A CV parsing pipeline stops being a parser the moment a score sorts a list. Where Textract and Bedrock actually fit, why redacting names does nothing for bias, and what to document before anyone asks you to explain a ranking.
A confident answer with a citation from a matter the reader was walled off from. Nothing crashed, nothing alerted. Building legal document intelligence on AWS with Textract, Bedrock and OpenSearch is the easy half; the hard half is tenant isolation, verified deletion, S3 Object Lock holds, what leaves the account, and an audit trail that names the actual user.
Connecting Salesforce to Amazon Bedrock Knowledge Bases takes an afternoon. Making sure the assistant does not read people records they were never allowed to see takes longer. A practical walkthrough of the connector, the permission model it quietly drops, and the two architectures that actually hold up.
Textract rarely fails loudly. It returns a plausible result that is quietly incomplete: a truncated result set, a tick box read as an empty string, a clause split across a page break. A practitioner's guide to the failure modes that actually bite when you point Amazon Textract at contracts, technical reports and correspondence, plus how to choose between sync and async, which feature types are worth paying for, and where Textract stops being the right tool.
The demo works, then someone sees records they shouldn't. Nothing errors. Here's how to build a Salesforce AI assistant with Amazon Bedrock that respects your sharing model: which doors AWS and Salesforce just closed, why identity propagation is the failure mode that bites, and how to shape the tool surface so the agent can't wander.
Valid JSON is not correct data. A practical guide to extracting clauses, parties, dates and obligations from contracts with Amazon Bedrock: the two build paths, schema design, the three failure families that produce confident wrong answers, and the validation layer that catches them.
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.
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.
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.
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.