Modern AI recruiting infrastructure

We target 95%+ field-level accuracy on core entities like contact details, education, experience, skills, seniority, and location across both resumes and JDs. Our stack combines LLMs with rule-based logic, so you get the flexibility of modern models with the consistency and guardrails you need for production ATS, CRM, and job board use cases.

RecruitAISuite parses 100+ languages, including English, French, German, Spanish, Portuguese, Dutch, Japanese, and Chinese, with ongoing expansion based on customer demand. We support all common document formats you see in real world recruiting: PDF, DOC, DOCX, XLS, XLSX, TXT, ODT, RTF, and images such as PNG and JPEG (via OCR).

All data is processed over encrypted channels (HTTPS/TLS), and stored data can be encrypted at rest in your own environment or ours. We do not train global models on your customer data and model behavior is tuned per tenant or per deployment.

Yes. You can consume RecruitAISuite as a managed cloud API or deploy it into your AWS, GCP, or Azure environment. In self hosted mode you keep full data and traffic control and pay a predictable platform and maintenance fee instead of per parse SaaS pricing.

Integration is straightforward via our REST API - most teams have Resume Parser and JD Parser running in staging within 2-3 days. Production deployment typically takes 1-2 weeks depending on UI changes and schema mapping. Adding our matching, enrichment, or automation APIs follows the same pattern.

RecruitAISuite is infrastructure, not another UI product and it sits underneath your ATS, CRM, job board, or recruiting tool as an API layer. You keep your own UX and workflows while we handle resume parsing, JD parsing, candidate and job matching, enrichment, and LinkedIn Recruiter automation.

The platform is designed for high volume parsing and matching across ATS and job board traffic, with typical latencies in the few seconds range depending on document size and workflow. In self hosted deployments you can scale horizontally inside your own cloud, so throughput grows with your infrastructure rather than being limited by a shared vendor cluster.

For the managed cloud APIs, pricing is usually usage based per parse, tuned to be roughly an order of magnitude cheaper than traditional enterprise parsers (closer to $0.03 per parse where legacy vendors often land around $0.30 at similar volumes). For customers who prefer no per parse billing, we offer self hosted plans with a fixed platform fee, so you can parse as much as you want inside your own cloud.