{"id":339,"date":"2026-10-06T19:00:20","date_gmt":"2026-10-06T19:00:20","guid":{"rendered":"https:\/\/blog.forfis.com\/blog\/uae-logistics-rag-candidate-screening-pilot\/"},"modified":"2026-10-06T19:00:20","modified_gmt":"2026-10-06T19:00:20","slug":"uae-logistics-rag-candidate-screening-pilot","status":"publish","type":"post","link":"https:\/\/blog.forfis.com\/blog\/uae-logistics-rag-candidate-screening-pilot\/","title":{"rendered":"8-Week RAG Pilot: Cutting Candidate Data Entry by 73% in a UAE Logistics Firm"},"content":{"rendered":"

Background: A 300-Person Logistics Firm in the UAE<\/h2>\n

This case study is a composite based on patterns observed across multiple engagements. We do not name real clients. The company described here is a mid-size logistics and supply chain operator in the UAE, with roughly 300 employees, operating in Dubai and Abu Dhabi. The firm runs a standard stack: SAP for ERP, Salesforce for CRM, Google Workspace for email and documents, and a legacy ATS (applicant tracking system) that predates the current hiring volume. The company is in a scaling phase, having doubled headcount over 18 months, and the HR and compliance teams are stretched thin. The CEO and COO are the decision-makers; there is no dedicated data science team. The firm handles personal data (candidate resumes, visa documents, salary history) subject to both GDPR (for EU-based candidates) and the UAE Personal Data Protection Law (PDPL, Federal Decree-Law No. 45 of 2021).<\/p>\n

Challenge: Manual Data Entry at Scale, with a Compliance Deadline<\/h2>\n

The HR team was processing 150 to 200 candidate applications per week across three departments: operations, compliance, and IT. Each application required a recruiter to manually extract fields from PDF resumes into the ATS: name, contact, years of experience, certifications, visa status, and expected salary. This took 3 to 5 minutes per candidate, roughly 12 to 15 hours of manual data entry per week. The error rate was 8 to 12%, with common mistakes including misread visa expiry dates and transposed phone numbers. The compliance team flagged a risk: under GDPR Article 22 and UAE PDPL Article 17, any automated decision-making affecting candidates required human oversight. The firm had no process to audit AI outputs, and the CEO set a hard deadline: a working pilot within 8 weeks, before the Q3 hiring surge. The constraint was not budget; it was time and compliance certainty.<\/p>\n

Approach: A Fixed-Scope RAG Pilot with Human-in-the-Loop<\/h2>\n

Forfis ran a one-week process audit, mapping the resume-to-ATS workflow and identifying the 12 fields most prone to manual error. The pilot scope was fixed: a retrieval-augmented knowledge assistant that ingests PDF resumes, extracts structured fields using an LLM, and returns a pre-filled ATS form for human review. The architecture used pgvector for embedding search over a small corpus of past hiring decisions (to calibrate extraction accuracy), OpenAI\u2019s API for generation, and a thin integration layer into Google Workspace (Gmail for resume intake, Google Docs for review notes). The model was model-agnostic: the pipeline called an API endpoint, so the client could swap to an on-prem open-weight model (Llama 3 70B) if data residency requirements tightened. A dedicated AI team of three (one engineer, one product designer, one compliance consultant) worked on-site in Dubai for the first two weeks, then remotely. Every extraction was logged; a human reviewer approved or corrected each field before it entered the ATS.<\/p>\n

Outcome: 73% Faster Processing, 80% Fewer Errors<\/h2>\n

After two weeks of pilot operation, the team measured before\/after baselines. Cycle time per candidate dropped from an average of 4.2 minutes to 58 seconds, a 73% reduction. The error rate on the 12 tracked fields fell from 9.5% to 1.8%, with the remaining errors concentrated in visa expiry dates (a known OCR weakness on scanned PDFs). The HR team processed 180 applications in the pilot week versus 140 in the prior week, with the same headcount. The compliance team signed off on the human-in-the-loop workflow: no field entered the ATS without a human click. The model-agnostic design meant the client could migrate to on-prem inference in Q4 if the UAE PDPL enforcement tightened. The pilot cost was within the fixed-scope budget; the ongoing managed operation (monitoring, model updates, support) was priced at a monthly retainer. The CEO approved rollout to the IT and operations departments in the following quarter.<\/p>\n

Lessons for Teams Scaling AI Across Departments<\/h2>\n