AI app for it and development · no coding needed
Schema-linked SQL generation and repair console
Reduce query authoring and repair time while keeping execution under developer control.
Made for: Developers and database administrators writing and maintaining SQL against production and staging databases
What it does for you
The problem
Writing, fixing and tuning SQL across several database systems is slow, error-prone and spread across disconnected tools.
What it gives you
Developer-approved SQL with source references
What you give it
Live database schemaplain-language instructionsexisting query text
Build your own version of Reindeer, AIHelperBot and more
One app with what these 4 AI tools do, yours to keep and change: Reindeer, AIHelperBot, FluentDB, Codifyer.
Everything these tools do, in one app
- Natural language to SQL Turns plain-language instructions into SQL queries.Found in Reindeer, AIHelperBot
- Schema-aware generation Uses your actual database schema to produce accurate queries.Found in Reindeer, AIHelperBot
- Inline autocomplete Suggests table and column names while you write queries.Found in Reindeer, AIHelperBot
- Query fixing Finds and fixes errors or suggests refactors for existing SQL.Found in Reindeer, AIHelperBot
- Query optimization Suggests improvements to make queries run faster and use fewer resources.Found in AIHelperBot
- Query formatting Simplifies and formats complex queries for readability.Found in AIHelperBot
- Save query snippets Stores generated queries for quick reuse.Found in AIHelperBot
- Multiple database support Connects to several database systems such as PostgreSQL, MySQL, and others.Found in AIHelperBot, FluentDB
- IDE integration Works inside your development environment without switching tools.Found in Reindeer
- Native desktop app Runs as a lightweight native application rather than a browser-based tool.Found in FluentDB
- Bring your own AI model Lets you use your own API keys or local models for AI processing.Found in FluentDB
- Privacy controls Limits AI access to schema only, not row data.Found in FluentDB
- Query approval workflow Requires user approval before any generated SQL is executed.Found in Reindeer, FluentDB
- Read-only mode Blocks write queries at the connection level to prevent accidental changes.Found in FluentDB
- Query cancellation Stops long-running or accidental queries with one click.Found in FluentDB
- Database code generation Automatically creates database code such as tables, fields, and relationships from a schema description.Found in Codifyer
- CRUD automation Generates repetitive create, read, update, and delete operations automatically.Found in Codifyer
- Cloud-ready output Produces code optimized for deployment on cloud platforms.Found in Codifyer
How it works, step by step
- Turn plain-language instructions into SQL queries
- Use the actual database schema to produce accurate queries
- Suggest table and column names while writing queries
- Find and fix errors or suggest refactors for existing SQL
- Suggest improvements to make queries run faster and use fewer resources
- Simplify and format complex queries for readability
- Store generated queries as reusable snippets
- Connect to several database systems such as PostgreSQL and MySQL
- Work inside the development environment without switching tools
- Run as a lightweight native application
- Use your own API keys or local models for AI processing
- Limit AI access to schema only, not row data
- Require user approval before any generated SQL is executed
- Block write queries at the connection level
- Stop long-running or accidental queries with one click
- Generate database code such as tables, fields and relationships from a schema description
- Generate repetitive create, read, update and delete operations
- Produce code optimized for deployment on cloud platforms
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned developer-approved SQL set with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Schema-linked SQL generation and repair console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Schema-linked SQL generation and repair console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data198 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce query authoring and repair time while keeping execution under developer control. For developers and database administrators writing and maintaining SQL against production and staging databases, convert the live schema, plain-language instructions and existing query text into developer-approved SQL with source references, review state and a value baseline. The benefit is a testable hypothesis, measured through accepted queries per developer hour and corrections after execution; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect the live schema, plain-language instructions and existing query text, then follow this sequence: 1. Turn plain-language instructions into SQL queries. 2. Use the actual database schema to produce accurate queries. 3. Find and fix errors or suggest refactors for existing SQL. Resolve uncertain cases with qualified reviewers, approve developer-approved SQL with source references, and measure accepted queries per developer hour and corrections after execution against a documented baseline.
How the AI works
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. Read-only mode and schema-only access; final execution and data-change decisions remain with the developer. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve schema confidentiality, source attribution, query accuracy and usage permissions. Developers approve substantive changes and execution scope. One database system and read-only mode; final execution and data-change decisions remain with the developer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One database system and read-only mode; final execution and data-change decisions remain with the developer. Implement one approved schema format, a bounded representative query set and the first two task modules: turn plain-language instructions into SQL queries; use the actual database schema to produce accurate queries. Support the third module with operator review: find and fix errors or suggest refactors for existing SQL. 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.
What it can connect to
Developer-owned database connections, version control and permitted schema exports. Cloud database services, IDE plugins and deployment destinations. Start with file exchange and validate destination specifications before promising direct execution. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Connection and schema scope, Query workbench, Review and execution log. Use a connection list for projects, a large central editor with results pane, and a right-hand panel for schema, snippets and comments. Let users compare generated and original queries side by side. Display draft, changes requested and approved states. Provide a review link with comments anchored to the relevant query and schema object. Make the task-specific outcome developer-approved SQL with source references visible beside its evidence, review state and value baseline.
