Real work on AWS, intelligent document processing, and generative AI. Case studies with the numbers, plus implementation guides from the engineers who shipped them.
AI searches small pieces of your files. If a piece loses its label and permissions, anyone can be shown it. How to label, filter, log and test on AWS.
Read the AI access guide →AI is being added to tools you already own. Here are the questions to ask about training, data location, access, and deletion, and why answers belong in the contract.
Read the AI vendor questions guide →A confidence score is not accuracy. How thresholds, human review, and a sample of about 385 documents tell you whether AI classification can be trusted.
Read the AI classification accuracy guide →Deleting duplicates does not last if a business process keeps making them. How to find the cause, fix it at the source, and then clean up.
Read the duplicate files guide →AI does not break your security. It amplifies the labeling and sharing mistakes you already had. Here is how, and what to fix first.
Read the AI data exposure guide →Contracts and decisions sit in individual Outlook inboxes. A practical guide to filing the email that matters into a shared, governed library.
Read the email filing guide →A practical checklist to prepare data, legal holds, and ownership before litigation arrives, including chat apps and AI prompts.
Read the e-discovery readiness checklist →Old drafts, duplicates, and expired policies make AI answer wrongly. Retention schedules are now an AI accuracy tool, not just a compliance one.
Read the retention and AI accuracy guide →We built a private AI opinion tool in a law firm's own AWS account. Every fact it states cites a source document, and anything missing shows as a placeholder.
Read the law firm AI case study →We moved a healthtech platform from Supabase, Clerk, and Vercel into one HIPAA-eligible AWS account. All 36 tables reconciled with zero mismatches at cutover.
Read the healthtech migration case study →We built OneVolume for Tennyson Systems on AWS. Claude reads each software contract in 7.2 seconds, where an analyst used to spend 15 to 30 minutes.
Read the Tennyson Systems case study →We rebuilt a regulated U.S. defense mission platform on AWS GovCloud as 100% infrastructure-as-code with Amazon EKS, AWS CDK, and NIST 800-53 as code, with zero static CI keys.
Read the GovCloud EKS modernization case study →Bank of Montreal automated handwritten loan application processing using Amazon Bedrock, achieving 92% handwriting recognition accuracy, $2.1M in annual savings, and 40% cost reduction.
Read the BMO loan automation case study →Horus Technology improved a course-catalog pipeline with infrastructure as code, queued admission, distributed workflows, and concurrency tuning.
Read the course catalog automation case study →Complete guide to implementing AWS Textract for document processing. Learn architecture patterns, best practices, and real-world tips from a team led by former AWS engineers.
Read the AWS Textract implementation guide →Complete breakdown of IDP costs in 2025. AWS Textract pricing, implementation costs, and ROI calculations with real client examples.
Read the IDP cost breakdown →We helped a vital-records provider build a GenAI–powered document processing platform for healthcare identity documents, with reviewer approval built in.
Read the healthcare document processing case study →How we built a contract-intelligence platform on AWS that uses Amazon Bedrock to extract structured, source-linked data from contract documents.
Read the contract review case study →How Punt migrated from DigitalOcean to AWS, overhauled their PHP Laravel backend, and used AWS MAP credits to offset the cost of the migration.
Read the Punt AWS migration case study →We helped a virtual mailbox provider replace manual mail processing with a Generative AI pipeline built on Amazon Bedrock.
Read the virtual mailbox automation case study →Get started
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