AI Systems for
Business Data,
Knowledge & Operations
We build intelligent AI systems that help companies analyze data, automate workflows and unlock insights.
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 AI Systems
End-to-end AI systems built and deployed for real business needs.
AI Analyst
Live DemoAI-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.
Tech Stack
IntelOps AI
Live DemoAI 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.
Tech Stack
How Our AI Systems Work
From problem to architecture — a look inside what we build and how.
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 teams now query live business data in seconds — no SQL, no analyst dependency. What took 30+ minutes of manual reporting is fully automated, freeing the data team to focus on strategy instead of repetitive requests.
User Query
Natural language input
LangChain Agent
Intent routing
SQL Generator
NL → SQL conversion
Neon PostgreSQL
Data retrieval
Plotly Charts
Visual output
GPT-4o
Insight generation
Architecture overview · Detailed diagram in project documentation
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
Instead of switching between tools and documents, teams now interact with a single AI system that understands both knowledge and data - turning scattered information into an accessible, reliable decision layer.
User Query
Role-based input
Router Agent
Intent classification
RAG Agent
ChromaDB retrieval
SQL Agent
PostgreSQL analytics
Report Agent
PDF generation
GPT-4o
Final response
Architecture overview · Detailed diagram in project documentation
Technologies We Use
A focused stack of proven technologies — from rapid prototypes to custom production frontends and enterprise AI backends.
Language
Python
Core language for all AI system development
Framework
LangChain
AI agent orchestration and chain management
Framework
LangGraph
Stateful multi-agent workflows and graph execution
AI Model
OpenAI API
GPT-4o models for reasoning and generation
AI Model
Anthropic Claude
Claude models for advanced reasoning and analysis
Database
PostgreSQL
Structured data storage and SQL analytics
Database
Neon Database
Serverless PostgreSQL with branching and scaling
Vector DB
ChromaDB
Vector database for semantic search and RAG
Architecture
RAG Systems
Retrieval-augmented generation for knowledge bases
Architecture
AI Agents
Autonomous multi-step AI reasoning systems
Backend
FastAPI
High-performance async backend for AI APIs
Frontend
React + Next.js
Custom production-grade web interfaces
Prototyping
Streamlit
Rapid AI prototype and demo deployment
Visualization
Plotly
Interactive data visualizations and charts
How We Build AI Systems
A structured, collaborative process from discovery to production deployment.
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.
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.
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.
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.
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.
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.
Frequently Asked Questions
Everything you need to know before starting a project with us.
Still have questions?
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Eligent AI
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Now Accepting New Projects
2 project slots open for Q2 2026 · Small businesses · Startups · Enterprises
What happens next?
We review your project
Within 12 hours of receiving your message
We schedule a free call
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We send a proposal
Clear scope, timeline & delivery plan