Enterprise AI Module for SaaS Application
Budget / Salary$250–750
TypeFreelance project
LocationRemote
Posted1 hour ago
# Senior AI Engineer – Enterprise AI Intelligence Module
## Project Overview
We are looking for a **Senior AI Engineer / AI Architect** to develop a production-ready AI intelligence module for an existing enterprise SaaS application.
This is **not a basic chatbot project**.
The objective is to build an AI-powered **Digital Quality & Operations Manager** that can securely analyze each organization's documents, workflows, records, and operational data and provide actionable insights, recommendations, alerts, and executive reports.
## Core Requirements
### 1. Organization-Specific AI
The AI must maintain completely isolated knowledge and data contexts for each organization/tenant.
It must never mix information between different organizations.
### 2. RAG & Knowledge Intelligence
Build a production-grade RAG architecture supporting:
* Documents
* PDFs
* Policies
* Procedures
* Forms
* Operational records
* Structured database data
Required capabilities:
* Semantic search
* Metadata filtering
* Hybrid search where appropriate
* Document chunking
* Embeddings
* Source citations
* Knowledge versioning
* Context-aware retrieval
### 3. AI Data Analysis
The AI should analyze operational modules such as:
* Nonconformities
* Corrective/Preventive Actions
* Customer Complaints
* Supplier Evaluations
* Training
* Audits
* Risk Management
* Meetings & Action Items
* Maintenance & Calibration
* Documents and Procedures
It should identify:
* Trends
* Repeated issues
* Root-cause patterns
* Compliance gaps
* Emerging risks
* Overdue actions
* Performance problems
* Recommended actions
### 4. Daily Intelligence Report
The system should automatically generate a daily executive report containing:
* Overall status
* Critical issues
* New risks
* Repeated problems
* Overdue actions
* Performance trends
* Priority recommendations
### 5. Embedded AI Copilot
Users should be able to interact with the AI directly inside the application.
Examples:
> Analyze this procedure.
> Identify recurring problems.
> Show high-risk suppliers.
> Explain why complaints increased.
> Summarize today's activities.
> Recommend corrective actions.
> Generate a CAPA based on this issue.
### 6. AI Agents & Tool Calling
Use a **modular agent architecture**, rather than one large monolithic agent.
The system should support specialized agents such as:
* Document Intelligence Agent
* Compliance Agent
* Risk Agent
* CAPA/NCR Agent
* Audit Agent
* Executive Intelligence Agent
Agents should securely call application APIs/tools to retrieve real-time data.
## Recommended Architecture
The AI layer should be implemented as an **independent AI service/microservice** connected to the existing application through secure APIs.
Preferred technologies:
* Python
* FastAPI
* OpenAI API
* LangGraph / LangChain
* Qdrant / Pinecone
* Redis
* PostgreSQL or existing database integration
* Docker
The architecture must allow future replacement of AI models/providers without rebuilding the entire system.
## Security
Required:
* Strict multi-tenant isolation
* Role-based access control
* Secure API authentication
* Audit logging
* Data protection
* No cross-organization data exposure
* Human approval before critical actions
* AI must not modify important records without authorization
## MVP – 4 to 6 Weeks
The initial MVP should include:
1. Multi-tenant RAG
2. Document intelligence
3. AI Copilot
4. Operational data analysis
5. Daily executive intelligence report
6. Secure API integration
7. Arabic & English support
## Deliverables
* Production-ready source code
* AI microservice
* API integration
* RAG pipeline
* Agent architecture
* Documentation
* Docker/deployment setup
* Testing
* Production deployment
* Technical handover
## Required Experience
Please provide examples of real production projects involving:
* RAG
* AI Agents
* LangGraph / LangChain
* OpenAI
* Vector databases
* Tool/function calling
* Enterprise SaaS
* Multi-tenant AI systems
**Please do not apply if your experience is limited to basic ChatGPT integrations or simple chatbot projects.**
We are looking for a long-term technical partner for the AI development and expansion of this system.
## Project Overview
We are looking for a **Senior AI Engineer / AI Architect** to develop a production-ready AI intelligence module for an existing enterprise SaaS application.
This is **not a basic chatbot project**.
The objective is to build an AI-powered **Digital Quality & Operations Manager** that can securely analyze each organization's documents, workflows, records, and operational data and provide actionable insights, recommendations, alerts, and executive reports.
## Core Requirements
### 1. Organization-Specific AI
The AI must maintain completely isolated knowledge and data contexts for each organization/tenant.
It must never mix information between different organizations.
### 2. RAG & Knowledge Intelligence
Build a production-grade RAG architecture supporting:
* Documents
* PDFs
* Policies
* Procedures
* Forms
* Operational records
* Structured database data
Required capabilities:
* Semantic search
* Metadata filtering
* Hybrid search where appropriate
* Document chunking
* Embeddings
* Source citations
* Knowledge versioning
* Context-aware retrieval
### 3. AI Data Analysis
The AI should analyze operational modules such as:
* Nonconformities
* Corrective/Preventive Actions
* Customer Complaints
* Supplier Evaluations
* Training
* Audits
* Risk Management
* Meetings & Action Items
* Maintenance & Calibration
* Documents and Procedures
It should identify:
* Trends
* Repeated issues
* Root-cause patterns
* Compliance gaps
* Emerging risks
* Overdue actions
* Performance problems
* Recommended actions
### 4. Daily Intelligence Report
The system should automatically generate a daily executive report containing:
* Overall status
* Critical issues
* New risks
* Repeated problems
* Overdue actions
* Performance trends
* Priority recommendations
### 5. Embedded AI Copilot
Users should be able to interact with the AI directly inside the application.
Examples:
> Analyze this procedure.
> Identify recurring problems.
> Show high-risk suppliers.
> Explain why complaints increased.
> Summarize today's activities.
> Recommend corrective actions.
> Generate a CAPA based on this issue.
### 6. AI Agents & Tool Calling
Use a **modular agent architecture**, rather than one large monolithic agent.
The system should support specialized agents such as:
* Document Intelligence Agent
* Compliance Agent
* Risk Agent
* CAPA/NCR Agent
* Audit Agent
* Executive Intelligence Agent
Agents should securely call application APIs/tools to retrieve real-time data.
## Recommended Architecture
The AI layer should be implemented as an **independent AI service/microservice** connected to the existing application through secure APIs.
Preferred technologies:
* Python
* FastAPI
* OpenAI API
* LangGraph / LangChain
* Qdrant / Pinecone
* Redis
* PostgreSQL or existing database integration
* Docker
The architecture must allow future replacement of AI models/providers without rebuilding the entire system.
## Security
Required:
* Strict multi-tenant isolation
* Role-based access control
* Secure API authentication
* Audit logging
* Data protection
* No cross-organization data exposure
* Human approval before critical actions
* AI must not modify important records without authorization
## MVP – 4 to 6 Weeks
The initial MVP should include:
1. Multi-tenant RAG
2. Document intelligence
3. AI Copilot
4. Operational data analysis
5. Daily executive intelligence report
6. Secure API integration
7. Arabic & English support
## Deliverables
* Production-ready source code
* AI microservice
* API integration
* RAG pipeline
* Agent architecture
* Documentation
* Docker/deployment setup
* Testing
* Production deployment
* Technical handover
## Required Experience
Please provide examples of real production projects involving:
* RAG
* AI Agents
* LangGraph / LangChain
* OpenAI
* Vector databases
* Tool/function calling
* Enterprise SaaS
* Multi-tenant AI systems
**Please do not apply if your experience is limited to basic ChatGPT integrations or simple chatbot projects.**
We are looking for a long-term technical partner for the AI development and expansion of this system.
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