Retrieval-Augmented Candidate Screening: A 4-Week Pilot for Austrian Healthcare

1. Replace Manual Data Entry First

Most companies that automate candidate screening start by replacing the manual data entry step. Recruiters spend 2-3 hours per week copying data from resumes into their ATS. A retrieval-augmented assistant built on pgvector can extract structured fields (name, experience, certifications) and classify candidates against your job description in under 18 seconds per application. The human-in-the-loop design means a recruiter approves or rejects each classification before it touches the hiring pipeline. This single process automation reduces cycle time by 40-60% and eliminates transcription errors, giving you a measurable baseline before you consider expanding to other workflows.

2. Build EU AI Act Compliance Into the Pilot

The EU AI Act, which entered into force in August 2024, classifies AI systems that make decisions affecting individuals as high-risk. Candidate screening tools that process personal data and influence hiring decisions fall squarely into this category. Article 10 requires data governance, Article 13 mandates transparency, and Article 14 demands human oversight. Forfis builds these controls into the pilot from day one: every classification is logged, every decision is auditable, and no candidate is screened out without human review. This is not a compliance checkbox added at the end; it is the architecture of the system.

3. Use pgvector for Grounded Answers

pgvector is a PostgreSQL extension that stores vector embeddings and performs similarity search. For a 501-2000 employee company, this means you can run your RAG pipeline on the same database as your transactional data, avoiding the cost and complexity of a dedicated vector database. The assistant embeds your job descriptions, screening criteria, and past hiring decisions into pgvector. When a new application arrives, the system retrieves the most relevant chunks and feeds them to an LLM, which generates a classification grounded in your data. This reduces hallucinations and keeps answers current as your criteria change.

4. Integrate With Your Existing ATS via REST APIs

The assistant connects to your ATS, HRIS, or recruitment platform via their REST APIs. Webhooks trigger the screening workflow when a new application arrives. The system extracts structured data from resumes, classifies candidates, and writes results back to your existing system. No replacement of your current tools is required. The architecture is deliberately model-agnostic: OpenAI or Anthropic APIs where quality matters, open-weight models on your own hardware where regulated data cannot leave the building. This means you can switch models without rebuilding the pipeline, and you can keep candidate data within your infrastructure if required.

5. Ship a Measurable Result in 4 Weeks

A 4-week timeline is realistic for a single-process pilot. Week 1: process audit and baseline measurement. Week 2: build the RAG pipeline and API integration. Week 3: test with real data and tune the model. Week 4: measure results, document findings, and hand over. This assumes your APIs are accessible and your data is in a usable format. The pilot ships with a report showing whether the automation meets the agreed thresholds on cycle time and error rate before you commit to rollout. This fixed-scope approach protects you from scope creep and ensures you have a measurable result before expanding to other workflows.

6. Keep Humans in the Loop for High-Risk Decisions

The assistant drafts a shortlist of candidates based on your job description and screening criteria. A recruiter reviews each draft, approves or rejects the classification, and the system logs the decision. This human-in-the-loop design ensures no candidate is screened out without human review, satisfying EU AI Act requirements for high-risk AI systems. The model classifies, the person decides. This is not a limitation; it is the correct architecture for a regulated environment. Every pilot ships with a measured before/after baseline on cycle time and error rate, so you know exactly what the automation achieved and where human judgment still adds value.