Case Study
Document Intelligence for a European Manufacturing Client Using Claude
Making 10,000+ technical documents queryable and actionable
We built a RAG-powered document intelligence system using AWS Bedrock and Claude that lets engineers query complex technical manuals in natural language — with automated gap detection between document versions and source-cited answers.
DELIVERED AT SCALE
10K+
Technical documents processed and indexed
85%
Faster information retrieval vs manual search
AWS Cloud
Deployed in the client's AWS account — fully managed, governed, and secure
Powered by AWS Bedrock and Claude — production-grade document intelligence
The Problem
Critical knowledge was buried in thousands of unqueryable documents
Engineers spent hours searching through dense technical manuals for maintenance procedures, compliance requirements, and spare parts information — with no automated way to detect when documentation had become outdated.
Unstructured Documents Across Legacy Systems
Over 10,000 technical documents — maintenance manuals, compliance specifications, engineering drawings — stored across disconnected systems with no unified search. Engineers relied on institutional memory and physical binders.
No Version Control or Gap Detection
When new versions of manuals were issued, there was no automated way to identify what had changed or flag procedures that were now outdated. Engineers could unknowingly follow superseded instructions — a compliance and safety risk.
No Scalable Search Across Documents
With thousands of documents scattered across systems, there was no way to run a single query across all of them. Engineers either knew where to look or spent hours searching manually — critical knowledge was effectively invisible.
The Solution
A Claude-powered RAG system that makes every document instantly queryable
We built a complete document intelligence pipeline on AWS — from raw PDF ingestion to natural language Q&A — using Bedrock for embeddings and Claude for reasoning, retrieval, and gap detection.
Intelligent Document Ingestion
PDFs processed using pdfplumber and OCR for scanned documents. Sections are chunked intelligently by document structure and embedded into a vector store — preserving document hierarchy and cross-references.
Automated Gap Detection
When a new document version is uploaded, Claude compares it against the previous version — identifying added, removed, and changed procedures. Gap reports are generated automatically, flagging compliance risks before they reach the shop floor.
Claude-Powered Q&A
Engineers ask questions in plain language — "What is the maintenance interval for component X?" Claude via AWS Bedrock retrieves the most relevant document sections, synthesizes an accurate answer, and cites the exact source pages — eliminating manual search entirely.
Deployed in the Client's AWS Account
The entire pipeline runs in the client's own AWS account — S3 for document storage, Bedrock for embeddings and Claude inference, OpenSearch for vector retrieval. Their data, their account, their control.
Tech Stack
Built on AWS Bedrock and Claude
Every component chosen for production reliability on AWS.
Why Datavent
Senior-led, production-first delivery
We don't hand you a report. We stay until it's in production — and we're accountable to the outcomes we define upfront.
Claude integration depth
We actively build with Claude in production — document Q&A, gap detection, and multi-agent workflows. We understand the deployment challenges from the inside.
Regulated industry depth
Proven delivery in pharma, manufacturing, and energy — the industries where data sovereignty, compliance, and documentation accuracy matter most.
Embedded, not outsourced
We work inside your team's tools — Jira, GitHub, Slack. We hire, train, and mentor engineers. We leave your team stronger than we found it.
AWS + AI expertise
We deploy production AI systems on AWS — Bedrock, OpenSearch, S3, and Claude — and deliver them into the client's own account so they own the infrastructure from day one.
Measured by outcomes
85% faster information retrieval. Deployed in the client's own AWS account. We define success metrics upfront and are accountable to them.
Talk to an Expert
We build and deploy RAG-powered document intelligence systems on AWS Bedrock and Claude — delivered into your own account so you own the infrastructure, the data, and the pipeline. Book a free session with one of our solution architects.