Document Intelligence Pipeline
Conversion of unstructured documents into structured, validated and auditable records.
What it does
Organisations in healthcare, financial services and professional services still receive a large share of their operational input as unstructured documents: faxed referral packets, scanned forms, emailed invoices and PDF contracts. Each one has to be identified, read, validated against business rules and entered into a system of record, and most of that work is still done by people.
The Rabbit Cloud Document Intelligence Pipeline automates that path on AWS. Amazon Textract handles layout-aware extraction, Amazon Bedrock performs classification, field extraction and normalisation against a defined schema, and a rules layer validates results before they are written to the client's systems. Anything below an agreed confidence threshold is routed to a reviewer with the source page and extracted values presented side by side. Every decision is logged with the model, prompt version and confidence score, so the pipeline is auditable end to end.
Built for
- Patient access and prior authorisation processing
- Claims, invoice and remittance processing
- KYC and customer onboarding document review
- Contract and policy data extraction
From input to auditable output
Ingest
Documents arrive through Amazon S3, SFTP, email or an API and are placed on an event-driven queue. Duplicate detection and file-type validation run at intake.
Extract
Amazon Textract returns text, layout, tables and handwriting with positional data. Amazon Bedrock classifies the document type and extracts the fields defined for that class.
Validate
A rules engine checks extracted values against business rules, reference data and cross-field constraints. Each field carries a confidence score.
Review
Results below threshold are routed to a reviewer console that highlights the source region for each field. Reviewer corrections feed the evaluation set.
Deliver
Validated records are written to the system of record through the client's API or database, with a full audit record retained in Amazon S3.
AWS services
Controls built in
- PII and PHI detection with configurable redaction before any generative step
- Per-tenant or per-department isolation with AWS KMS customer managed keys
- Confidence thresholds agreed per document class with the client's operations team
- Model, prompt version and confidence recorded for every extracted field
- Evaluation set built from real documents and executed on every change
How the solution is delivered
Each stage defines scope, cost and outcomes before the next commitment. Qualifying engagements are structured to AWS partner programme requirements.
| Stage | Duration | Outcome |
|---|---|---|
| Assessment | 2 to 4 weeks | Use-case scoring, data readiness review, evaluation set design and a cost-per-transaction model. |
| Proof of concept | 4 weeks | Working pipeline on the client's data against agreed success criteria, with measured accuracy and cost. |
| Production build | 8 to 16 weeks | Hardened deployment in the client's AWS account: infrastructure as code, guardrails, monitoring and integration. |
| Operate | Ongoing | Evaluation on every change, model and cost review, and managed operations under SLOs. |
Delivered with this solution
Frequently asked
What document types can it handle?
Any document class for which a field schema can be defined: referral and prior authorisation packets, claims, invoices, remittances, KYC documents, contracts and policy schedules. New classes are added by defining the schema and providing a sample set for evaluation.
How is accuracy measured?
Against a client-specific evaluation set assembled during the assessment. Straight-through processing rate and field-level accuracy are reported per document class, and the evaluation runs automatically on every prompt, model or rules change.
What is the cost per page?
It depends on document complexity and the models selected. A cost-per-page model is produced during the assessment and validated during the proof of concept, before any commitment to production build.
Discuss Document Intelligence Pipeline with a solutions architect
A structured assessment determines feasibility, data readiness and the appropriate delivery path before any commitment to build.