NajmAI

Enterprise Vector RAG & Intelligent Knowledge Discovery

Semantic Search

14k Docs

Input Ingestion

Continuous asynchronous stream processing with schema guardrails.

LLM Reasoning

Semantic parsing with context retrieval and safety filters.

Transactional Sync

Sub-second database sync with multi-region replication.

Latency: ~180ms99.98% Autonomous Accuracy

Real-time sync

14,000+

Internal Documents Indexed

Hybrid vector search

<300ms

Search Latency

Zero hallucination

99.9%

Citation Accuracy

Daily users

5,000+

Active Knowledge Workers

The Challenge

Enterprise teams were losing hundreds of hours every quarter searching for critical institutional data locked across disparate silos—Slack, Notion, internal PDFs, Jira, and relational SQL databases. Critical decisions were delayed due to information opacity.

The Engineering Solution

Built a secure multi-tenant Vector RAG knowledge assistant with document chunking, sub-second hybrid dense-sparse vector search, dynamic permission filters, and strict citation tracing down to paragraph and timestamp.

Key Impact & Transformation

Empowered over 5,000 knowledge workers to discover answers in under 300ms, reduced repetitive internal queries by 64%, and maintained 100% compliance with strict zero-training data retention policies.

Project Architecture & Execution

NajmAI bridges the gap between siloed enterprise knowledge repositories and frontline team execution with sub-second semantic retrieval.

By indexing documents across Slack, Notion, Jira, and internal relational databases, NajmAI provides accurate, source-grounded answers to complex organizational questions.

Advanced hallucination guardrails and dynamic confidence scoring ensure high-stakes decisions are backed by verifiable evidence.

Tailored user permissions reflect existing corporate hierarchies, ensuring sensitive financial and HR records remain strictly compartmentalized.

Backed by SOC-2 Type II standards, NajmAI empowers over 5,000 knowledge workers daily with zero training data leakage.

“Finding contracts, engineering specs, and compliance records used to take hours of hunting. With NajmAI, our whole team gets verified answers with source citations in under a second.”

Tariq Al-Mansoor

Chief Technology Officer • Najm Enterprise

Project Overview

Live in Production

CategoryEnterprise Systems

Primary Impact14,000+ (Real-time sync)

ArchitectureFull-Stack & AI

Build a Similar System

Project Stack

8 Technologies

Design

Figma• Search Experience & Citation CardsUI/UX Design• Knowledge Architecture & Hierarchy

Frontend

Next.js• Enterprise Knowledge WorkspaceReact• Streaming Answers with Citations

Backend

Python• Document Parsing & ChunkingFastAPI• Vector Search Microservice

AI / ML

OpenAI• Text Embeddings & GenerationPinecone• Sub-300ms Hybrid Vector DB

Tags & Disciplines

Next.jsReactTypeScriptFigmaUI/UX DesignFrontend DevelopmentBackend DevelopmentPythonFastAPIOpenAIPineconeVector DBEnterprise Search

Related Work & Solutions

All Case Studies

Support Hub

Live

Autonomous Resolution

24/7 Tier-1 Auto Resolution

Latency: 180ms99.4% CSAT

Intelligent Support Hub

Next.js · React · TypeScript · Figma · UI/UX Design · Frontend Development · Backend Development · FastAPI · Python · OpenAI · AI & Machine Learning · Mobile App

Workflow Pipeline

Active

Ingest

Webhook

Parser

LLM Node

Sync

DB Cluster

Success: 99.98%Zero Human Overhead

Business Automation System

Next.js · React · TypeScript · Figma · UI/UX Design · Frontend Development · Backend Development · FastAPI · Python · PostgreSQL · Kafka · Docker · Workflow Automation

Mobile SaaS Engine

42ms Edge

Subscribers

48,290

Latency

42ms

Cross-platform iOS & AndroidOffline-first

Mobile SaaS Platform

React Native · Next.js · React · TypeScript · Figma · UI/UX Design · Frontend Development · Mobile Development · Backend Development · Cloudflare · SQLite · CRDT