Kindlebit
Enterprise AIProposed offering

Turn invoices, claims and contracts into validated data without retyping a single field.

We build document pipelines that classify, extract and check information, send clean results into your systems and put uncertain cases in front of a reviewer with the evidence highlighted.

Send us a sample document setTry the claims demo

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Document pipeline

  1. Ingest
  2. Classify
  3. Extract
  4. Validate
  5. Review
  6. Post to system

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

Manual claims processing

A claim arrives with a form, photos, an invoice and a medical letter, each in a different format. An adjuster reads them all, retypes key facts into the claims system and checks them against the policy.

Why it happens
Claims evidence is unstructured, and the system of record expects structured fields.
What it costs
Slow settlements, transcription errors and adjusters spending skilled time on clerical work.
How we approach it
We classify each page, extract the fields the claim needs with a confidence per field, validate against the policy record and rules, and route low confidence or rule breaking claims to review.
What to measure
Straight through processing rate, extraction accuracy per field and time to first decision.

Invoice data entry

Supplier invoices arrive as PDFs, scans and emails with tables that change layout between suppliers. Staff key header and line data into the ERP.

Why it happens
There is no standard invoice layout, and templates break whenever a supplier changes theirs.
What it costs
Delays, duplicate payments and month end bottlenecks.
How we approach it
We use layout aware and model based extraction rather than fixed templates, validate totals and tax, detect duplicates and send clean invoices into the ERP.
What to measure
Touchless rate, field accuracy, duplicate detection rate and processing cost per invoice.

Slow contract review

Legal must check whether each new supplier contract has the agreed liability cap, termination clause and data protection terms. A first pass takes hours per contract.

Why it happens
Clauses are phrased differently in every contract and live deep in long documents.
What it costs
Backlog, inconsistent reviews and risk missed at signature.
How we approach it
We extract clauses and key terms, compare them with your playbook, highlight deviations with the passage shown, and leave the legal judgement with the lawyer.
What to measure
Review time per contract, deviations found and agreement between the tool and lawyer review on a test set.

Variable document formats

The same document type comes as a native PDF, a phone photo, a fax scan or a spreadsheet. Extraction accuracy varies by source and nobody knows which.

Why it happens
Image quality, language, handwriting and layout vary and a single method does not cope.
What it costs
Hidden error rates, and trust lost on the worst sources.
How we approach it
We measure accuracy by source and quality band, apply image preprocessing, choose engines per type and set thresholds so poor quality inputs go to review.
What to measure
Accuracy by source type, percentage routed to review and rework rate after posting.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

Ingestion and OCR

Capture from email, portals and drives, image correction, OCR and layout analysis for scans and photos.

Customer need
Reliably read the inputs you actually receive.
Integration
Mailboxes, SFTP, storage buckets and scanners.
Deliverable
Ingestion service and quality report.
Business value
A clean starting point.

Document classification

Classification and page splitting to separate documents in a bundle.

Customer need
Know what each page is.
Integration
Workflow and storage.
Deliverable
Classifier and confusion matrix.
Business value
Correct routing.

Field and table extraction

Header fields and line items extracted with per field confidence and a pointer to the location on the page.

Customer need
Turn content into structured data.
Integration
ERP, claims, CRM and data stores.
Deliverable
Extraction service with schema.
Business value
Less keying and fewer errors.

Validation and enrichment

Checks of totals, dates, formats, reference data and cross document consistency, with matching to master data.

Customer need
Catch errors before they are posted.
Integration
Master data, policy and purchase order systems.
Deliverable
Validation rules with tests.
Business value
Fewer corrections downstream.

Human review interface

A review screen showing the source image, extracted values, confidence and reasons for flags, with keyboard shortcuts.

Customer need
Make correction fast.
Integration
Your SSO and workflow.
Deliverable
Review application.
Business value
Faster exceptions and learning data.

Workflow and system integration

Posting results to systems of record, with retries, audit and status callbacks.

Customer need
Close the loop.
Integration
REST, SOAP, file drops and message queues.
Deliverable
Integration connectors.
Business value
End to end automation.

Accuracy monitoring

Sampling, ground truth comparison, drift detection and cost reporting.

Customer need
Prove and maintain quality.
Integration
BI and monitoring.
Deliverable
Quality dashboard.
Business value
Sustained accuracy.

How we solve it

A delivery sequence that includes model selection, evaluation, data governance and human oversight.

Collect representative documents

Gather real samples across sources, quality levels and exceptions, and define the fields and decisions required.

Define schema and rules

Specify extraction fields, validation rules, confidence thresholds and what a person must decide.

Select engines and models

Compare OCR and extraction options on your samples by field accuracy, cost and data residency, including layout aware and large model based approaches.

Build the pipeline

Implement ingestion, classification, extraction, validation and the review interface.

Integrate with systems

Connect to ERP, claims or document systems, handle duplicates and failures, and keep an audit record.

Measure on held out data

Score field level accuracy on documents the system has not seen, by source type and quality band, and set thresholds for automatic posting.

Run in parallel and deploy

Process alongside the manual team, compare outcomes, then switch by document type.

Monitor and retrain

Sample posted documents, track corrections and update models or rules as formats change.

Solution in action: Claims intake from submission to adjuster decision

Solution Concept An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

An insurer receives claims by email and portal with mixed attachments.

Existing workflow

Staff open each attachment, identify the claim type, key facts into the claims system, look up the policy and check coverage rules.

Improved workflow

The pipeline ingests the bundle, splits and classifies documents, extracts claim and incident fields with confidence, retrieves the policy, applies coverage and completeness rules, and creates the claim. Clean claims go to the adjuster with a prepared summary, and doubtful fields are highlighted for review.

A claim bundle arrives.

Systems involved

Email and portal, document store, policy administration system, claims system, review application.

Data movement

Document images, extracted fields with confidence, policy record, rule results.

Human decisions

Reviewers confirm flagged fields. Adjusters make coverage and settlement decisions.

Automation opportunities

Classification, extraction, policy retrieval, completeness checks and claim creation.

Exception handling

Illegible pages, missing documents and policy mismatches go to a review queue with a reason code.

Resulting user experience

The adjuster opens a prepared claim with source highlights instead of starting from a pile of attachments.

KPIs to evaluate

  • Straight through processing rate
  • Field accuracy
  • Time to first decision
  • Manual correction rate

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

  • Document and field schema with validation rules
  • Ingestion, classification and extraction pipeline
  • Review application with source highlighting
  • Integration connectors to your systems of record
  • Accuracy report by document type and quality
  • Monitoring dashboard and sampling process
  • Security and retention design for document data
  • Runbook and user training

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

OCR and extraction (proposed implementation options)

  • Azure AI Document Intelligence
  • AWS Textract
  • Google Document AI
  • Tesseract
  • Layout aware and multimodal large models

Pipeline

  • Python
  • Node.js
  • Queues and workers
  • Object storage

Review and integration

  • React
  • REST and SOAP APIs
  • SFTP
  • Workflow engines

Cloud

  • AWS
  • Azure
  • Google Cloud

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status
Solution ConceptInteractive demo with simulated data

Intelligent Insurance Operations

An interactive demonstration of claim document extraction, policy lookup, rule validation and human review, using synthetic documents and simulated systems.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Extraction accuracy

Correct values per field against ground truth.

Straight through processing

Share of documents posted without human touch.

Manual correction rate

Fields changed by reviewers.

Processing time

Time from receipt to posting.

Questions buyers ask

How accurate will it be?

It depends on document quality and the fields. We measure accuracy per field on your held out documents and set thresholds that control what posts automatically. We do not promise a number before seeing your samples.

Can it read handwriting and poor scans?

Often partially. We measure performance by quality band and send low quality inputs to review, so errors do not enter your systems silently.

Is our document data safe?

We design for your data classification: encryption, access control, retention limits and, where required, in-region or private deployment of extraction engines.

Do you use templates?

We avoid fixed templates because they break when layouts change. We use layout aware and model based extraction, with rules for validation.

How long does implementation take?

A pipeline for one document type with one target system often takes several weeks, with more time for many layouts, languages or complex integrations.

Does the system learn from corrections?

Corrections are captured as labelled data. We use them to tune rules, thresholds and models under a controlled process, and we re-evaluate before changes go live.

Related services

Industries where this applies

Send us a sample set and see what can be extracted.

Share ten representative documents and the fields you need. We will assess feasibility and outline the pipeline.

Send a document sample Explore a demonstration