Capture order and preserve source
AI Employee for order management
AI sales order automation: from email, PDF or WhatsApp to ERP without rekeying data.
B2B orders arrive through formats customers already use: emails, PDFs, Excel sheets, messages, photos and notes containing their own product references. The problem is not receiving them but converting them into correct order lines, mapping every reference to the right SKU, applying prices and terms, checking availability, detecting duplicates and recording the result in the ERP. An AI Employee can coordinate that journey while keeping exceptions and sensitive decisions under human control and preserving evidence for every transformation.
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1. Unify intake without forcing customers to change
The workflow can monitor a sales inbox, receive documents from a portal or collect messages from authorised channels. Each input becomes a case with an identifier, probable customer, original document, timestamp and state. The goal is to absorb channel variety without pushing work back to the buyer or creating another inbox that staff must manually monitor.
Capture must not equal order acceptance. Files, senders and formats are validated first, and unexpected content is isolated. This separation prevents an inbound message from triggering an ERP write by itself and makes it possible to apply different policies by channel, customer or legal entity.
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2. Extract lines, quantities and terms with evidence
The AI Employee structures purchase-order number, references, descriptions, quantities, units, requested dates, addresses and special instructions. It keeps the source document and the relationship between each field and its origin so a person can quickly review data when uncertainty exists.
Extraction is a proposal, not automatic truth. Totals, units and formats are checked with rules; missing or conflicting fields create an exception. Automation therefore accelerates clear cases without inventing information to complete an incomplete order.
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3. Identify the customer before applying commercial terms
Email domain, tax identifiers, internal codes, addresses and historical references help associate an input with an existing account. The match must reach a business-defined level before pricing, discounts, credit or authorised addresses are retrieved.
If two customers are plausible, the system does not choose silently. It presents candidates and evidence for review. Avoiding an incorrect association matters more than saving a few seconds because the wrong identity can contaminate pricing, reserved stock, payment terms and downstream documents.
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4. Map customer references to real SKUs
In B2B, buyers may use their own codes, abbreviations or historical descriptions. The AI Employee combines cross-reference tables, catalogue data, history and context to propose the internal SKU. Exact matches follow deterministic rules; ambiguous matches expose alternatives and confidence for review.
Human corrections are recorded as signals but do not automatically become global rules. A mapping may be valid only for one customer, business unit or period. Versioning these relationships prevents a one-off correction from creating future errors at scale.
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5. Validate prices, discounts and contracts
Once customer and product are identified, the system queries the authorised commercial source: price list, contract, promotion, volume discount or specific condition. AI does not improvise a price when information is missing. If the received condition differs from the active one, it creates an explainable discrepancy.
Pricing exceptions follow the company's defined route. The AI Employee can prepare the comparison, margin, history and stated reason, but an out-of-policy concession remains with the authorised role. This creates speed without weakening commercial governance.
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6. Check stock, dates and fulfilment constraints
The order is checked against available inventory, reservations, calendar, minimum quantities, pack multiples and logistics rules. When the requested date is not feasible, the system can prepare alternatives based on real data rather than promising a delivery operations cannot fulfil.
Availability changes quickly, so it should be queried close to confirmation time. An old reading is not reused as a guarantee. Where stock is constrained or allocation priorities exist, the final decision follows ERP policy and authorised operations owners.
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7. Detect duplicates and resubmissions
The same order may be forwarded, attached to two emails or resent by a customer who did not receive confirmation. Before creating a sale, the AI Employee compares customer, external number, lines, quantities, date and other signals against open cases and ERP records.
Clear matches are blocked; uncertain similarities are presented for review. Idempotency should rely on persistent identifiers and reconciliation with the system of record. Faster processing has no value if sales, reservations or shipments are duplicated as well.
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8. Keep credit and other sensitive decisions under human control
The workflow can query credit status, overdue invoices and limits to determine whether an order may proceed under existing rules. However, increasing a limit, unblocking an account or accepting a financial exception should not become an autonomous model decision.
When a block appears, IA Empleado gathers amount, exposure, history and applicable policy and routes the case to the owner. A person decides and the decision is recorded. The system automates preparation and coordination, not the financial authority defined by the company.
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9. Create draft orders through bounded writes
After validations pass, the connector can create a draft sales order in the ERP with customer, lines, quantities, prices, dates and references. The operation uses a validated schema, least-privilege permissions and an idempotency identifier, retaining the ERP response as evidence of the result.
Starting with drafts reduces pilot risk. When metrics demonstrate stability, defined standard cases may progress automatically within approved limits. High-value orders, pricing exceptions, credit issues or ambiguous references can retain permanent human review.
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10. Turn exceptions into a clear operational queue
Unknown references, incompatible units, impossible dates, pricing differences or unidentified customers should not disappear into technical logs. Each exception becomes visible work with a category, severity, owner, evidence and recommended next action.
The queue lets people prioritise work that genuinely requires expertise. It also reveals recurring causes: outdated catalogues, incomplete cross-reference tables, unclear policies or slow integrations. Fixing those patterns progressively reduces the percentage of orders requiring intervention.
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11. Measure order quality, not only speed
Metrics should include intake-to-draft time, touchless rate, SKU accuracy, pricing corrections, duplicates prevented, exceptions, write errors, review time and cost per correct order. Speed alone can hide downstream rework.
Segmenting by customer, channel, document type and workflow version shows where stability is sufficient to expand autonomy. The goal is not to maximise an automation percentage but to improve the whole process without increasing commercial, logistics or financial errors.
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12. Scale by customers, channels and actions
A prudent rollout starts with representative customers, known formats and draft creation. It can then expand volume, add channels or automate low-risk actions. Changing one dimension at a time makes improvements and regressions easier to attribute.
Controls do not disappear when scaling; they become permanent infrastructure. Traces, least privilege, approvals, reconciliation, rollback and metrics stay with the system in production. This allows volume to grow without forcing a choice between efficiency and control.
WORKFLOW
Recommended workflow for B2B order automation
Identify customer
Extract lines and terms
Map references to SKUs
Validate price, stock and dates
Detect duplicates
Apply credit controls
Route exceptions to human review
Create idempotent ERP draft
Reconcile and measure outcome
METRICS
What to measure
Intake-to-draft time
Touchless orders
SKU accuracy
Pricing corrections
Duplicates prevented
Exceptions by cause
Integration errors
Review time
Cost per correct order
Downstream rework
RELATED GUIDE
How to automate B2B sales order processing with AI without losing commercial control
A practical architecture for turning unstructured orders into validated ERP drafts while managing references, pricing, stock, credit, exceptions and human review.
FAQ
Frequently asked questions
Can it read orders from PDF, Excel, email or WhatsApp?
Yes, when the channel is authorised and integrated. Content is normalised into a common case and validated before any ERP write.
How does it map customer codes to our SKUs?
It combines explicit cross-references, catalogue data, history and context. Ambiguous matches go to human review rather than being guessed.
Can it create the order directly in the ERP?
It can create drafts through bounded permissions and idempotency. Further autonomy depends on rules, risk and evidence of stability.
What happens with out-of-policy pricing or credit?
The system prepares the discrepancy and evidence, while sensitive commercial or financial exceptions remain with the authorised human role.
How are duplicate orders prevented?
It compares identifiers and content with open cases and ERP records, uses idempotency keys and reconciles the system-of-record response before considering the operation complete.
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