Case study
Intake/OCR & Spend Analytics for Healthcare
Document intake & OCR with auto-classification, summarization, routing and budgeting & spend analytics copilot over GL/PO data.
Enterprise‑grade AI consulting & products for large companies and public institutions. On‑prem or private cloud. Executive‑grade outcomes this quarter. Bilingual (Arabic + English) when needed.
We've delivered AI initiatives with large organizations across the US, UK, and Australia (references under NDA). Our work routinely clears rigorous security reviews, MSAs, and public‑sector procurement.
Vendor‑friendly approach with fast responses on security questionnaires, legal redlines, and data‑processing terms.
Pick one as a production pilot with milestone‑based acceptance (typical window: 8–12 weeks). Timeline depends on data readiness, identity integration (SSO), and security approvals.
Secure search across PDFs, laws, contracts & intranet pages — answers with citations; Arabic↔English summaries.
Impact: Info retrieval time ↓ 60–80%.
Turn letters/emails/scans into routed, SLA‑tracked cases with auto‑classification and summaries.
Impact: Manual sorting time ↓ 70%; misroutes ↓ 40%.
Flag risky clauses, missing annexures, pricing anomalies; enforce your clause library (bi‑lingual).
Impact: Review time ↓ 50%; exceptions caught ↑ 30%.
Chat over GL/PO data; explain variances, forecast spend, suggest re‑allocations.
Impact: Forecast error ↓ 30–40%; spreadsheet toil ↓ 60%.
Real results from enterprise AI deployments across government, financial services, and healthcare organizations.
Case study
Intake/OCR & Spend Analytics for Healthcare
Document intake & OCR with auto-classification, summarization, routing and budgeting & spend analytics copilot over GL/PO data.
This is the first AI system my office approved to run on production data. It cites correctly in Arabic and English, and our staff trust it. We shipped more, with fewer revisions.
Security first. Compliance ready. Outcome‑driven. Vendor‑friendly. Fast responses on security questionnaires, legal redlines, and data‑processing terms.