Technical inputs
Ingest approved specifications, manuals, PDF drawings, inspection reports, maintenance records, change requests, and non-conformance documents.
Engineering and manufacturing document AI
RAG and OCR for engineering and manufacturing documents.
Make specifications, manuals, PDF drawings, maintenance reports, change requests, and quality records easier to search and review.
OCR extracts configured fields and annotations from scanned technical documents. Validation checks identifiers, revisions, and formats before enterprise RAG retrieves answers from approved files with citations. A controlled agent prepares the review output, while an engineer remains responsible for technical decisions.
Revisionaware retrieval, superseded excluded
OCRtitle block: drawing number, revision, date
Citeddocument, page and revision on every answer
Engineersigns off before anything is used
Technical workflow
Use the right technical document and the right revision.
BlueMouse.ai combines OCR for technical documents with enterprise RAG systems to keep extracted data, retrieved answers, and source evidence connected.
OCR extraction
Capture title-block fields, asset identifiers, report dates, tables, inspection observations, and configured annotations from scanned or image-based files.
Technical validation
Check document revisions, equipment or project identifiers, units, required fields, and extraction confidence before information enters the workflow.
RAG knowledge retrieval
Search permission-approved project files and current technical references, then return the relevant passage with document, page, and revision context.
Engineering output
Produce a cited answer, extracted record, comparison table, checklist draft, or issue summary for a defined engineering task.
Engineering control
Respect document permissions, show citations and uncertainty, log the workflow, and require an engineer to review consequential conclusions or actions.
Revision awareness
The right document is not enough. It has to be the right revision.
Technical libraries accumulate superseded drawings, draft procedures and legacy scans. Retrieval that ignores revision returns confident answers from the wrong document. These are the controls that prevent it.
Control
How it works
Superseded revisions
Excluded from answers by default; available only when a user asks for history explicitly.
Title-block metadata
Drawing number, revision letter, date and approver are read by OCR and attached to every chunk.
Units and tolerances
Tables are extracted as tables, so a tolerance of 0.05 mm does not become prose.
Legacy scanned manuals
OCR with page references, so a cited answer points to a page a technician can open.
Asset hierarchy
Retrieval filtered by plant, line or asset, so a pump question is not answered from a different pump's manual.
Change requests
Linked to the revision they affect, so an open change shows beside the current text.
Suitable first use case
Start with cited technical knowledge retrieval.
Choose a bounded document collection with clear owners and revision rules, such as equipment manuals and maintenance reports for one asset class. The system indexes approved files, extracts metadata where needed, and returns answers with the supporting page and revision. Engineers can inspect the source before using the information.
After retrieval quality and permissions are established, a controlled workflow can prepare maintenance summaries, requirements comparisons, or change-review checklists. See how RAG- and OCR-powered agents support action, or browse other industry document AI applications.
Engineering knowledge
Make technical documents useful without losing source control.
Bring a representative document set, revision rules, access model, and target question for a practical engineering RAG and OCR assessment.