EU AI Act
The EU AI Act (Regulation (EU) 2024/1689) is the first comprehensive AI regulation in the world, entering into force on 1 August 2024. It classifies AI systems by risk level and imposes obligations on providers and deployers. For a document extraction pipeline that processes order and shipment data, the system is generally not high-risk, but if it touches personal data or feeds automated decisions, it may trigger transparency and logging obligations under Articles 13 and 14. The Act’s Article 4 requires AI literacy for staff operating the system, which Forfis addresses through the pilot’s training module. In this scenario, the compliance checklist maps each pipeline step to the relevant Act articles, ensuring the client can demonstrate conformity during audits.
Document Extraction
Document extraction is the process of converting unstructured or semi-structured documents (PDFs, emails, scanned images) into structured data (JSON, CSV, database records). In this scenario, the LLM reads order confirmations and shipment notifications from Gmail, extracts fields like order ID, shipment ID, carrier, and tracking number, and outputs them as JSON. The extraction accuracy depends on the document format and the LLM’s training data; Forfis measures accuracy per field during the pilot and reports it in the baseline. The human-in-the-loop review step catches extraction errors before the data is written to the SaaS platform, reducing the error rate to below 0.5% in Forfis’s measured baselines.
Human-in-the-loop (HITL)
Human-in-the-loop (HITL) means a person reviews and approves the AI’s output before it affects downstream systems. In this pipeline, the LLM extracts order and shipment data, but a human operator confirms the extracted fields before the data is written to the B2B SaaS platform. This is mandatory under Forfis’s default delivery model for anything touching financial records or customer commitments. The HITL step adds roughly 30–60 seconds per document but reduces error rates to below 0.5% in Forfis’s measured baselines. The EU AI Act’s Article 14 requires human oversight for high-risk systems, and the HITL review step satisfies this requirement by allowing the operator to reject, correct, or escalate the extracted data.
n8n Orchestration
n8n is an open-source workflow automation platform that uses a visual node-based editor to connect APIs, databases, and services. In this scenario, n8n acts as the orchestration layer: it receives a new email from Google Workspace, triggers the LLM extraction node, validates the output against a schema, and pushes the structured data into the B2B SaaS platform’s order management API. n8n’s self-hosted deployment option keeps data within the client’s Austrian infrastructure, satisfying data residency requirements. The platform’s node-based architecture means the pipeline can be modified without code changes, and the model-agnostic design allows swapping between OpenAI, Anthropic, or open-weight models by changing a single configuration parameter.
LLM Integration
LLM integration refers to embedding a large language model into an existing system to perform a specific task, such as document extraction or text classification. In this scenario, the LLM is integrated into the n8n pipeline to read order and shipment emails and extract structured data. The integration is model-agnostic: Forfis uses OpenAI’s GPT-4o or Anthropic’s Claude 3.5 Sonnet for cloud-based processing, or an open-weight model like Llama 3 70B on the client’s own GPU server for regulated data. The n8n orchestration layer abstracts the model choice, so switching providers requires only a configuration change, not a code rewrite. The LLM’s output is validated against a JSON schema before being pushed to the SaaS platform.
Fixed-Scope Pilot
Fixed-scope pilot is a bounded engagement with a defined deliverable, timeline, and success metric. Here, the pilot runs for two weeks, targets one specific workflow (order and shipment status updates), and ships with a measured before/after baseline on cycle time and error rate. The scope excludes multi-language support, voice interfaces, or integration with systems outside the agreed API list. This structure limits risk for the client and gives Forfis a clear acceptance criterion. The pilot report compares the baseline metrics from the first three days (manual process) with the metrics from the remaining nine days (automated pipeline), quantifying the reduction in cycle time and error rate as the business case for full rollout.
Process Audit
Process audit is the first phase of Forfis’s delivery model, typically taking two to three days. Forfis interviews the operations team, observes the current manual workflow, and maps every step from email receipt to data entry completion. The audit identifies which fields are extracted, which systems are involved, where errors occur, and how long each step takes. The output is a process map and a recommendation on which workflow to automate first. In this scenario, the audit confirmed that order and shipment status updates were the highest-volume, most error-prone workflow, making it the ideal pilot candidate. The audit also identifies compliance requirements under the EU AI Act and data residency constraints that shape the architecture.
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