Generative AI | John Nessime

Agentforce and AWS: Where the Trust Layer Stops and Your Logs Begin

Post category:Cloud Computing DevOps Technical Guides

Agentforce and AWS wire together in four standard patterns, and every one of them has a point where Salesforce's guarantees stop and yours start. This traces a single request across each boundary it crosses, covers the Trust Layer default most write-ups get wrong (LLM data masking is disabled for agents), and sets out what changes the moment a callout lands in your own account: retention, audit trail, and user identity that does not travel.

It Counted the Chunks: Preparing CRM Data for Amazon Bedrock

Post category:Cloud Computing DevOps Technical Guides

Asked how many open opportunities an account had, the assistant said five. It had nineteen. Retrieval returned five chunks and the model counted them. CRM data is structured and RAG is built for text, so the preparation work is mostly deciding which questions are retrieval and which are queries.

Building an AI Construction Claims Platform on AWS That Holds Up Under Scrutiny

Post category:Case Studies Cloud Computing Technical Guides

Semantic search finds the most persuasive document, not the earliest one. Here is how to architect an AI construction claims and dispute intelligence platform on AWS so retrieval respects the contractual clock, schedule data stays out of the vector index, every answer resolves to a page, and privileged material never shares a retrieval path with project records.

Build a Secure AI Medical Assistant on AWS: The Boundaries That Actually Leak

Post category:Case Studies Cloud Computing DevOps Technical Guides

A practical architecture for a secure AI medical assistant on AWS, organised by the boundary the data crosses: the input box, your own invocation logs, cross-Region inference routing, the retrieval index, and clinical accuracy. Includes real commands, the failure modes that stay invisible until an audit, and the trade-offs worth knowing before you build.