Turn Document-Heavy Work into Reviewable, Automated Workflows
Invoices, claims, applications, contracts and forms still arrive as scans, PDFs and email attachments. Kramora engineers automation that reads those documents, classifies them, extracts the fields that matter and checks them against your business rules. Reviewers see what the system found and why, approve or correct it, and the result flows into the systems your teams already use. Exceptions stay visible instead of disappearing into inboxes.
What We Deliver
OCR & ICR capture
Text recognition for scanned, photographed and handwritten documents, tuned to the quality of the inputs you actually receive. OCR is applied only where a document lacks a usable text layer.
- Scanned onboarding forms
- Handwritten field notes
- Faxed purchase orders
Document classification
Models that sort incoming documents by type, sender or intent so each one reaches the right workflow without manual triage.
- Shared-inbox triage
- Splitting multi-document PDF packets
- Routing claims by line of business
Field and table extraction
Structured extraction of fields, line items and tables, combining layout analysis with language models where templates vary between senders.
- Invoice line items
- Policy declarations pages
- Bank statement transactions
Validation & business rules
Checks that compare extracted values against reference data, prior records and your own rules. Confidence scores decide which items need a person.
- Vendor and purchase order matching
- Date and total cross-checks
- Missing signature detection
Human review & approvals
Review screens that show the source page and extracted values side by side. Corrections are captured for audit and for improving models and rules.
- Exception queues by confidence
- Multi-level approval routing
- Reviewer correction history
RPA for legacy systems
Scripted automation for applications with no usable API, kept narrow, monitored and documented so it does not turn into fragile shadow IT.
- Data entry into desktop applications
- Report downloads from supplier portals
- Updates to legacy terminal screens
Workflow orchestration
Coordination of every step, retry and handoff across extraction services, people and business systems, with a clear status for each item.
- Event-driven processing pipelines
- Aging and escalation alerts
- End-to-end item tracking
Business Problems & Meaningful Outcomes
Solution Architecture
Illustrative reference architecture showing how documents move from intake to validated records, with people reviewing uncertain results.
Reviewers work in a purpose-built queue or inside an existing case tool. Downstream systems receive data through APIs where available, with RPA reserved for systems that offer no alternative.
Document storage, model processing location and retention periods are agreed up front. Sensitive fields can be masked in review screens and logs, and data handling requirements are assessed for each engagement.
Results are written idempotently with document references, so retries never create duplicates and every record traces back to its source page.
How We Work
Sample real documents, map current handling steps and find where errors and delays actually occur.
- Document inventory and sample set
- Current-state workflow map
- Prioritized automation candidates
Define the document taxonomy, extraction schema, validation rules and review experience before choosing models.
- Extraction schema and rule catalog
- Review and exception design
- Integration and data handling plan
Build the pipeline in increments and measure extraction quality against a labeled test set drawn from your documents.
- Working pipeline increments
- Field-level evaluation reports
- Reviewer acceptance sessions
Run alongside the existing process, shift volume as confidence grows and watch for drift as the document mix changes.
- Parallel-run findings
- Operational runbook
- Model and rule improvement backlog
Technology Choices
Example Scenarios
Claims intake for a regional insurer
A regional insurer receives claim forms, repair estimates and photos through email and a broker portal. Adjusters spend much of their day sorting attachments and keying details into the claims system.
Kramora would start from a sample of real claim packets to define document types and the fields adjusters rely on, then build classification and extraction with confidence thresholds agreed with the claims team. Uncertain fields would go to a review queue inside the adjusters' existing workflow, and approved data would post to the claims system through its API.
Accounts payable across several entities
A distribution business processes supplier invoices for several legal entities, each with its own approval rules. Invoices arrive in many layouts, and mismatches against purchase orders surface late.
Kramora would design extraction for header and line-item data, then add matching rules against purchase orders and goods receipts. Approval routing would follow each entity's rules, with exceptions explained to approvers and recurring supplier issues reported to finance.
Frequently Asked Questions
Accuracy depends on document quality, layout variety and the fields you need. It is measured field by field on a labeled sample of your own documents during the build, and confidence thresholds send uncertain values to a reviewer. You see the evaluation results before deciding how much volume to automate.
Related Solutions
Adjacent Services
Industries We Serve
Tell us about your challenge.
Share what you are trying to achieve with automation & document AI and the systems involved. We will suggest a practical next step.