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Koddox Technologies

Work / Finance operations

CONCEPT PROJECT · SOLUTION DESIGN

AI Document Intelligence

From incoming invoices to a review-ready accounting record.

A Koddox concept case study illustrating a proposed system. This is not a delivered client engagement, live application or report of achieved results.

ILLUSTRATIVE WORKSPACE RECORD
Document
INV-DEMO-1042
Status
Needs review
Exception
Purchase order total mismatch
Next action
Compare source and extracted line items
  1. 1. Receive document
  2. 2. Extract fields
  3. 3. Validate evidence
  4. 4. Review exceptions
  5. 5. Export approved record

The business scenario

Consider a distributor receiving supplier invoices through a shared inbox and an upload portal. Staff retype supplier details, compare totals with purchase orders and chase missing information. The proposed system concentrates on document intake and review; payment authorization stays in the accounting team's existing process.

A practical first release

The initial scope covers PDF and image invoices, one organization and one accounting export format. Users can inspect a queue, open the original document beside extracted fields, correct values and approve a record. Roles distinguish uploaders, reviewers and administrators. Credit notes, multiple entities and payment initiation remain outside the first release.

How the workflow would operate

An upload receives a document ID and content fingerprint. A background worker checks file type and size, extracts text and proposes structured fields. Deterministic checks compare line totals, currency, supplier references and required fields. Duplicate candidates and uncertain values enter a review queue. Approved records receive an export status and an immutable record of who reviewed them.

Architecture and integrations

The Next.js workspace would call a Python API backed by PostgreSQL. Original documents would be stored separately with scoped access. A queue separates uploads from longer OCR and extraction jobs. An accounting adapter would validate approved output before export, use a stable record identifier to avoid duplicate exports, and expose retryable failures to operators.

The difficult cases

A scanned invoice may be legible to a person but produce unreliable extraction. The interface should highlight the source area for each field and permit correction. A high model-confidence score does not establish that an invoice is genuine or payable. Supplier bank-detail changes, conflicting totals and missing purchase orders require explicit human review.

Evaluation plan

Use a held-out set of synthetic and permission-cleared invoices covering scans, different layouts, duplicates and malformed files. Measure exact field accuracy, correction effort, exception precision, export failures and processing time. Require every approved export to have a traceable review history and ensure reprocessing cannot create duplicate records. These are proposed acceptance checks, not measured results.

Delivery and next steps

A proposed delivery package includes the intake portal, review workspace, extraction pipeline, accounting adapter, role matrix, test fixtures and operating guide. Begin with a shadow run alongside the existing process before permitting exports. Any business improvement would need to be measured against the team's baseline after implementation.

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