AI Enterprise Platform Development

We are building a next-generation Native AI Enterprise Platform that transforms raw enterprise data into AI-driven insights through a layered, governed, and secure architecture. Scope of Work 1. AI Framework Definition (Core Responsibility) Design and define a complete AI Framework covering: AI/ML/LLM lifecycle (MLOps + LLMOps) Agent lifecycle management Prompt lifecycle governance AI risk management and control framework Responsible AI principles and guardrails Ensure alignment with: ✅ ISO/IEC 42001 (AI Management System) ✅ SDAIA AI Ethics Principles ✅ NCA Cybersecurity Framework 2. Enterprise AI Architecture Design Define the end-to-end architecture across all layers: Data → Knowledge → Agents → Channels Define interactions between: Data platform (existing Astrom layers) Central knowledge layer (RAG-based) Multi-agent orchestration layer Business AI agents Define: Secure API architecture Governance boundaries Scalability patterns (cloud-native, distributed systems) 3. Multi-Agent Orchestration Framework Evaluation Evaluate and recommend the best-fit orchestration framework for enterprise use: Candidates: LangGraph Microsoft Semantic Kernel Microsoft AutoGen Langflow (if applicable) Evaluation Criteria: Enterprise readiness and scalability Security and governance capabilities Extensibility and modularity Support for human-in-the-loop workflows Integration with Azure ecosystem (preferred) Support for multi-agent collaboration and reasoning Deliver: Comparative analysis Recommendation with rationale Reference architecture based on selected framework 4. Centralized Knowledge Layer (RAG Architecture) Design a governed enterprise knowledge layer: Document ingestion, parsing, chunking Embedding generation and indexing Vector database design Semantic search and retrieval Metadata tagging and lineage Knowledge versioning and governance Fine-grained access control Technology considerations: Vector DBs (Azure AI Search, Pinecone, pgvector) Document processing (LangChain, Apache Tika) 5. Continuous Learning & AI Optimization Framework Design a closed-loop learning system: Human-in-the-loop feedback mechanisms Agent performance evaluation metrics Prompt engineering lifecycle and optimization Workflow optimization and tuning Reinforcement learning strategies based on outcomes Experiment tracking and model/version governance 6. MLOps & LLMOps Design Define production-ready pipelines for: Model training, testing, deployment LLM lifecycle (prompt versioning, evaluation, monitoring) CI/CD for AI models and agents Monitoring: Model drift Prompt degradation Agent performance KPIs Observability and logging strategy 7. Governance, Risk & Compliance (GRC) Define AI governance aligned with: ISO 42001 structure: AI policy Risk assessment Controls and auditability SDAIA + NCA requirements: Data privacy controls Security architecture Compliance monitoring Include: Explainability requirements Bias monitoring Audit trails for decisions and agents 8. Business AI Agents Design Standards Define reusable standards for: Agent roles and responsibilities Instruction and prompt templates Tool integrations (via APIs) Knowledge layer usage (no siloed knowledge) Governance, permissions, and guardrails Key Deliverables ✅ Enterprise AI Architecture Blueprint ✅ ISO 42001-aligned AI Framework ✅ Multi-Agent Framework Evaluation Report ✅ RAG Knowledge Layer Architecture ✅ MLOps / LLMOps Design ✅ Reference Architecture for Agentic Systems ✅ Implementation roadmap