Ras Al Khaimah, UAE · AI & ML / Intelligent Document Processing
Vellum – AI Invoice Processing
Vellum: AI invoice processing cut manual work 70%* at 95–99%* extraction accuracy for a Ras Al Khaimah business
Executive summary
- •Problem: manual invoice entry was slow, error-prone, and delayed finance close/reporting.
- •Solution: OCR + AI validation + automated workflow into existing financial systems.
- •Result (website-published*): 70% less manual processing, 95–99% data accuracy, faster finance workflows.
The business challenge
Staff spent excessive time extracting invoice fields by hand. Error rates created reconciliation debt; slow processing delayed payments and management reporting.
- •AP backlog and late payments
- •Reconciliation rework from bad data entry
- •Finance close delayed by document queues
- •Scaling headcount linearly with invoice volume
Goals & non-goals
Goals
- •Extract structured fields from unstructured invoices
- •Validate against business rules before finance system write
- •Automate happy path; escalate edge cases to humans
- •Integrate with existing financial systems
Non-goals
- •Guaranteed OCR accuracy on every exotic layout without sampling
- •Unattended financial approvals without human policy
The solution
Built an AI invoice pipeline: OCR extraction, AI model tuning for vendor/layout patterns, validation rules, and workflow automation from receipt through approval/export — with human review for flagged edge cases.
OCR extraction
Read key fields across varying invoice formats
AI validation
Business rules + historical vendor patterns
HITL workflow
Humans only on exceptions
Finance integration
Push clean data into existing systems
Technical architecture
| Layer | Technology | Why | Alternatives |
|---|---|---|---|
| Ingest | Document intake pipeline | Normalize files into processing queue | Email inbox chaos |
| Vision/OCR | OCR integration | Convert pixels → candidate fields | Manual retyping |
| Model layer | Custom AI tuning | Vendor/layout-specific accuracy | Generic OCR only |
| Control plane | Validation + workflow engine | Prevent bad writes to ledger systems | Blind auto-post |
| Infra | Cloud processing | Elastic bursts for invoice batches | Desktop OCR tool |
Implementation path
- 1.Sample invoice set + field schema definition
- 2.OCR + model tuning on client layouts
- 3.Validation rules + exception queue UX
- 4.Export/integration into finance systems + pilot accuracy review
Challenges & trade-offs
Layout diversity
Why hard: Vendors send wildly different invoice formats
Solution: Train/tune on client-specific samples; keep HITL for tails
Trade-off: Long-tail layouts need ongoing samples
Trust in automation
Why hard: Finance will not accept silent wrong postings
Solution: Validation gates + exception review before export
Trade-off: Not fully lights-out on day one
Results & metrics
Labels: WP = website-published · OPS = operational outcome · ARCH = design target
| Metric | Before | After | Source | Label |
|---|---|---|---|---|
| Manual invoice processing | Baseline | −70%* | stackwise.site case study | WP |
| Extraction/data accuracy | Error-prone manual | 95–99%* | stackwise.site case study | WP |
| Finance cycle | Delayed by entry backlog | Faster processing* | stackwise.site case study | WP |
| Exception handling | Everything manual | Human review on edge cases only | Solution design | OPS |
Value by stakeholder
Finance ops
Hours back from keying; fewer reconciliation loops
Controller / risk
Validation gates before system write
IT
Cloud pipeline integrable with existing finance stack
Agency buyers
Reference narrative for IDP / document automation
Planning something similar?
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