Banking
Automating credit memo review across 40+ product lines.
67% reduction in analyst review time
A mid-sized commercial lender was processing over 800 credit memos per month across structured loans, trade finance, and revolving facilities. Each required a trained analyst to read the full document, extract key figures, and flag policy exceptions — a process taking 45–90 minutes per memo. ArkOne built a document intelligence layer that parsed memos regardless of originating template, extracted covenant, collateral, and counterparty data into a structured record, and surfaced policy deviations automatically. Analysts now review a pre-populated exception report rather than the raw document. Average review time dropped to under 15 minutes.
Consumer Goods
Centralising product knowledge across 6 ERP systems.
2,400 hours saved per quarter
Following two acquisitions, a consumer goods company was managing product catalogues across six ERP instances with no single source of truth. Descriptions, units of measure, and supplier codes conflicted across systems — causing downstream issues in procurement, logistics, and customer invoicing. We built an intelligence layer that ingested all six feeds, identified and scored record conflicts using embedding-based similarity, and maintained a unified master catalogue that each system could query. The data team moved from firefighting reconciliation errors to reviewing a weekly conflict digest.
Engineering & Construction
Tender evaluation at scale — consistent, auditable, fast.
340 tenders evaluated in a single procurement cycle
A national infrastructure authority ran a procurement cycle requiring evaluation of 340 supplier submissions against a 12-criterion scoring matrix. Using human evaluators alone, this would have required months of calendar time and produced inconsistent results across reviewers. ArkOne designed and built an AI-assisted evaluation system: each submission was parsed, criteria were extracted and scored against the authority's published standards, and a structured evaluation report was generated per tender. Human reviewers validated scores and added qualitative notes rather than reading from scratch. The full cycle completed in 19 days. Results were fully auditable against the source documents.
Insurance
Extracting structured loss data from unstructured claims correspondence.
91% extraction accuracy on first pass
A specialty insurer was receiving claims across marine, property, and liability lines — each with its own vocabulary, document formats, and loss description conventions. Adjusters spent significant time manually extracting loss date, cause, estimated quantum, and affected asset from free-text correspondence before they could begin assessment. ArkOne built an extraction pipeline trained on the insurer's own historical claims vocabulary. The system parsed incoming correspondence, populated a structured claims record, and flagged low-confidence extractions for adjuster review. First-pass accuracy reached 91%, reducing manual extraction effort by over 70% and cutting average time-to-assessment by four days.
