AI Automation Audit
An AI automation audit is a structured assessment that maps existing workflows, scores them by volume, error cost, and data sensitivity, then selects one for a fixed-scope pilot. The audit produces a one-page scope document with a measurable baseline and an 8-week timeline. In a Swiss logistics firm, the audit typically compares invoice processing against ticket triage, choosing the workflow with the highest monthly manual hours and the clearest GDPR boundary. The output is not a technology recommendation but a business case: cost per document, cycle time delta, and the specific human-in-the-loop checkpoints required under Article 22.
Document Extraction Pipeline
Document extraction pipelines ingest unstructured or semi-structured documents, parse them into machine-readable fields, and route the output to downstream systems. The pipeline typically runs OCR or structured parsing, then uses a language model to extract fields like invoice number, supplier, and line items. For a Swiss logistics team handling German, French, and Italian documents, the model handles multilingual input without separate rule sets. Extraction accuracy is measured against a labeled sample of 100 documents per language, targeting 95% field-level accuracy before moving to production. The pipeline plugs into the existing ERP through its API rather than replacing it.
LangChain and LangGraph
LangChain provides the abstraction layer for chaining model calls, document loaders, and vector stores. LangGraph adds stateful orchestration, letting you model a ticket-triage pipeline as a directed graph where nodes represent classification, extraction, and human-approval steps. For a 20-person operations team, LangGraph’s checkpointing means a failed extraction can resume without reprocessing the entire batch, which matters when you are running 500 documents a day. The architecture is model-agnostic: OpenAI or Anthropic APIs where quality matters, open-weight models on the client’s own hardware where regulated data cannot leave the building.
GDPR Article 22 and Human-in-the-Loop
GDPR Article 22 prohibits automated decisions with legal or similarly significant effects without human oversight. In practice, this means the AI classifies and routes tickets but a person approves any action that triggers a refund, a contract amendment, or a data subject access request. The system logs every automated decision with the model version, input hash, and approver ID to satisfy Article 30 record-keeping. For a Swiss logistics firm, this checkpoint is non-negotiable: the AI drafts the response, a human reviews it, and the approval timestamp is stored in the audit log. The architecture is designed so that removing the human step breaks the pipeline, not just a policy.
Ticket Triage and Routing
Ticket triage and routing is the process of classifying inbound tickets by urgency, category, and required skill, then routing them to the right queue. For a Swiss logistics firm handling German, French, and Italian customers, the model detects language, extracts shipment reference numbers, and flags time-sensitive issues like customs holds. A human reviews any ticket tagged as high-value or involving personal data. The goal is to cut first-response time from 4 hours to under 30 minutes without adding headcount. The triage model runs on a 15-minute batch cycle, pulling new tickets from the helpdesk API and pushing classified results back through the same API.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) grounds the AI’s responses in the company’s own documentation rather than relying on the model’s training data. The system indexes Notion or Confluence pages containing SOPs, escalation paths, and exception handling rules, then retrieves relevant procedures when drafting a response. For a 11-50 person team, this means the AI does not hallucinate a refund policy that contradicts the Confluence page updated last Tuesday. The integration uses the Confluence Cloud API to pull page content on a 15-minute refresh cycle, and the vector store is rebuilt nightly to capture any changes made during the day.
Multilingual Support Coverage
Multilingual support coverage means the AI handles customer communications in the languages the company serves, without separate rule sets or translation layers. For a Swiss logistics firm, this covers German, French, and Italian documents and tickets. The model detects language automatically, extracts fields in the source language, and drafts responses in the customer’s language. Accuracy is measured per language against a labeled sample of 100 documents, targeting 95% field-level accuracy. The multilingual capability is not a feature added after the fact but a requirement baked into the audit: if the workflow cannot handle all three languages at production accuracy, it is not selected for the pilot.
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