Intelligent document processing solutions for invoices, KYC packs, claims and policies
We build document pipelines that read Arabic, English and bilingual paperwork, extract the fields your process needs, check them against your master data and post the result into your ERP or case system. Low-confidence fields go to a reviewer with the source page highlighted. Every decision is logged, and the pipeline is built around your forms, rules and approval chain.
Why document work still eats finance and operations time
Most enterprises still key invoices, onboarding forms and claims by hand because the documents arrive in too many shapes for a template tool to cope. A supplier changes its invoice layout. A KYC pack mixes an Arabic Emirates ID with an English bank letter. Template OCR breaks on each, and the backlog returns to the people it was meant to free.
The cost shows up as late payments, slow onboarding and error corrections nobody budgets for. Teams in UAE and Saudi Arabia carry an extra load, because bilingual, right-to-left documents defeat tools built for English forms.
Our approach to AI document processing
We treat extraction as a decision problem, not a template problem. Several vision-language models read each page independently, a judge model compares their answers field by field and scores agreement, and only fields that clear your confidence threshold pass without a person looking. We proved this on Arabic invoice extraction, where a single model was not accurate enough for straight-through posting and a judged ensemble was.
Around that core we build classification, layout-aware extraction, validation against your master data, a review queue and connectors into Business Central, SAP or your case platform. Our AP Automation product packages this pattern for supplier invoices; this page covers everything else you need read.
What the solution includes
Six components, each configured to your documents during discovery.
Document classification
Sorts incoming files by type, language and scan quality. A tax invoice and a credit note follow different rules, and a blurred photo is sent back for rescan rather than guessed at.
Layout-aware extraction
Reads header fields, line-item tables, stamps, handwritten totals and bilingual labels in their visual context. Right-to-left Arabic and mixed-direction tables are first-class cases.
Multi-model judging
Two or more models read every page. A judge model reconciles their answers and scores agreement per field. That score decides whether a human sees the document.
Business validation
Checks values against your vendor master, open purchase orders, customer records or policy schedules. Arithmetic is recomputed, duplicates detected, tax identifiers matched to the registered entity.
Review queue and audit trail
Reviewers see the field, the source crop and the alternatives side by side and correct with a keystroke. Every original, extraction, correction and posting is retained.
ERP and case system connectors
Validated records post into Dynamics 365 Business Central, SAP, Oracle or your claims and onboarding platforms, with retries, idempotency and a reconciliation report.
High-value use cases for document AI
These document types have the strongest case for straight-through processing. Each defines a correct answer differently, so validation is configured per use case.
Supplier invoice extraction and matching
Header, lines and tax reconciled against purchase orders and goods receipts. Designed to work alongside ZATCA e-invoicing in Saudi Arabia, UAE e-invoicing program and India GST e-invoicing, which reduce but do not remove scanned invoices.
KYC and onboarding packs
Identity documents, proof of address, trade licences and board resolutions read, cross-checked and matched to the applicant. We have built digital onboarding platforms for securities firms in India; the same checks apply to Gulf banks and insurers.
Insurance claims intake
Claim forms, repair estimates, medical bills and photographs extracted into a structured claim, with coverage checked before an adjuster opens the file. Pages that do not belong are flagged, not silently dropped.
Policy checking
Issued policy documents compared clause by clause with the quote, proposal form and carrier schedule, so mismatches in sums insured, endorsements or exclusions are caught before the client sees them.
Contract and purchase-order fields
Parties, dates, payment terms, renewal clauses and penalties pulled into your contract register, with the source clause linked so legal can verify in seconds.
Searchable document archives
Scanned records classified, indexed and searchable by field and meaning, with retention rules applied. Often the first step before an enterprise knowledge assistant can answer questions over the archive.
How does an intelligent document pipeline work in practice?
A document moves through six stages. Most pass through all six untouched; the rest stop at stage four and wait for a reviewer.
- 01
Capture from every channel
Mailboxes, scanner folders, supplier portals and mobile uploads feed one intake queue. Attachments are split and de-duplicated.
- 02
Classify type, language and quality
Each file is labelled by type and language, pages are ordered, and unreadable scans go back to the sender.
- 03
Extract with multiple models
Vision-language models read the page in parallel, each producing fields with coordinates so every value traces to its source crop.
- 04
Judge, score and route
The judge reconciles the readings, assigns confidence per field and sends documents below threshold to review.
- 05
Validate against business data
Totals recomputed, tax numbers checked, vendors matched, purchase orders compared. Failures become typed exceptions.
- 06
Post and reconcile
Clean records post into the ERP or case system. A daily report lists what was posted, what is waiting and why.
Deployment, data residency and integration
Where a pipeline runs is decided before how it runs. We deploy inside your Azure, AWS or Google Cloud tenancy in the region you choose, on-premises next to your ERP, or hybrid with extraction in-country and only anonymized metrics leaving.
Integration uses supported APIs: Business Central and Dynamics 365, where our Business Central practice writes the AL extensions, plus SAP, Oracle, SharePoint, claims platforms and your identity provider. Where we deliver: in India, pipelines are designed for the DPDP Act 2023 and GST e-invoice formats; in UAE, for UAE PDPL and the e-invoicing rollout; in Saudi Arabia, for SDAIA PDPL, NCA ECC-2:2024 and ZATCA Fatoora documents; in United States, for SOX-auditable finance and HIPAA-governed claims data. Our offices cover each region.
Benefits and how we measure them
We agree the measures before the pilot and report them from production data against a baseline from your current process.
Straight-through rate
Share of documents posted with no human touch, reported per document type. This decides the business case.
Field-level accuracy
Precision and recall per field on a verified sample, with a separate line for Arabic and bilingual pages.
Review burden
Reviewer minutes per hundred documents, and how it falls as corrections feed the confidence model.
Turnaround and exceptions
Receipt-to-posting time, plus a typed breakdown of exceptions so the top causes are engineered out each cycle.
Implementation approach
A first pilot covers one document type and one posting target. Each later type reuses intake, judging and review.
- 01
Collect a representative sample
You give us 200 to 500 real documents across suppliers, languages and quality levels, with the values your team considers correct.
- 02
Agree fields, rules and thresholds
We define each field, its validation rule, the confidence below which a person must look, and who that person is.
- 03
Run the multi-model feasibility
Candidate models and the judge run on the sample. We report accuracy per field and language, and say plainly if a type is not ready.
- 04
Build intake, review and connectors
The pipeline is wired to your mailboxes and ERP in a test environment. Reviewers work the real queue.
- 05
Run in shadow, then in production
Live documents run alongside your manual process. When the measures hold, posting switches on for the high-confidence band first.
- 06
Expand by document type
The next type or entity is added with its own rules and sample. Monitoring watches for drift in layouts and model behavior.
Why Constient for document processing
We have run the Arabic invoice feasibility most vendors only talk about, and we publish the honest result: one model was not enough, a judged ensemble was. We build the ERP side as well as the AI side, because an accurate extraction posted to the wrong ledger account is still an error.
The same team owns the data engineering behind the pipeline, and for banks and insurers we design to BFSI governance expectations under SAMA, RBI and UAE regulators from the first workshop.
Frequently asked questions
How does an intelligent document processing engagement with Constient start?
With a sample. You share 200 to 500 real documents of one type with the values you consider correct, under NDA, and we run the multi-model feasibility. You get accuracy per field and per language, a confidence threshold and a scoped pilot proposal. If the type is not ready for automation, the report says so.
Can you handle Arabic, bilingual and right-to-left documents?
Yes. Arabic and Arabic-English documents are where we did our most demanding feasibility work, including invoices with mixed-direction tables and stamped totals. We report Arabic accuracy as a separate line, not one blended number. Indian-language forms in Hindi or Tamil get their own evaluation set in the same way.
Where does our data go and can it stay in-country?
It can. The pipeline runs in your cloud tenancy in the region you choose, on-premises, or hybrid with extraction in-country. Model calls can use regional endpoints or models hosted inside your environment. We write the residency design against SDAIA PDPL, UAE PDPL or the DPDP Act 2023 before any document is processed.
How long does a first pilot take and how is it priced?
A single document type with one posting target typically runs 8 to 12 weeks from sample to shadow mode. We offer a fixed-price feasibility and pilot, then a fixed-scope build per document type or a dedicated team if you have many. We quote after seeing the sample, because the document mix decides the effort.
What do you need from our side?
A document owner who can say what correct looks like, a test ERP or case environment, a mailbox or folder we can read from, and two or three reviewers for the pilot. IT involvement is mostly identity setup and API credentials. Most of your effort lands in the first three weeks, agreeing fields and rules.
Is this the same as your AP Automation product?
AP Automation is the packaged version of this pattern for supplier invoices, with three-way matching and Business Central integration ready to configure. This solution covers every other document type, such as KYC packs, claims, policies and contracts. See AP Automation if invoices are your only problem.
Send us a sample and we will tell you what is automatable
Start with one document type. We run the feasibility on your real paperwork and propose a pilot only where the numbers support it.