Enterprise Multi-Agentic AI Platform for Unified Knowledge Access and Decision Support

Executive Summary

Large enterprises engaged atQor to design and deploy a Multi-Agentic AI Platform that unifies organizational knowledge, automates document intelligence, and enables domain-specific AI agents to work collaboratively across departments.

Traditional enterprise knowledge environments were fragmented across systems, departments, and document formats—forcing users to rely on manual searches, siloed tools, and inconsistent access controls. atQor delivered a secure, Azure-native, multi-agent AI architecture that enables intelligent, role-based access to enterprise knowledge through a single conversational interface, dramatically improving data retrieval efficiency and cross-functional collaboration.

The platform has been successfully implemented across multiple enterprises to support Legal, HR, Manufacturing, and Information Security teams at scale.

The solution was delivered for large, diversified enterprise organizations operating across regulated and operationally complex environments such as pharmaceuticals, manufacturing, engineering, and industrial services.

These organizations:

Operate across multiple business units and domains
Manage large volumes of unstructured and semi-structured documents
Require strict role-based access control and auditability
Support geographically distributed users and frontline teams

They required a secure, scalable AI platform capable of supporting multiple domain-specific AI agents without creating new silos.

Industry Cross-Industry (Pharmaceuticals, Manufacturing, Engineering, Industrials)
Region Canada

Business Challenges

Common challenges addressed across customers included:

Secure Authentication & Data Segregation
Ensuring department-level and role-based access across sensitive documents.
High-Volume, Event-Driven Document Ingestion
Real-time handling of document uploads, updates, and metadata changes.
Complex Document Structures
Extracting structured data, tables, and contextual meaning from varied document formats.
Siloed Domain Knowledge
Legal, HR, Manufacturing, and Operations teams operated in disconnected systems.
Lack of Continuous Learning & Auditability
No centralized chat history or feedback loop to improve AI responses over time.
External Channel Integration
Need to securely extend enterprise AI access to channels such as WhatsApp.

atQor’s Multi-Agentic AI Platform

atQor designed and implemented an Azure-native Multi-Agentic AI Platform that orchestrates multiple specialized AI agents through a single, secure conversational interface.

Core Architecture Principles

Zero-trust, identity-first design
Event-driven ingestion and processing
Retrieval-Augmented Generation (RAG)
Domain-specific AI agents with centralized orchestration
Enterprise-grade security, compliance, and auditability

Architecture & Implementation Highlights

Identity-Driven Access & Security

Integrated with Microsoft Entra ID for authentication and authorization
Automatic mapping of users to departments, roles, and group memberships
RBAC-driven document access aligned to enterprise policies

Event-Driven Document Intelligence

Real-time document ingestion using Azure Event Grid and Azure Functions
Automated processing of uploads, updates, and metadata changes
Policy-driven enforcement without manual intervention

AI-Powered Document Understanding

Document extraction using Azure AI Document Intelligence and GPT Vision
Structured data, tables, and context extracted from diverse formats
Content enriched and indexed in Azure AI Search for RAG-based querying

Multi-Agent Orchestration

Semantic Kernel orchestrates specialized AI agents across domains:
- Legal Agent
- HR Agent
- Manufacturing / SOP Agent
Intelligent routing of user queries to the relevant agent based on context and intent

Conversational Enterprise Interface

Unified chat interface for natural-language interaction
Users can upload documents, ask questions, and execute domain-specific tasks
OAuth token attributes guide agent selection and response context

Chat History, Feedback & Continuous Learning

Chat histories stored in Azure Cosmos DB
Search and retrieval backed by Azure AI Search
Feedback loops enable continuous refinement and personalization of AI responses

Secure WhatsApp Integration

Extended AI access to WhatsApp for mobile and frontline users
Secure enterprise integration without exposing internal systems
Enables on-the-go access to domain knowledge and document insights

Business Outcomes & Value Delivered

Organizations using the platform achieved:

Unified Knowledge Access
Single conversational interface across departments and systems.
Reduced Manual Search & Effort
Faster access to accurate, contextual information.
Improved Cross-Functional Collaboration
Legal, HR, Manufacturing, and IT agents working cohesively.
Stronger Security & Auditability
Identity-driven access, chat history retention, and traceable interactions.
Scalable AI Adoption
Platform supports new domains and agents without re-architecture.