Text-to-SQL AI Agent Service — Built for You

✓ Read-only & permission-scoped ✓ Knows your business terms ✓ Answers with the SQL shown ✓ Tested on your real questions

Quick answer

What is a text-to-SQL agent? 

A text-to-SQL agent is an AI system that turns a plain-language question into a SQL query, runs it on your database, and returns the answer. Production agents add a semantic layer (business definitions), query validation, safety limits and self-correction.

What does this service include? 

Codersarts engineers map your schema and business terms, build the agent with read-only access and query validation, return results as tables or charts, connect it to Slack, a web app or an API, and measure accuracy on your real questions.

How long and how much? 

Starter from $149 (3 days, up to 10 tables, 20-question test). Standard $299 (5 days, up to 50 tables, charts, 50-question test). Advanced $599 (10 days, multiple databases, Slack/web UI, access rules and monitoring).

Is this your problem?

• Managers wait days for analysts to pull simple numbers

• Your data team spends hours on repetitive ad-hoc SQL requests

• Dashboards don't answer the question someone actually has

• You tried "chat with your database", and it returned confident but wrong numbers

• Column names like cust_rev_q_adj mean nothing to an AI model or a new employee

• Security won't approve AI access to the production database

Why most text-to-SQL demos fail on real databases

• Benchmarks flatter the problem. On BIRD, a benchmark built on real databases, the best single model reached 80.04% execution accuracy (Google Gemini-SQL2, June 2026), against 92.96% for human experts. On Spider 2.0, an enterprise benchmark averaging around 800 columns per database, the best model in the original study solved only 21.3% of tasks, compared with 91.2% on the simpler Spider 1.0.

• The model doesn't know your business. "Active customer", "net revenue" and "churn" have company-specific definitions that aren't in the schema.

• Large, messy schemas. Hundreds of tables, cryptic names and duplicate columns confuse retrieval.

• No validation. Queries that run aren't necessarily correct: wrong joins, double counting, missing filters.

• Security risk. In a documented incident, prompt injection hidden in a support ticket led an AI agent with an over-privileged database connection to leak sensitive tokens. Agents need read-only, least-privilege access.

We close the gap with a semantic layer, curated examples, validation, strict permissions and testing on your questions.

How a production text-to-SQL agent works

User question (Slack / web / API)
      ↓
1. Understand  →  map terms to your semantic layer ("revenue" = SUM(net_amount) WHERE status='paid')
2. Retrieve    →  find relevant tables, columns, example queries
3. Generate    →  write SQL for your dialect (Postgres, MySQL, BigQuery, Snowflake…)
4. Validate    →  syntax, allowed tables only, row limits, no writes, cost checks
5. Execute     →  read-only role, timeouts
6. Self-check  →  retry on errors; sanity-check results
7. Answer      →  table / chart + plain-language summary + the SQL used

What we do

1. Scope check: databases, tables in scope, users, top questions, permissions and where people will ask.

2. Schema & semantic layer: document tables, relationships and business definitions (metrics, filters, synonyms).

3. Example library: curate verified question-to-SQL pairs that guide the model.

4. Build the agent: retrieval, SQL generation and self-correction using the model of your choice (OpenAI, Claude, Gemini or a private model).

5. Safety: a read-only database role, an allow-list of tables/views, row and time limits, PII masking, and query logging.

6. Output: tables, charts and plain-language answers, with the SQL shown for transparency.

7. Interface: a web chat, Slack/Teams bot or API (per package).

8. Testing: run your question set, measure accuracy, fix failures and expand the examples.

Packages

Add-ons: Priority delivery (+50%) · Extra database (+$79) · Slack integration (+$49) · Chart output (+$39) · 30-min walkthrough call (+$25) · 14-day support (+$39)

Final scope and price are confirmed after a free 30-minute scope check. AI model usage costs are paid to your provider.

Supported databases & tools

Use cases

Safety by design

• Read-only database role: the agent can't insert, update or delete

• Table/view allow-list: sensitive tables such as tokens, passwords and payments stay out of reach

• Row-level rules: users see only data they're allowed to see (Advanced)

• Limits: row caps, query timeouts and cost checks for warehouses

• PII masking: sensitive columns hidden or masked in answers

• Untrusted text handling: content stored in the database is treated as data, never as instructions

• Audit log: every question, generated SQL and result is logged (Advanced)

Text-to-SQL agent vs dashboards vs analysts

The agent doesn't replace dashboards or analysts. It handles routine questions so analysts can focus on deeper work.

What you get

• A working text-to-SQL agent (web chat, Slack/Teams or API)

• A semantic layer and example library you can extend

• Read-only, permission-scoped database access

• An accuracy report on your real questions (Standard+)

• Query logs and monitoring (per package)

• An admin guide for adding tables, metrics and examples

• Proof of done: a live demo answering your test questions, with the SQL shown

What we need from you

Required (the delivery clock starts once we have these):

• Read-only database access (or a schema export plus a staging copy)

• Tables or views in scope

• 20–100 real business questions, with the correct answers if available

• Business definitions for key metrics (revenue, active user, churn…)

• Where users will ask (web, Slack, Teams, API) and who should have access

Optional:

• Existing SQL queries or dashboard definitions to learn from

• Preferred AI model or a private-model requirement

• Data privacy rules (PII columns, row-level access)

• Whether you need an NDA

How it works

1. Free scope check (30 min): databases, questions, safety and package.

2. Model the data: semantic layer, examples and safety setup.

3. Build & test: agent, interface and accuracy testing on your questions.

4. Review & revise: adjust within your package's revisions, then roll out.

Why Codersarts Tasks

• One accountable team: Codersarts engineers with data and AI experience build and test the agent.

• Accuracy you can see: every answer shows its SQL, and Standard+ includes an accuracy report.

• Security first: read-only, least-privilege access by default.

• Room to grow: extend to more databases, dashboards or a full AI analytics product.

Related tasks

• Build MCP Server for Your Database (Postgres, Supabase): give Claude or other agents safe database access

• Build AI Agent Workflow

• Build Dashboard from Dataset: for KPIs you track every day

• Optimize Slow SQL Query

• Set Up Private Local LLM (Ollama, vLLM): keep queries and data fully private

Bigger than one task? Plan an AI analytics build →

FAQ

What is a text-to-SQL agent? 

It's an AI system that converts plain-English questions into SQL, runs them on your database and returns the answer, often as a table or chart. Production agents add business definitions, validation and security controls.

How much does it cost? 

From $149 for one database with up to 10 tables and a 20-question accuracy test. Standard ($299) covers up to 50 tables with charts and a 50-question report. Advanced ($599) adds multiple databases, Slack/Teams, row-level access rules and monitoring.

How accurate is text-to-SQL? 

It depends heavily on schema clarity and business context. On the BIRD benchmark, the best single model scored 80.04% against 92.96% for humans, and complex enterprise benchmarks score much lower. We raise accuracy for your data with a semantic layer, verified examples and validation, then measure it on your questions.

Is it safe to connect AI to our database? 

Yes, with the right controls. We use a read-only role, an allow-list of tables or views, row limits and timeouts, optional row-level rules and PII masking, and log every query. The agent can't change your data.

Which databases are supported? 

PostgreSQL, MySQL, SQL Server, SQLite, Supabase, BigQuery, Snowflake, Redshift, Databricks SQL, ClickHouse and others with a SQL interface.

Can it handle our business terms like "active customer" or "net revenue"? 

Yes. We define them in a semantic layer so the agent uses your exact definitions every time.

Can it create charts? 

Yes. Standard and Advanced return charts alongside tables and a plain-language summary.

Can it run in Slack or Microsoft Teams? 

Yes. Advanced includes Slack or Teams, and Slack is also available as an add-on.

Can we use a private AI model? 

Yes. We can use a self-hosted open model so queries and results never leave your infrastructure.

Will it replace our data analysts? 

No. It answers routine questions instantly so analysts can focus on deeper analysis.

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