We've been having a lot of fun testing AI recently in a brand and web projects.

I'm a big believer in everything we produce is human first and AI assisted to help spin up ideas of enhance our digital presence. Behind the scenes there isn't anything we can't achieve in digital now. With Claude 5.5, An initial brief and just a few prompts later on a logo animation and I can visualise what I want for my animators and not just have to talk them through a few slides. Most will say do we even need to improve what claude spits out, but I notice the little things and the little things are why we are always busy. But this unlock has huge potential for us and our clients.

Love the little petals shifting like they're saying hi. Lovely work, Alex!

Enterprise RAG AI: Private Doc Intelligence A fast-growing B2B software firm needed a secure, enterprise-grade Retrieval-Augmented Generation (RAG) system to query thousands of confidential internal documents (legal contracts, technical architecture specs, Notion pages, and employee SOPs) in real time without third-party data leakage.

Key Challenges:

High hallucination risk and irrelevant context retrieval using standard keyword search.

Managing multi-format files (scanned PDFs, tables, markdown) with strict access control based on user hierarchy.

Technical Implementation & Solution: • Engineered an automated ingestion pipeline in Python with LlamaIndex and LangChain for semantic chunking and metadata preservation. • Integrated a hybrid vector search architecture pairing dense embeddings (OpenAI text-embedding-3) with sparse BM25 indexing in Qdrant. • Added a cross-encoder re-ranking layer (Cohere Re-ranker) to prune low-relevance chunks, reducing API token costs by 40%. • Built robust Role-Based Access Control (RBAC) via FastAPI endpoints, ensuring departmental data isolation.

Results & Impact: • Dropped internal information retrieval time from 20 minutes to under 4 seconds. • Attained 98% factual precision with traceable in-line page citations. • Full on-prem Docker deployment ensuring 100% private data security.

I'd test revoking a user's document access mid-conversation. In your RBAC flow, do cached retrieval results get rechecked against current permissions before they're sent to the reranker or model?