Consistent intake
Capture documents from monitored email, uploads, folders, portals, scanners, APIs, or existing applications.
From inbox to system
A model result is not a completed document workflow.
Organizations receive important information in purchase orders, invoices, applications, inspection forms, certificates, contracts, statements, claims, and free-form attachments. Employees open the files, identify the document type, read fields, compare rules, re-enter values, and decide where the item goes next. Volume and variation make that work slow and difficult to standardize.
Modern AI and document services can classify content and extract both structured fields and meaning from variable layouts. A dependable solution still needs intake controls, file handling, validation, reference-data checks, duplicate detection, confidence thresholds, secure storage, exception routing, audit history, and integration with CRM, ERP, accounting, document, or custom systems.
Naper Solutions designs the complete workflow. We begin with representative documents and the decisions people make today, then evaluate the simplest reliable combination of rules, OCR, document services, language models, and human review.
Workflow leverage
Move routine documents faster without hiding uncertainty.
The system should automate predictable work, surface exceptions clearly, and preserve evidence for review.
Structured information
Classify content and extract required values, tables, entities, or summaries from varied document formats.
Validated results
Check values against business rules, reference data, totals, duplicates, and required-field expectations.
Focused human review
Route uncertain, incomplete, unusual, or high-consequence cases to an efficient approval screen.
Scope
The complete intake, review, and integration layer.
A production solution combines document intelligence with conventional software controls and a practical experience for reviewers.
Document intelligence
Select and evaluate the right extraction pattern for the document set, fields, accuracy needs, and operating volume.
- Document inventory and representative test set
- OCR, classification, extraction, and summarization
- Prompt, schema, confidence, and evaluation design
- Quality measurement by document type and field
Workflow and systems
Build the software around the model so accepted results become useful, traceable work.
- Email, upload, API, and folder intake
- Validation, review, approval, and correction screens
- CRM, ERP, accounting, database, and document integration
- Audit history, monitoring, access controls, and support
How we work
Prove quality on real documents before scaling volume.
A representative evaluation set exposes format variation and edge cases early, when the workflow can still be adjusted cheaply.
Sample the work
Collect representative documents, outputs, validation rules, exceptions, volumes, and downstream actions.
Prototype and measure
Test extraction approaches by document type and field, then quantify quality, cost, and latency.
Build the workflow
Add intake, business validation, review screens, integration, security, audit, and failure recovery.
Pilot and improve
Run controlled production volume, compare results, tune thresholds, and monitor quality after rollout.
1999
Built for continuity, not a handoff.
Naper Solutions has solved business technology problems since 1999. The same senior team can carry an engagement from assessment through implementation, deployment, and ongoing support.
Common questions
AI document processing questions.
What types of documents can AI process?
Common candidates include invoices, purchase orders, applications, forms, certificates, statements, claims, contracts, inspection records, and email attachments. Feasibility depends on image quality, format variation, handwriting, required fields, language, and the consequence of an error.
Is AI document extraction completely automatic?
It can be for well-defined, high-confidence cases, but most production workflows benefit from thresholds and human review for uncertainty, missing information, unusual layouts, or high-impact decisions. The review rate should be measured and improved over time.
How do you measure document-processing accuracy?
We create a labeled evaluation set and measure results by document type and important field, not only an overall average. We also track validation failures, review rates, false acceptance, latency, and cost because operational quality involves more than raw extraction.
Can extracted data be sent into our ERP or CRM?
Yes. We can validate and map approved information into CRM, ERP, accounting, database, document-management, or custom application workflows through APIs, supported imports, or other appropriate interfaces.
How is sensitive document data protected?
The design considers access controls, encryption, retention, redaction, logging, vendor data-use terms, environment configuration, and which content can be sent to each service. Requirements should be confirmed for the organization and document types involved.
Related expertise
Keep exploring.
The strongest solution often combines several disciplines. These pages go deeper on adjacent work.
AI solutions
Plan and deliver practical AI across documents, knowledge, email, applications, and operations.
Learn moreAI knowledge assistants
Help teams find grounded answers across approved documents and business information.
Learn moreAPI development
Connect document outputs to the systems, records, and actions that complete the workflow.
Learn more
Start with the real problem
Bring a representative document set and the decisions around it.
We can help you assess feasibility, design an evaluation, choose the right automation pattern, and connect the result to real operations.