AI for Databases: Field Guide, Live Apps & Lessons

Field Guide

15+ Months of Client Operations

Connect to databases, analyze and visualize with natural language to SQL

Live App: www.tigzig.com

Deploying AI on Databases

Lessons

Live Apps (4 Variants – 8 Apps)

Source Code

DATS-4: Database AI Suite – Version 4

GPT-5: Initial Assessment & Live Integration

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Deployments

Build by Practitioner. Built for Business: The DATS-4 suite is built by a data scientist for internal teams. The design prioritizes analytical agility and rapid deployment in secure SMB environments. This involves different trade-offs than the standards for large-scale enterprise software.

Live History: The first client deployment was in April 2024. There are currently 9 live, customized versions running across 3 SMB clients.

Implementation Variants: Client projects vary based on need-specific components only, custom GPTs connected to databases, rapid-deploy version & customizations.

The Public App (www.tigzig.com): Fully functional version of the suite. It has been configured as a minimal security sandbox to allow for unrestricted testing of the core features.

Live Project Checklist: ALL client projects include a mandatory checklist: security layers, semantic model, fixed database connections, and disabling of admin features for end users

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DATS-4 Evolution

Database AI Suite – Version 4

| OSS Release | Name | Additional Features

V1 | Jun ’24 | Analytics Assistant App | Flowise UI + FastAPI for Text-to-SQL MySQL support Python charts & stats ChatGPT connected to Databases

V2 | Nov’24 | REX-2 | React UI Flowise chatflow backend Postgres support Interactive grid Direct file upload to DB PDF reports Quick analysis options OAuth

V3 | Feb’25 | REX-3 | Multi-step reasoning based analysis Choice of multiple LLM Flowise sequential agent backend Agent reasoning view Quick try sample functionality Logs

V4 | Aug’25 | DATS -4 | Flowise new multi agent backend Updated LLM Choices w/ GPT-5 Database table export & CSV Download Export to PDF (Text only) Updated UI Portfolio analyst integration

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Field Report: GPT-5 First Look

From my experience, new model releases often have higher, volatile latencies and costs in the first days or weeks, then stabilize over time.

My preliminary assessment of GPT-5 first few days of release

Reasoning & Analysis: close to Claude Sonnet 4

Latencies: higher - temporary phenomenon

Costs: higher than expected : temporary phenomenon

Variance: Given the amount of variance I am seeing with GPT-5, cost estimates would not be reliable. I am holding off on sharing exact cost estimates for GPT-5 for the time being. But based on published rates, I expect them to stabilize around the GPT-4.1 levels

Integration: At the same time, I have incorporated GPT-5 as an LLM Choice options in the public DATS-4 for users to try out and compare results

Detailed cost comparisons and model choices are covered in the LLM section later in this guide

I typically migrate clients only once I am confident that the model performance, cost and latencies have stabilized.

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1. Lessons

Security

Datamart & Context

Agent Setups

LLM Choices

LLM Cost

Usage Patterns

Platforms

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Security

Align with rules – set by DB and server admins. They are troublesome but will save your bacon one day.

No end user touches the raw tables– even with SELECT access

Separate user ID’s at DB level with fine grained permissions

Row Level Security – use with Postgres

Separate : schemas / database / views for say Finance vs. Marketing. The additional maintenance effort is worth it.

Authentication: OAuth / API Keys

Log all API calls : push to a DB / 3rd party tools

IP / Domain Whitelist : FastAPI / DBs / Agents / all end-points

CORS : for all FastAPI, with domain whitelist

Resource Limits for CPU & Memory – implement on server

Rate Limits: at FastAPI (with SlowAPI) and Agent end

Server: Firewalls, only SSH, Fail2Ban, IP Whitelists etc.

VPNs: default deployment always on a client VPN.

Security is expensive – direct cost, bandwidth and business opportunity loss. Every layer adds cost and user friction. Assess risk of breach for each data item, worse cases and potential impact. Apply layers accordingly. Everything is not catastrophic.

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Building the Foundation Datamarts and AI Context

Datamarts

Creating usable datamarts is one of the most time consuming things, especially the data cleaning and validating against reported numbers.

Need to know: create custom datamarts and views for specific use cases. Operate on need to know basis.

Auto Refresh: setup auto refresh of datamarts

Validation reports: validation reports for all datamart refreshes is mandatory

Be alert: After running for months, a validation can suddenly fail out of the blue. You must be ready to catch it.

Context

Use system prompt to provide context to AI

Sample rows

Univariates for numerics & distributions for categoricals

Business rules and business context

Golden queries – sample queries for common requests, particularly for the more complex queries

Output formats / row limits / data gotchas

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Agent Setup

Agent backend on Flowise AI, with conditional routing based on type of request. A multi-sequential agent setup

Conditional routing agent will route to advanced analyst or general analyst based on a set of guidelines or if specifically instructed by user

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Agent Setup

1. The Dispatcher (Conditional Router Agent)This is the gatekeeper. Its only job is to analyze the user's request and route it to correct specialist agent based on a set of rules.

2. The Workhorse (General Analyst Agent)This is GPT-4.1 mini - optimized for execution speed. It handles the majority of requests: direct SQL queries, data pulls, and standard charts. It does not perform multi-step reasoning. It directly executes, validates, and returns the result.

3. The Specialist (Advanced Analyst)This is a two-step routing, used only for complex requests that require reasoning.A. The Planner: First, a reasoning-focused LLM (choice of LLMs) creates a step-by-step analysis plan, including the exact SQL and python code required.B. The Executor Agent: This is GPT-4.1 in all cases - reviews and executes that plan, performing final error checks and formatting the output.

The Executor agent will be upgraded to GPT-5 series once its cost and latency have stabilized

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Equipping the Agents: Core Tools

1. Database Connect

Custom FastAPI Server

Allows agent to connect to database to execute SQL queries

2. e2b Code Interpreter

Flowise built-in tool

Python sandbox

To create charts and run statistical analysis

3. Markdown to PDF

MCP Server

To create PDF (text only) output. The agent sends markdown to the MCP Server, which returns a PDF file path.

The core DATS-4 agent uses three primary tools:

The system is modular, allowing other tools to be plugged in as needed: web scrapers, Excel updaters, report emailers, file converters, custom automations and more.

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Agent Orchestration

No 100% : You will never get 100% what you instruct 100% of the time. Test and determine what variance you can live with.

Edge cases: test edge cases and outliers. Calibrate instruction till you get your desired outcome

Break it : Push it to limits. See where it trips and falls.

Reasoning required ? – if so , specify . Not always required.

Number of Queries – CRITICAL to specify a cap on number of SQL queries an agent can run for a single question.

CREATE / ALTER/ DROP : specify if they are allowed or not

Temporary tables : specify if permitted and how (CREATE TEMP or CREATE TABLE) , and cleanup protocols

Limit clause: how many rows ? When to use ? When not?

Division by zero: common error – COALESCE(), NULLIF() etc

Debug : debugging protocol for query failures

Reminders help – remind to check for common issues – missing table, table exists, joins, data type mismatches etc

To get an agent to deliver the right outcome, you have to test and calibrate- sometimes 100s of times. It's the only way. The are the rules I follow

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Agent Backend

Don’t reinvent the wheel.

Don’t reinvent the wheel: use tools like Flowise/ n8n as first choice- they take care of many nuances out-of-box. Connect user interface via API calls.

Flowise AI: is my first choice. Robust out-of-box memory and state management and numerous other features. Great for complex agent workflow, especially for sequential flows.

n8n – for app integrations and where Flowise not the best fit.

Hard-coded agents: used only for functionality that framework tools can't support.

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LLM Choices

For end-user applications, use frontline providers (OpenAI, Google, Anthropic). They offer the best combination of reliability, consistency, quality and pricing. For internal analytics work – practitioners should test and use other models per their own judgment.

My Top Recommendations

SQL Executions: GPT-4.1 (GPT-5 once stable) for complex and 4.1-mini (GPT-5-mini once stable) for rest. GPT-4o-mini is excellent for simpler and repetitive requests.

Tool use: for all tool use functions, OpenAI’s GPT models - effective, reliable and cost efficient

Non-Tool LLM use: Gemini Flash 2.0/ 2.5 as first choice for non tool tasks - e.g. automations, schema detection, reasoning, planning

Complex: Claude Sonnet 4 for the hardest and most complex tasks

Other LLMs: DATS-4 provides LLM choices including DeepSeek, Qwen & GLM. Great quality and pricing. But I see a lot of variance in billed cost and latencies. DATS-4 allows for easy integration of other LLMs

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LLM Costs: Guidelines

Use Case: Always estimate for your specific use case. Review actual charged API costs. Don’t rely on published rate.

Lowest Cost: GPT-4o mini and Gemini Flash 2.0 are older model, but robust, lowest cost and great for many tasks. Test them first

Value: GPT-4.1-mini (GPT-5-mini once stable) and Gemini Flash 2.5 - great workhorses at reasonable cost.

GPT-4.1 for harder tasks especially complex SQL executions (GPT-5 once stable)

Claude Sonnet 4.0 is an all rounder and the best, but expensive. Keep for most complex reasoning.

DeepSeek. Qwen, GLM and others – High latency and cost variance based on provider. DeepSeek more stable now.

Single step agents for direct questions = low cost.

Multi-step agents = exponential cost increase. Use with care. See next sections

Number of SQL queries an agent is allowed has direct cost impact. 2 queries per question vs. 10 queries= 5x cost

Context – piles up with same session adding to cost. New question = open new session

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LLM Costs: 1 Question 1 Query

LLM | ~USD | Remarks

GPT-5 | Volatile | New release - high variance. Expect to stabilize around GPT 4.1 levels

GPT-4.1 | 2.0 | Best for complex SQLs

GPT-4.1-mini | 0.50 | Great for med. complexity SQL

GPT-4o- mini | 0.25 | Great for simple/ med. complex

Cost Per 100 Simple Questions

1 Question = 1 SQL Queries/ Tool Call

Single step: no reasoning step, direct execution

Example of single question

Share sample rows

Add new columns as per instructions

Join Table A & B by cust_id

Summarize by housing and show counts

Share chart for housing summary

Actual vary by use case and the agent setup. Always estimate for your use cases and compare vs. actuals.

For all estimates – keep in mind that as context increases the cost goes higher

~USD per 100 Q

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LLM Costs : Advanced Analysis

~USD per 100 Q

Reasoning Model | Quality Score | Logic USD | Exec. USD | Total USD | Remarks

Gemini Flash 2.0 | 75 | 0.25 | 12.5 | 12.75 | Best value

Gemini Flash 2.5 | 75 | 1.75 | 12.5 | 14.25 | Next after Flash 2.0

Gemini Pro 2.5 | 85 | 8.50 | 12.5 | 21.00 | Avoid. V.High.Cost

Claude Sonnet 4 | 100 | 6.50 | 12.5 | 19.00 | Topmost Quality

DeepSeek R1 | 90 | 2.25 | 12.5 | 14.75 | Great Value

Qwen 3 | 75 | 3.50 | 12.5 | 13.25 | High variances

GLM 4.5 | 80 | 1.00 | 12.5 | 13.50 | High variances

o4-Mini | 75 | 2.75 | 12.5 | 15.25 | Avoid.

GPT-4.1 | 90 | 3.00 | 12.5 | 15.50 | Great Value

GPT-5 | 95 | Volatile | | | Top Quality

Example of one Advanced Analysis Question (shortened)

Create Weighted Average score based on available variables

Modelling Data Mart : Take transaction table, summarize based on cust_id and create derived variables. Summarize and merge with customer data to create a modelling data mart

In multi-step reasoning-based analysis, execution cost is biggest chunk due to multiple tool calls.

1 Question = 7-10 SQL Queries / Tool Calls.

All executions by GPT-4.1. GPT-5 costs, once stable, likely to be around same levels.

Time: ~2-3 mins per que. Can go upto 10m. Varies by question.

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Advanced Analysis: Costs Vs. Quality Matrix

Claude Sonnet-4 and GPT-5 are top-tier models for advanced reasoning.

Estimates based on live deployments & 250+ test runs

Quality scores are a judgment-based assessment of the model's analytical reasoning depth

Estimates vary – always estimate and check actuals for your use cases

*** GPT-5 is plotted at its projected stabilized cost (equal to GPT-4.1) for quality comparison only. Current costs are volatile and are not plotted..

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Warning: The Cost Multipliers of Multi-Step Agents

No. of Steps: 2 Step = double the context = 2X the cost

Number of tool calls Determined by # of SQL Queries allowedSimple Question = 1 SQL QueryAnalysis Question = 2 to 10+ SQL Queries=10X cost

Execution ModelNeeds stronger model GPT 4.1 vs 4.1 mini = 5X

Additional LLM Cost for reasoning- ~ 7 cents per question for Sonnet 4

Context is a Multiplier, not an addition: larger semantic models, context, and system instructions don't just add to the cost; they multiply it with every step

DebuggingIn case of SQL query error - LLM will auto debug and re-run, taking up additional tool calls and costs.

For multi-step analysis, costs don't just go up; they escalate exponentially - from 10X to 50X or more. This is a critical budget risk. Key factors:

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Usage Patterns

The highest adoption I see is from operations, marketing, and finance teams. The following are the most common usage patterns from my client deployments

Operations, Marketing & Finance Teams

Natural language interface to backend datasets and uploaded CSV files

Pull specific customer and transactions records for review

Recon between finance and ops data

Insert / Update / Delete records

Download filtered data for offline analysis in Excel

Append fields and field cleanups

Generate summary reports with standard prompts for reuse

Generate PDF output

Many users prefer AI interface over their existing interfaces given the range of operations they can carry out and the efficiency of direct integration with automations

Analytics Folks

Pre-process raw tables and then download for offline analysis

Adhoc queries

Database level tasks requiring SQL

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Platforms

Servers: Very often client determined. But where you have a choice, here are my defaultsServer based: Hetzner + Coolify for deployments. Allows a firewalled environment to deploy any apps and databases. Reliable performance and pricing.Serverless : Vercel for React & Render/ Railway for FastAPI

Databases - Neon: instant Postgres DB creation, deletion etc via API Top choice for AI apps requiring instant temp. databases- Aiven: great free tier. - Supabase: integrations esp. auth. - Standard / Self-Hosted: AWS RDS or Hetzner with Coolify

SQLite solid option for in-browser work. Requires setting up SQL Agent from the grounds up.

LLM Gateway: OpenRouter provides a single point gateway to all major LLM including the latest e.g GLM 4.5. Also, great set of reports to monitor costs.

Custom User Interfaces: React / NextJS / HTML-JS

Backend Services: FastAPI

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2. Live Apps

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DATS-4 : Deployed Live

www.tigzig.com

Path: Database AI & SQL Apps

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4 Variants – 8 Live Apps

All apps live, fully functional and open source. DATS-4 is the flagship app.

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DATS-4: How to Use

Option 1 : Customize & Deploy

Option 2: Try Live on www.tigzig.com

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1. Customize & Deploy

Open Source

Customize & deploy on your server/ VPNs

Key Customization Areas

Security

User API Keys / Oauth

Parameterized queries

DB user ID with restricted privileges

Row Level Security w/Postgres

Context : Schemas, rows, queries, business rules

Interface : customize based on user needs

Components: deploy full suite or components

Functionality : integrate additional functionalities

Core Deployment Patterns:

Full Suite: DATS-4 with custom UI.

Custom GPT: connected to the database backend.

Rapid UI: Flowise Agent UI for quick deployment.

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2. Try live on www.tigzig.com

The public site is a minimal-security sandbox configured to provide an unrestricted environment for testing the suite's full range of capabilities.

WARNING: All database credentials and queries submitted via the public app are logged on the backend. Use this sandbox with non-sensitive data and credentials only.

ADMIN-LEVEL ACCESS: The full DATS-4 suite is an admin-level tool with extensive logging. For end-user deployment, you must restrict functionality and customize logging configurations.

Use on-the-fly temporary Postgres database generated by the app or create one instantly at Supabase/ Neon/ Aiven

Use the sample files on the app / google drive

Practitioner’s Warning

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Interface Components

2 Agents : Main Database Analyst and Quants Analyst

Sample data for rapid testing

Menu option to upload files and connect to databases

On-the-fly temporary Postgres database

Choice of LLM for advanced analysis

Chart & Document pane

Logs

File uploads: interactive grid and automated data quality metrics

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Choice of LLM for Advanced Analysis

Choose your LLM for the reasoning step. The app setup also allows an efficient method to add and remove LLMs

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Core Workflows

1. Sample File Test

Use the built-in sample data and an on-the-fly temporary database for rapid evaluation

2. File Upload

Upload a local file (CSV/Tab Delimited) to a temporary or user-provided database.

3. Direct ConnectionConnect directly to a remote Postgres or MySQL database

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1. Sample File – Rapid Test

Copy and paste ready to use starter prompt for quick analysis once database is setup. Or go with your own request.

Use on-the-fly temporary Postgres database OR connect to your own DB.

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2. Upload Your File

File schema sent to AI automatically. Go to Advanced Analyst tab and ask questions, analyze, create and customize charts – in natural language

Use an instant temporary Postgres DB OR connect to your DB

Select your file for upload. Supports CSV and tab delimited

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3. Connect your Database

Go to Advanced Analyst tab

The AI agent does not automatically know your database schema upon connection. You must instruct it to list tables or query sample rows to provide it with the necessary context for analysis.

Query, Analyze, Merge, Summarize, Visualize

Menu -> Connect to DB

Postgres and MySQL supported

Paste your DB credentials. Format does not matter – URI / table / text – AI will parse it

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Agent Reasoning View

Full trace of agent’s reasoning process from business context and feature engineering to the final SQL queries and degug logs

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Dedicated Charts & Docs Panel

Single-click toggle to open/close charts & document panel

Dedicated, full-screen chart panel for visualizations.

Dynamic document panel for live report and data updates.

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Python Charts

Integrated Python Interpreter for charting. All charts below were generated directly in the app.

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Statistical Analysis

The integrated Python Interpreter enables full statistical analysis, not just charting.

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PDF Output

On-demand, formatted PDF report generation for all analysis and query outputs (text only)

Report structure and content are fully customizable via natural language instructions.

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Detailed Logs

Detailed logging of key API calls and actions

Valuable for first line of debugging

Full logs are ‘admin’ level with sensitive info being logged. Restrict as per security access. For end users, remove / customize logging as per requirement

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Export Tables

Perform transformations and create new tables, then export any table to a local file (CSV or Pipe Delimited).

Full support for both MySQL and Postgres environments.

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Interactive Data Table & DQ Report

Interactive data grid for all uploaded files.

On-the-fly descriptive statistics and data quality assessment.

Record-level view with filtering and sorting capabilities.

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SSO with OAuth

OAuth-based single sign-on (SSO) via Auth0 for - Google, LinkedIn, Microsoft, GitHub and Amazon.

Current Scope: The baseline implementation is linked to ‘Create DB’ function only. This provides unrestricted testing of analysis tools without forcing an app level login

This baseline setup is built for extension. It provides the OAuth foundation needed for full app authentication, fine-grained access controls, and row-level security in a live client project.

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App Variants

Custom GPT

Custom GPT connected to databases is a robust, effective solution - straightforward setup and low maintenance

Combines a front-end UI, built-in AI Agent, and the full native ChatGPT feature set

No separate API Cost for Agent + GPT-5 access

Efficient to connect automation backends and other apps via FastAPI/ n8n / Flowise / Make.com

This is my first choice

2. Flowise / n8n

Built-in user interfaces from Flowise and n8n.

Setup is efficient with direct connection to automation backends and other apps

API Charges - as per usage

3. Database AI Suite -4

Top choice where full feature and customization needed

Fully customisable : user interface as well as backend

Deploy anywhere, connect to Oauth

API Charges - as per usage

There are three stable variants of the app, each suitable for a different use case . The fourth, Voice AI, is experimental.

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3. Source Code

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Source Code

Hit Docs on the app page

Links to source codes and build guide, including video guides

All source codes links on app page in docs section

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Architecture

Modular architecture for efficient integration of automation services or backend

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Components Based Architecture

GitHub Repos | Description

Main App | The main application with the UI

FastAPI: Database Connector | Handles Text-to-SQL processing, including file uploads

FastAPI: Neon DB Creation | Temporary database creation with Neon

Flowise Agent Schemas | Sequential Agent Framework with LLM Agent built with Flowise AI

Proxy Server | For API Calls to OpenAI / Gemini / Openrouter

MCP Server - Markdown to PDF | For converting markdown to PDF

Quant Agent Backend Repos | The TIGZIG Quants Agent app integrated into a single tab

Mix and match deployment of individual tools

Connect components to your own user interface

Numerous more components available open source at www.tigzig.com - web scraper, pre-formatted slide deck creator, Excel table to PDF, Excel and Google Sheets updater, file converters, finance data extractors…

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Architecture Overview Doc

This is my personal app architecture file for DATS-4 that I feed to AI Coder at start of every session. Enables immediate productivity without full codebase exploration. Includes critical gotchas from earlier experiences.

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README

All GitHub repos with README with step-by-step guide

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TIGZIG: Micro-Apps for Analytics

25+ apps: Database AI / xlwings Lite / Automation / Quants

Amar Harolikar

Specialist - Decision Sciences & Applied AI

Builder of www.tigzig.com

www.tigzig.com

Access the full suite of open-source tools at