Maintenance data is locked inside supplier manuals.
Every supplier sends its own manual, in its own format. Someone has to read each one and type the maintenance plan into the ERP by hand.
- Hundreds of supplier manuals, each in a different format
- Maintenance plans re-typed into the ERP by hand
- Errors found only when a service is missed
- No way to trace a value back to its page
From PDF to ERP, with every value traceable.
We parse scanned and digital manuals, including tables, schedules and multi-language text.
- OCR and layout-aware parsing
- Tables and maintenance schedules
- Any supplier format
Claude on Amazon Bedrock extracts maintenance plans, intervals and parts into a fixed schema, with a confidence score on every field. A RAG chatbot answers questions with cited sources.
- Maintenance plans and intervals
- Spare parts and specifications
- Confidence score per field
Low-confidence fields go to a person for review. Approved data flows into your ERP as structured records.
- Human review for uncertain fields
- PDF name and page cited for every value
- Structured output for your ERP
Less manual documentation work at Tracium.
Scored for confidence and linked to its source page.
From kickoff to your first document type in production.
Supplier manuals, maintenance plans, spare parts and compliance documents.
Research, clinical and regulatory documents, with access control and audit trails.
Contracts and portfolio-company documents, reviewed faster and on the same foundation.
Results in production
AWS
Amazon Bedrock
Anthropic Claude
Amazon S3
Amazon OpenSearch Service
Python- OCR
Amazon CloudWatch
Questions we hear
Which documents can it read?+
Scanned and digital PDFs, including tables and schedules. We start with one document type, such as supplier maintenance manuals, and add more once it is live.
How do you handle mistakes?+
Every extracted field gets a confidence score. Low-confidence fields go to a person for review before anything reaches your ERP.
Can engineers ask questions about the manuals?+
Yes. A RAG chatbot answers questions from the same documents, and every answer cites the PDF name and page.
Can it feed our ERP?+
Yes. The output follows a fixed schema that matches your ERP’s import format, so approved data loads as structured records.
Where does our data go?+
In the cloud, it stays in your AWS account, with Claude running through Amazon Bedrock under your AWS controls. On-premises, documents and the open-source model stay on your own GPU servers. See on-prem RAG & AI agents.
Where does it run, and who controls it?+
Everything we build for you is yours: pipelines, data models, infrastructure code and documentation, in your own cloud account and repositories. Our agents and accelerators come with a licence that keeps working even if you stop working with us. If you need full source access, we offer that too.
Put your first document type into production.
Tell us which documents you work with and where the data should go.