OCR AI
OCR AI Invoice Capture
Auto-extract vendor invoices straight into Odoo's bills queue — line items, GST, vendor matching, and approval routing handled by an AI pipeline that learns from your team's corrections.
Last reviewed:
What it is
An AI pipeline that ingests vendor invoices from PDFs, scans, and email attachments; extracts structured data (vendor, invoice date, due date, line items, GST/VAT, totals); matches against existing vendors and POs in Odoo; and creates a vendor bill in draft state for human review and approval. The model learns from each correction your AP team makes — accuracy improves over time without explicit retraining.
Why it matters
AP teams spend most of their time on data entry, not value-add work. A typical 5-person AP team processes 2,000–5,000 invoices a month; manual entry takes 3–6 minutes per invoice. OCR Invoice Capture cuts that to 30–60 seconds per invoice (review and approve, not entry). The freed-up time lets the team focus on vendor-management quality, GL accuracy, and exception handling — not retyping.
Features
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Multi-format ingestion
PDFs, scanned images, email attachments, and uploaded files all flow into the same pipeline. Email-to-invoice routing supported.
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Line-item extraction
Header (vendor, dates, totals) plus line-level detail (quantity, description, rate, GST, total). Multi-page invoices handled cleanly.
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Vendor matching
Auto-matches against existing vendors using name, GST number, address, or bank details. Unknown vendors flagged for one-time vendor creation.
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PO matching
When invoices reference POs, line items match against PO lines automatically. Variance (price, quantity) flagged for AP review.
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GL coding suggestions
Suggests GL accounts based on vendor, line description, and historical posting patterns. AP team accepts or overrides; the model learns.
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Approval routing
Draft bills route to approvers based on amount, vendor, or department rules. Approvers see the original invoice document alongside the structured data.
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Duplicate detection
Hash-based duplicate detection catches the same invoice arriving via two channels (email + portal upload). Configurable false-positive override.
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Continuous learning
Every AP-team correction (vendor mismatch, line-item edit, GL recoding) feeds back to the model. Accuracy improves week-over-week without manual retraining.
How it works
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Discovery + invoice sample
We audit a sample of 200–500 representative invoices across your vendors. Output: vendor patterns, format variability, and accuracy projection.
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Pipeline build
Email-routing setup, OCR engine configuration, and Odoo integration deployed. Initial model trained on your invoice samples.
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Sandboxed testing
AP team reviews extracted data on real invoices for 2–3 weeks. Edits feed back to improve accuracy before going live.
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Phased go-live
Start with one vendor category (e.g. utilities and recurring services where format is consistent), expand to general vendors after week 2.
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Tuning loop
Monthly accuracy review with the AP team for the first 90 days. After that, the model self-tunes from corrections without dedicated reviews.
Deployment timeline
Standard deployment is 6–10 weeks: 1 week discovery and invoice sampling, 2–3 weeks pipeline build, 2–3 weeks sandboxed testing with the AP team, 1 week phased go-live. The model continues learning from corrections after go-live; accuracy plateau is typically reached by month 3.
Best for
AP teams processing 500+ invoices per month where manual entry is consuming significant headcount. Especially valuable when invoice volume is growing faster than headcount, when AP is a hiring bottleneck, or when invoice formats vary across many small vendors. Less useful for teams processing fewer than 100 invoices a month — the setup cost outweighs the manual-time savings at low volume.
Frequently asked questions
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What's the typical accuracy?
First-pass accuracy is 92–96% on header data and 88–94% on line items after the model is trained on your invoice samples (typically 200–500 invoices). 'First-pass' means the AP team reviews and approves without edits. Edits during the first 90 days feed back to the model and accuracy continues improving — most clients see 95%+ on header and 92%+ on line items by month 3.
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What invoice formats does it handle?
PDFs (digital and scanned), images (JPG, PNG), and emails (with PDF/image attachments). Multi-page invoices handled. Handwritten invoices and very low-quality scans (sub-300dpi) have lower accuracy — we flag these during discovery and recommend a vendor portal alternative.
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Does it handle GST / VAT compliance?
Yes — GST extraction (CGST/SGST/IGST/cess for India), VAT for EU/UK clients, sales tax for US clients. Line-level tax breakdowns captured separately. Tax-mismatch detection (e.g. line totals don't add up to header total) flags invoices for review.
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What about three-way matching?
Standard. When invoices reference POs, our pipeline matches line items against PO lines and goods-receipt records. Variance (price difference, quantity short/over, missing GR) flags the bill for AP review with the discrepancy highlighted.
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Will it work with our existing approval workflow?
Yes — Odoo's standard approval workflow handles routing based on amount, vendor, department, or custom rules. Our pipeline creates draft bills; existing approval logic takes over from there. We don't replace your approval process.
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Can vendors send invoices directly?
Yes — we set up an email address (e.g. invoices@yourcompany.com) that routes attachments through the pipeline. Vendors send invoices to that address; AP team reviews structured drafts in Odoo. No portal sign-up required for vendors.
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What happens with low-confidence extractions?
Bills with confidence below threshold (configurable, default 80%) flag for AP review with the uncertain fields highlighted. AP team confirms or corrects; corrections feed back to the model. No silent low-confidence posts — everything below threshold gets human review.
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Implementation timeline?
6–10 weeks total. The bottleneck is usually invoice-sample collection (your AP team needs to gather 200–500 representative invoices) and the sandboxed testing period (2–3 weeks of real corrections to train the model). Phased go-live happens in week 6–8.