See Through Market - 2026 Pricing, Features, Reviews & Alternatives

See Through Market is a comprehensive campus placement management software designed to transform how educational institutions handle recruitment processes. The platform significantly reduces manual workload while enhancing the professionalism of placement activities through its centralized system. Educational institutions can manage job listings, track application statuses in real-time, schedule interviews and assessments, and provide mobile-friendly access to all stakeholders. The software enables one-click Excel downloads of applicant CVs and implements automated student notifications for job opportunities and schedules.

The platform facilitates deeper industry-academia collaboration by maintaining a centralized employer database with engagement tracking capabilities. Institutions can collect valuable feedback and execute automated email campaigns to nurture employer relationships. This multi-dimensional approach extends beyond traditional placements to encompass research and development partnerships, sponsorships, and skill development initiatives. The system delivers actionable data insights through one-click placement reports, year-over-year trend analysis, departmental dashboards, and detailed role and salary information, allowing institutions to align academic goals with industry expectations.

See Through Market prioritizes security and privacy through multiple protection layers. The platform employs advanced DDoS protection combining proprietary systems with Cloudflare, conducts regular vulnerability testing including OWASP Top ten assessments, and performs annual penetration testing following the OWASP WSTG framework. Data security is maintained through TLS encryption for data in transit and RDS AES-two hundred fifty-six encryption for data at rest. The system operates on AWS infrastructure, supporting numerous security standards and compliance certifications, and implements a comprehensive backup and recovery system to safeguard institutional data from loss or damage.