IT Guidance for Recruitment and Staffing
Recruiting organisations work with sensitive candidate data, fast search processes, and many external platforms. IT must bring data quality, access protection, and automation together.
Selection, assessment, and placement stay with recruiting specialists. le dot supports with architecture, technical data protection, integration, and controlled AI pilots.
Focus Areas
AI-Supported Candidate Search
RAG and search solutions are introduced only where data sources, consent, and quality checks are clarified.
Data Protection and Compliance
Candidate data is secured in an nFADP-compliant way: consent, roles, deletion periods, and logging are anchored provably.
Choose systems neutrally
ATS, CRM, and marketing automation are evaluated by process fit, portability, and lock-in risk.
Anchor adoption
New recruiting systems, automations, and AI searches are anchored with key users, training, and usage metrics.
Common situations
Applicant Tracking System (ATS) Integration
Applicant management, CRM, calendar, communication, and reporting are connected in a controlled way through APIs or automation platforms.
Data Sharing with Client Portals
Profiles, notes, and salary details are separated by purpose, recipient, and authorisation before exports or portal access arise.
Candidate Communication Without Spam
Status emails, follow-ups, and talent pool campaigns are rule-based, so outreach stays traceable and opt-out is possible.
Access and Data Leakage
CVs, salary information, and client mandates are secured against wrong permissions, insecure storage, and data leakage.
Further information
- Modern Databases, search and data models for large profile sets.
- Vector Databases, the substrate beneath semantic search.
- Prompt and Context Engineering, context control for reliable AI results.
- Marketing Automation, automated outreach with clear rules.
- Workflow Automation, controlled automation with clear data flows.
- Vendor Lock-in, assessing dependence on ATS and CRM providers.
- Data Governance, ensuring data quality and responsibility.