Document AI
The problem
Finance, insurance, healthcare and trade operations that still key invoices, claims and forms into their ERP, claims or core system by hand.
A typical situation
Our teams spend hours re-typing invoices, claims and customer documents into our systems. Errors slip through, payments and claims are delayed, and every month-end becomes a reconciliation exercise. We tried OCR before, but it broke on every new layout and nobody trusted the output.
Outcomes
Indicative ranges, validated in your pilot. These figures come from comparable industry programmes, not from TashIT projects. Your pilot sets a baseline with you and measures the actual change.
Typically 50-80% less keying and checking time per document
Basis: Document processing programmes on structured and semi-structured documents such as invoices and claim forms. Depends on document variety, image quality and the share routed to review.
Typically 30-70% shorter from receipt to posting
Basis: Removing data-entry queues and first-pass checks. Depends on how many approval steps remain manual.
Typically 30-60% fewer data-entry errors
Basis: Validation rules and cross-checks against master data catch errors before posting. Depends on master-data quality.
Typically 0.1-0.5% of invoice value prevented or recovered
Basis: Duplicate, price and quantity checks in accounts payable. Depends on the controls already in place.
From data to decision
We pick one or two high-volume streams, such as supplier invoices, medical claims, KYC packs, bills of lading and letters of credit, or laboratory and discharge reports. Fields, rules and target systems are agreed with your team.
Documents arrive by email, scanner, portal or API. Each one is classified, split where a file holds several documents, and cleaned up for reading.
OCR reads text, tables and legible handwriting. Language models extract fields and line items into a defined schema, with a confidence score for every field. Multilingual documents, including Arabic, are assessed during the diagnostic.
Rules check totals, dates, tax, coverage and contract terms, and match each document with purchase orders, goods receipts, policies or customer records. Duplicates and mismatches are flagged.
Low-confidence fields and failed rules go to a review screen where staff confirm or correct them. Approved data posts to your system with the document image and decision trail linked. Corrections are used to improve extraction.
Data and integration
How the pilot works
Duration
Scope
Agreed in writing before work starts, with targets set against your baseline.
The pilot follows a short diagnostic and ends with a written results review. Scaling up is a separate decision, made on the measured results. How our engagements work
Solution blueprint
Document AI that reads claims, payer remittances and supporting papers, checks them against contract rules and matches payments to invoices, so people review only the exceptions.
Status: Reference architecture and pilot plan, available for a paid proof of concept. Reference material, not a client project.
Where it applies
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5 min read
Every topic
Practical articles on AI, automation and operational data: what works, what it needs and how to test it before you scale.
FAQs
Something else to ask?
Email your question with a little about your site and systems, and we will reply in plain language.
info@tashit.comIt says so. Every field carries a confidence score, and fields below the threshold, or documents that fail a rule, go to a person. Thresholds are set per field during the pilot, based on the cost of an error in that field.
Yes. Extraction can run in your own data centre or in a cloud region you choose, using privately deployed models. The deployment is decided with you, based on data sensitivity, volume and regulatory requirements.
Template OCR expects a fixed layout. Here, language models read documents more as a person would, and validation rules check the result against your own data. A new layout usually needs examples and testing, not a new template for every supplier.
Tell us about your site or process and the data you already have. We will reply with a diagnostic plan, the measures we would propose for a pilot and an honest view of feasibility.