AI Engineering Studio

AI Systems for
Business Data,
Knowledge & Operations

We build intelligent AI systems that help companies analyze data, automate workflows and unlock insights.

2+Production Systems
9+Technologies
100%End-to-End Delivery
What We Build

AI Systems We Build

Focused engineering on intelligent systems that connect AI reasoning with real business data.

AI Analytics Dashboards

Interactive analytics systems where users query business data using natural language. The AI automatically generates SQL, visualizes results, and surfaces insights.

AI Knowledge Assistants

RAG-powered systems that let teams search and retrieve information from company documents, wikis, and internal knowledge bases through natural conversation.

AI Customer Support Automation

Intelligent support systems that understand customer inquiries, retrieve relevant knowledge, and generate accurate responses — reducing manual support load.

AI Business Process Automation

End-to-end workflow automation that connects AI reasoning with your business data, triggering actions, generating reports, and surfacing anomalies automatically.

Custom AI Agents

Purpose-built AI agents with tool-use capabilities — able to query databases, call APIs, analyze documents, and execute multi-step reasoning tasks autonomously.

Our Work

Our AI Systems

End-to-end AI systems built and deployed for real business needs.

ProductionBusiness Intelligence AI

AI Analyst

Live Demo

AI-powered business analytics system that allows users to ask questions about business data in natural language. The system automatically generates SQL queries, retrieves data, creates charts, and provides AI-driven insights.

Natural language → SQL queries
Automated business insights
Interactive charts & dashboards
Business analytics reporting

Tech Stack

PythonLangChainOpenAI APIPostgreSQLNeon DBPlotlyStreamlit
AI Analyst screenshot
ProductionInternal Knowledge Assistant

IntelOps AI

Live Demo

AI internal company assistant designed to help teams retrieve knowledge, analyze data and generate reports. The system uses RAG architecture and SQL agents to access company knowledge and structured data.

Knowledge retrieval from documents
Business analytics queries
Role-based access system
AI-generated reports

Tech Stack

PythonLangChainOpenAI APIPostgreSQLNeon DBRAG ArchitectureStreamlit
IntelOps AI screenshot
Deep Dives

How Our AI Systems Work

From problem to architecture — a look inside what we build and how.

AI AnalystBusiness Intelligence

Problem

Business analysts spent hours writing SQL queries and building charts manually. Non-technical stakeholders couldn't access data insights without developer support, creating bottlenecks in decision-making.

Solution

A natural language interface connected to a PostgreSQL analytics database. Users type questions in plain English — the AI generates SQL, runs the query, and returns charts and written insights automatically.

Architecture

LangChain SQL agent with OpenAI GPT-4 for query generation. Streamlit frontend for the chat interface. Plotly for dynamic chart rendering. Neon Database for scalable serverless PostgreSQL.

Tech Stack

Python · LangChain · OpenAI API (GPT-4) · PostgreSQL · Neon Database · Plotly · Streamlit

Outcome

Non-technical team members can now self-serve data queries without engineering support. Reports that previously required manual work are generated in seconds through conversational queries.

User Input
LangChain Agent
LLM + Tools
Database / Docs
Response
Architecture overview · Detailed diagram in project documentation
IntelOps AIInternal Knowledge System

Problem

Company knowledge was scattered across documents, wikis, and databases. Employees lost significant time searching for information and generating reports manually from disparate sources.

Solution

A unified AI assistant combining RAG for unstructured documents and a SQL agent for structured data. Team members ask questions and receive accurate, sourced answers and auto-generated reports.

Architecture

RAG pipeline with vector embeddings for document retrieval. LangChain agents orchestrate between document search and database queries. Role-based permissions control data access per user.

Tech Stack

Python · LangChain · OpenAI API · PostgreSQL · Neon Database · RAG Architecture · Streamlit

Outcome

Teams retrieve information and generate reports through conversation instead of manual search. Knowledge silos are broken down through a single unified AI interface with appropriate access controls.

User Input
LangChain Agent
LLM + Tools
Database / Docs
Response
Architecture overview · Detailed diagram in project documentation
Our Stack

Technologies We Use

A focused stack of proven technologies for building production AI systems.

🐍

Language

Python

Core language for all AI system development

Framework

LangChain

AI agent orchestration and chain management

AI

OpenAI API

GPT-4 models for reasoning and generation

🐘

Database

PostgreSQL

Structured data storage and SQL analytics

Database

Neon Database

Serverless PostgreSQL with branching & scaling

📚

Architecture

RAG Systems

Retrieval-augmented generation for knowledge bases

🤖

Architecture

AI Agents

Autonomous multi-step AI reasoning systems

Frontend

Streamlit

Rapid AI application frontend deployment

📊

Visualization

Plotly

Interactive data visualizations and charts

Our Process

How We Build AI Systems

A structured, collaborative process from discovery to production deployment.

01

Problem Discovery

We start by understanding your business processes, data sources, and where AI can have the most meaningful impact. No generic pitches — just a focused analysis of your actual needs.

02

System Architecture Design

We design the AI architecture before writing a single line of code. This includes agent design, data flow, RAG pipeline structure, database schema, and integration points.

03

AI Development

Building the AI core — LangChain agents, RAG pipelines, SQL tools, and LLM integrations. Every component is built for reliability, with proper error handling and prompt engineering.

04

Integration with Business Data

Connecting the AI system to your actual data sources — databases, documents, APIs. We set up vector stores, SQL connectors, and access controls so the AI works with real information.

05

Deployment

Deploying the system to production with monitoring, documentation, and handoff. We ensure the system is stable, performant, and maintainable before considering the project complete.

Who We Are

About Eligent AI

Eligent AI is a specialized AI engineering studio focused on building intelligent systems for modern businesses.

We specialize in AI agents, RAG systems, data analytics automation and AI-powered knowledge assistants.

Our mission is to help companies transform their data and knowledge into intelligent systems — practical tools that work reliably in production.

Focused Execution

We work on a small number of AI projects at a time to deliver focused, high-quality engineering — not rushed deliverables.

Engineering First

Every AI system we build starts with rigorous architecture design. We believe robust foundations produce reliable AI systems.

AI That Works

Our goal is systems that actually run in production. We build for reliability, not just demos — every project is designed to last.

Get in Touch

Start Building with
Eligent AI

Have a project in mind or want to understand what AI could do for your business? Reach out and we'll get back to you quickly.