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CertifAI Labs: Industrial RAG Internship Program

 

CertifAI Labs: Industrial AI RAG Internship Program

Building AI Resilience for Industry 4.0

This is not a traditional academic course. This is a hands-on, project-based internship designed to transform engineers into AI Architects. Participants will work through the lifecycle of an industrial-grade RAG system, applying it to real-world datasets like technical manuals, switchgear specifications, and safety protocols.

Program Structure & Modules

Week 1: Foundations & The Data Fabric

Goal: Master the art of "Ingestion" for complex industrial data.

  • Module 1: Advanced Data Ingestion & Refinement

    • Processing unstructured PDF manuals, CAD metadata, and technical tables.

    • Implementing Vision-RAG for analyzing industrial diagrams and P&IDs.

  • Module 2: The Vector Ecosystem

    • Selecting embedding models for technical domain-specific language.

    • Hands-on with Vector Databases (Milvus/Chroma) and metadata filtering.

Week 2: Precision Engineering for Retrieval

Goal: Move beyond simple search to "High-Fidelity" context retrieval.

  • Module 3: Hybrid & Semantic Search

    • Combining keyword matching for part numbers with semantic search for concepts.

  • Module 4: Re-Ranking & Context Compression

    • Using Cross-Encoders to filter "noise" from retrieved results.

    • Implementing Long-Context strategies to handle massive technical documents.

Week 3: Advanced Architectures & Agency

Goal: Building systems that can "think" and "act."

  • Module 5: Agentic RAG & Multi-Hop Reasoning

    • Building agents that can query multiple sources to answer complex questions (e.g., "Compare the maintenance schedule of Building A vs Building B").

  • Module 6: Hierarchical & Knowledge Graph RAG

    • Mapping relationships between industrial assets using Graph Databases to improve context.

Week 4: The Production Frontier

Goal: Hardening the system for industrial deployment.

  • Module 7: Evaluation (The RAGAS Framework)

    • Automated testing for Faithfulness (no hallucinations) and Relevance.

    • Red-teaming the system for safety and security.

  • Module 8: MLOps & Industrial Integration

    • Implementing Semantic Caching for latency reduction.

    • Monitoring, tracing, and cost-optimization for enterprise scale.

What You Will Gain from this Internship

  • Design End-to-End Scalable Pipelines: Learn to build RAG systems that don't just work in a notebook, but scale to 17-building projects and thousands of technical documents.

  • Master Retrieval Quality: Gain the skills to implement Hybrid Search and Re-ranking, ensuring the AI never misses a critical technical detail.

  • Navigate Real-World Challenges: Tackle the "Three Pillars of Production": Latency, Cost Optimization, and Scale.

  • Explore Frontier Architectures: Build Agentic and Multi-hop systems that mimic the reasoning of a senior lead engineer.

  • Validate with Rigor: Learn how to use frameworks like RAGAS to provide a "Certification of Accuracy" for every AI response.

  • Production-Ready Portfolio: Build a modular system complete with caching, monitoring, and safety guardrails, ready for deployment in an industrial environment.

The Capstone Project

Interns will be tasked with building a "Digital Technical Assistant".

Scenario: Create a system that retrieves information from the SAX 55-7 / SS-18 Switchgear technical scope. The assistant must provide exact wiring instructions, safety protocols, and cross-reference multiple vendor manuals without hallucinating a single specification.


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