Information governance for AI.
AI answers from your files, your email, and your records. How clean, labeled, and well-controlled those are decides whether AI is safe and accurate. These guides explain what to check and fix, in plain language.
Start with the readiness checks →Every guide, in one place.
Before you connect AI
How AI exposes data nobody labeled
Why unlabeled, over-shared files turn into exposed answers, and what to fix first.
Questions to ask every AI vendor before you sign
Training, data location, access, deletion, and logs, plus why answers belong in the contract.
How many documents to review to trust AI classification
Confidence thresholds, human review, and the sample size that gives a defensible answer.
Files and email
Legal and retention
Work with Horus
AWS AI assessment
Decide which AI projects are worth building, with a roadmap, architecture, ROI model, and security review.
Intelligent document processing
Extract, classify, and route documents from email, SharePoint, scans, and paper, with a human on decisions that count.
Generative AI consulting
Private, citation-grounded AI on Amazon Bedrock, inside your own AWS account.
Not sure where to begin?
Tell us which systems you want to connect to AI, and we will tell you what to check first. The guides here are general information and not legal advice.
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