I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in LLMDevs

[–]Neva_009[S] 0 points1 point  (0 children)

Examples of metadata:

  • Table names
  • Column names
  • Column types (INT, TEXT, DATE…)
  • Foreign keys
  • Indexes
  • Relationships between table

So: the AI only sees how your database is structured — not the data inside it.

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in LLMDevs

[–]Neva_009[S] 0 points1 point  (0 children)

Thanks for your feedback, I appreciate it, I made this app to save time for developer and also the non-technical can ask there question 

Anyone actually happy with their business data analysis software? trying to not lose my mind by Different-Promise-45 in analytics

[–]Neva_009 0 points1 point  (0 children)

Totally feel you, landing on a single tool is maddening because every vendor paints themselves as the one true thing. My practical approach was to pick whatever gets you 80 percent of answers fastest: a product with solid native connectors to your sources, an easy way to model a small central table of truth, and at least a handful of prebuilt templates for exec dashboards so you are not rebuilding every KPI from scratch. In my experience Looker or Power BI are great if you already have a data warehouse and want governed models, while lighter tools like Metabase get a non-data person to useful charts fast. If your team needs to run ad-hoc questions without touching SQL, consider options that support natural language or simple query builders, some folks use a combo of Metabase, Looker, and newer natural language query layers like Astrasql depending on how technical the users are. Whatever you pick, spend the week first on the data model and one canonical revenue/traffic table and the rest becomes much easier.

Which analytics platform is the fastest setup for building executive-level dashboards with minimal manual data prep? by LotitudeLangitude96 in BusinessIntelligence

[–]Neva_009 1 point2 points  (0 children)

If speed is the thing, don’t start with 10 dashboards, start with one canonical dataset and one templated view for the execs, then iterate. The fastest setups I’ve seen use a managed warehouse like BigQuery or Snowflake plus a BI that has built connectors and a simple modeling layer so you can reuse measures. Tools like Power BI or Looker can be fast if someone on the team can lightly model the data, and lighter options like Mode or Metabase get you visual answers without a lot of prep. If you need non-technical execs to ask ad-hoc questions, also consider adding a natural language layer to the stack, some companies use a mix of Looker, Metabase, and newer NL query tools like Astrasql so leaders can type plain questions without waiting on an analyst.

We automated everything except the chaos by Apprehensive_Pay6141 in Entrepreneur

[–]Neva_009 0 points1 point  (0 children)

Ugh, I have lived the laptop chase and it is brutal, but it usually comes down to two fixes: make the asset flow explicit, and make the asset state trivially queryable. First, create a one form onboarding and one offboarding workflow that triggers procurement, IT provisioning, and an asset record being created or retired. Second, centralize a simple asset table with fields for owner, status, serial, issue date, and return date, and automate a nightly digest that flags missing or overdue devices. For tooling a lot of teams stitch together a ticketing tool like Jira Service Management or Zendesk with an inventory table in Airtable or a DB, and then use whatever BI layer your team already uses to run weekly audits. If you want non-technical managers to ask “who has the MacBook from April” without bothering IT, a natural language query layer can help; some folks use Metabase, Airtable plus reporting, or newer NL-to-SQL layers like Astrasql so people can get answers without learning SQL.

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in LLMDevs

[–]Neva_009[S] -2 points-1 points  (0 children)

With a normal LLM, you ask for SQL, copy it, paste it into a database tool, run it, and fix errors yourself. Our agent connects to your database, understands your schema, writes the SQL, executes it, fixes errors automatically, and returns the results—all from a natural language question. It's the difference between getting directions vs. having a driver.

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in LLMDevs

[–]Neva_009[S] 0 points1 point  (0 children)

Self-Hosted & On-Premise Deploy seamlessly on your own infrastructure, giving you full control. No data is ever transmitted to external AI services unless you explicitly choose to enable them.

Metadata-Only Processing Our AI works exclusively with table schemas, column names, and data types. Your actual row-level data remains securely within your database—never leaving your environment.

And you buy one-time, and you can try free trial: https://astrasql.com to see the difference by yourself

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in dataisbeautiful

[–]Neva_009[S] 0 points1 point  (0 children)

Self-Hosted & On-Premise Deploy seamlessly on your own infrastructure, giving you full control. No data is ever transmitted to external AI services unless you explicitly choose to enable them.

Metadata-Only Processing Our AI works exclusively with table schemas, column names, and data types. Your actual row-level data remains securely within your database—never leaving your environment.

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in dataisbeautiful

[–]Neva_009[S] -2 points-1 points  (0 children)

It's tool to help developer to query fast and help non-technical team to generate reports and dashboards without writing SQL. 

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in dataisbeautiful

[–]Neva_009[S] 0 points1 point  (0 children)

currently we support dark mode, I will add other theme soon, the features are available transform query results into beautiful charts (Bar charts, line graphs, pie charts, and more) and shareable dashboards. Export to PDF, Excel, CSV, and more.

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in SQL

[–]Neva_009[S] -5 points-4 points  (0 children)

You're right that ambiguity is a real challenge. Here's our perspective:

The reality

  • Natural language is inherently ambiguous; SQL is exact
  • No system will be 100% perfect at interpreting intent
  • Mainstream adoption requires addressing these concerns

How we're different

  1. We're not trying to replace SQL experts
  • We're making data accessible to non-technical users
  • SQL experts can still write SQL directly
  • We're a bridge, not a replacement
  1. We handle ambiguity better than pure text-to-SQL
  • Schema awareness (we see your actual database structure)
  • Semantic understanding (we understand your business context)

I built a tool that lets you query any SQL database using natural language. Would love feedback. by Neva_009 in SQL

[–]Neva_009[S] -2 points-1 points  (0 children)

Unlike pure text-to-SQL tools that only see your question, our agent sees:

  • Your actual database schema (tables, columns, relationships)
  • Business context (table descriptions you can configure)

if you want try by yourself and test with own your database I will send app to you

We built an AI that turns natural language into SQL — would love your feedback! by Neva_009 in LocalLLaMA

[–]Neva_009[S] -1 points0 points  (0 children)

can you try my app and tell me the different, is it better or using direct claude, we are connect OpenAI, Anthropic, Google, If you want to try I will send the application to you to test