructure. 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 reviewed query results, charts and scheduled reports. Retain the explicit scope boundary: One approved database engine and read-only access; write, update and delete commands remain under named-owner approval.

What the build depends on. Database connection and preview, asynchronous query jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved database engine and read-only access; write, update and delete commands remain under named-owner approval.

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 database package. Offer a monthly production allowance after repeat demand. Quote complex multi-database or write-access workflows separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed query results, charts and scheduled reports. 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 the queue of ad-hoc query requests while keeping data access under the owner's control. Demonstrate a concrete reviewed query results, charts and scheduled reports using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Data analysts and operations teams querying production databases without writing SQL professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed query results, charts and scheduled reports from a small authorized input set, with a transparent calculation of answered data questions per analyst hour and query corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five data analysts and operations teams querying production databases without writing SQL and inspect a recent example of non-specialist staff depend on engineers for routine database questions, slowing decisions and creating a queue of ad-hoc query requests.
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 answered data questions per analyst hour and query corrections after review, 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: Answered data questions per analyst hour and query corrections after review. 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

Answered data questions per analyst hour and query corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed query results, charts and scheduled reports. 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, query patterns and review examples, together with reliable delivery for a narrow data-operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for data analysts and operations teams querying production databases without writing SQL. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Chat2DB Local, TurboSQL, NeoBase - AI Copilot for Database, AskYourDatabase Desktop, Text2Query, Hoop.dev for Databases, Incerto, Meet Macro Terminal, Draxlr AI and Haiva Analytics for SQL Databases. Compare this product with the buyer's present method on answered data questions per analyst hour and query corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, database connection and 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 reviewed query results, charts and scheduled reports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data access boundaries, source attribution, query accuracy and usage permissions. Data owners approve substantive changes and write access scope. One approved database engine and read-only access; write, update and delete commands remain under named-owner approval. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.