76k documents searchable in 5 seconds: Industrial equipment
AI in Manufacturing Use Case
An industrial equipment company had decades of technical documentation locked in PDFs and images, needing to transform the repository into an accessible, searchable asset. This created knowledge silos, customer delays, and more. The solution is a “machine guru”, reducing search time by making every document queryable through natural language.
.jpg)
The business problem: Inaccessible knowledge
This industrial equipment company had knowledge hiding in plain sight. Over decades of operation, they had accumulated 76,000+ documents - equipment manuals, specification sheets, product catalogs, maintenance guides, customer quotes. The extraordinary repository of technical expertise was practically inaccessible.
The pain points were:
- Search time - 15-30 minutes of manual searching per query
- Hidden specifications - Critical data buried in tables and images
- Knowledge silos - Only experienced employees knew where to find information
- Customer delays - Sales teams couldn't quickly answer technical questions
The knowledge lived in the documents and in people's heads, and both were bottlenecks.
For shareholders in the company, this shows up as slower sales cycles, as onboarding that takes months instead of weeks, as experienced engineers spending their day answering questions that shouldn't require them, and hundreds of “let me get back to you”.
The deeper issue was structural: critical data was buried in tables and images inside PDFs - formats that traditional search tools simply can't read. The information existed. It just couldn't be found by anyone who wasn't already expert enough to know which document to open.
Unique requirements
| Requirement | Complexity |
|---|---|
| 76,000+ documents | Scale beyond traditional approaches |
| Tables & images | Text extraction alone insufficient |
| Technical precision | Exact specification matching required |
| Source traceability | Every answer must link to source |
What we built together: Multimodal RAG AI system
We built a “machine guru” - a retrieval-augmented generation with multimodal extraction. The user types a question the way they'd ask a colleague. The system finds the answer, cites the source, and returns it in seconds.
We used a multimodal approach: AWS Bedrock's Claude 3.5 Sonnet to extract content from both text and visual elements, Titan embeddings to convert every document into searchable vectors, and FAISS to handle the retrieval across 76,000+ indexed documents.
Every answer comes with a source link. In a technical environment where precision matters and compliance requirements mean every claim needs to be traceable, an AI tool must give credible answers.
Privacy compliance was built in from the start - all data stays within the company's own AWS environment, never touching external services.
Tech Stack
| Component | Technology |
|---|---|
| LLM | AWS Bedrock Claude 3.5 Sonnet |
| Embeddings | AWS Bedrock Titan (1536-dim) |
| Vector Search | FAISS |
| Compute | AWS Lambda (serverless) |
| API | Flask on ECS Fargate |
| Storage | S3, RDS PostgreSQL |
| Queue | SQS FIFO |
The results: Hundreds of employee hours saved
After implementing the RAG, we measured the following results:
- Search time: from 30 minutes to 5 seconds - a 99.5% reduction
- Cost per document processed: from $5.00 to $0.05 - 99% reduction
- Documents searchable: from 0 to 76,000+ - full coverage
- Manual data entry: from hours daily to zero - fully automated
The numbers on search time are striking, but the business impact behind them matters most. Sales engineers can now answer technical questions on the call instead of following up. New hires reach productive capability faster because the knowledge that used to require years of institutional memory is now queryable on day one. Senior engineers get their time back.
Why this matters beyond the technology
The three technical decisions that made the machine guru work were the multimodal extraction, serverless architecture, and source traceability.
Multimodal AI was chosen because the value was locked in tables and images, not plain text. Serverless architecture means the system scales to 76,000 documents without a proportional cost structure, and stays economical as the document base grows.
Measuring the business problem is how a 99.5% search time reduction becomes a tool that's still running.
