OCR AI
OCR AI Sale Orders
Auto-extract customer purchase orders, RFQs, and quotes from PDFs and email — converted into Odoo sales orders or quotations with line items, customer matching, and pricing applied.
Last reviewed:
What it is
An AI pipeline that ingests customer POs, RFQs, and quotes from PDFs and email attachments; extracts header (customer, PO number, dates, ship-to address) and line items (SKU, description, quantity, price); matches against existing customers in Odoo; applies customer-specific pricing; and creates a sales order in draft state for sales-team review. RFQs become quotations; PO references become sales orders.
Why it matters
B2B sales teams burn hours re-keying customer POs into Odoo. The PO arrives via email as a PDF; someone copies it into the order entry form; pricing rules are checked manually; credit limits are checked manually; the order finally lands in production 24–48 hours after the customer hit send. Faster turnaround means happier customers and fewer rush orders. Automation typically cuts order-entry time from 8–15 minutes per PO to 1–2 minutes (review + approve).
Features
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Multi-format ingestion
PDFs, scanned images, email attachments, EDI 850 documents (where applicable). Email-to-order routing supported.
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Customer matching
Auto-matches against existing customers by name, GST number, address, or email domain. Unknown customers flagged for one-time creation.
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Line-item parsing
Extracts SKU, description, quantity, customer's listed price, requested ship date. Multi-line POs with sub-totals handled cleanly.
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Pricing reconciliation
Applies your customer-specific pricelists (contract pricing, volume breaks). Variance vs. customer's listed price flagged for sales review.
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Credit-status check
Open AR + new order amount checked against customer credit limit. Orders that exceed limit route to credit team for approval before being released.
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RFQ vs. PO classification
Auto-classifies as RFQ (creates a quotation), PO (creates sales order), or change-order (updates existing order). Classification confidence shown to sales rep.
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Continuous learning
Every sales-team correction (customer mismatch, SKU correction, pricing override) feeds back to the model. Accuracy improves week-over-week.
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Multi-language support
Handles POs in English, Hindi, Spanish, Italian, Portuguese, and major Indian regional languages.
How it works
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Discovery + PO sample
We audit 100–300 representative customer POs across your top customers. Output: customer-format patterns, edge cases, and accuracy projection.
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Pipeline build
Email-routing setup, OCR engine configuration, and Odoo integration deployed. Initial model trained on your PO samples.
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Sandboxed testing
Sales team reviews extracted SOs on real POs for 2–3 weeks. Edits feed back to improve accuracy before going live.
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Phased go-live
Start with one customer segment (typically high-volume customers with consistent PO formats), expand to other segments after week 2.
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Tuning loop
Monthly accuracy review with the sales team for the first 90 days; self-tuning afterward.
Deployment timeline
Standard deployment is 6–10 weeks: 1 week discovery and PO sampling, 2–3 weeks pipeline build, 2–3 weeks sandboxed testing, 1 week phased go-live. Existing OCR-Invoice pipelines accelerate sister deployments — many of the patterns are reusable.
Best for
B2B distributors and manufacturers receiving 100+ customer POs per week via email. Especially valuable when customers send POs as PDF (no EDI), when PO formats vary across customers, when sales reps are the data-entry bottleneck, and when faster order-acknowledgement would win or retain key accounts.
Frequently asked questions
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What's the difference between this and EDI?
EDI (Electronic Data Interchange) requires both parties to use compatible EDI gateways and standardized message formats. It's mature and reliable but requires customer adoption — and most small/mid-market customers don't have EDI. OCR AI handles whatever PDF the customer sends, no customer-side change required. Many clients use both: EDI for large customers who already support it, OCR AI for everyone else.
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How accurate is line-item extraction?
First-pass accuracy is 88–94% on line items after the model is trained on your PO samples. Header data (customer, PO number, dates, totals) is typically 92–96%. Edits during the first 90 days feed back to the model; accuracy plateau is reached by month 3.
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What happens when SKUs don't match our catalog?
Customer SKUs (often different from your internal SKUs) are mapped via cross-reference tables. We build the initial mapping during deployment. Unmapped SKUs flag the line for sales review with a search-suggest feature; once a sales rep maps the SKU, future POs from that customer auto-resolve.
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Does it handle change orders and PO revisions?
Yes — when a PO references a previous PO number (e.g. 'change order against PO #12345'), the pipeline classifies as a change-order and updates the existing sales order rather than creating a duplicate. Change reasons (quantity, ship date, address) flagged for review.
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What about customer-specific pricing?
Standard. Odoo's customer pricelists (contract pricing, volume breaks, special agreements) apply automatically when the sales order is created. Variance between the customer's listed price and your pricelist flags for sales review — sometimes the customer is wrong, sometimes the pricelist is out of date.
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Can it handle RFQs that aren't yet POs?
Yes — RFQs are classified separately and create quotations (not sales orders) in Odoo. Quotations follow your standard quote-to-cash workflow: pricing review, approval, send to customer, conversion to SO when accepted.
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What if the customer sends an EDI 850 instead of a PDF?
EDI 850 documents flow through the same pipeline (parsed natively rather than OCR'd) and create the same Odoo sales order. Mixed environments (some customers on EDI, some sending PDFs) work cleanly; downstream sales-order workflow is identical.
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Implementation timeline?
6–10 weeks. Same shape as OCR Invoice: discovery, pipeline build, sandboxed testing, phased go-live. If you're also rolling out OCR Invoice, doing them together cuts ~2 weeks vs. sequential deployments because much of the underlying infrastructure is shared.