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Procurement of AI-Powered Employee Performance & Appraisal Management System
Tender No: NICL/Procurement/2026/35
Active Tender
Closing Date:
2026-09-24
2026-09-24
Tender Overview
- Category
- IT Services & Support
- Sector
- Services
- Tender Type
- Services
- Procurement Method
- Single Stage Two Envelope
- Submission Method
- E-bids must be submitted through EPADS v2.0 only; manual submissions are not accepted. Hard copy of bid security must be submitted to Manager Procurement Department, 5th Floor, Left Wing, NICL Building, Abassi Shaheed Road, Karachi.
- Estimated Cost
- Not explicitly stated
- Source Name
- PPRA
Location & Dates
Contact & Websites
- Contact Person
- Deputy Manager
- Contact Phone
- +92-343-771-1158
- Contact Email
- shahid.niazi@nicl.com.pk
- Website
- https://epads.gov.pk/opportunities/federal/procurements/109792
- Original Source
- https://epads.gov.pk/opportunities/federal/procurements/109792
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Tender Document
Tender Description
The National Insurance Company Limited (NICL) invites bids for the procurement of an AI-Powered Employee Performance & Appraisal Management System under reference number NICL/Procurement/2026/35 (P109792). This procurement is for Non-Consultancy Services and will be conducted through the e-Pak Acquisition and Disposal System (EPADS) v2.0 using a Single Stage-Two Envelope procedure with Quality and Cost Based Selection (QCBS) technique. The system to be procured is a comprehensive, enterprise-grade AI-based employee performance management platform designed to automate and modernize the entire employee appraisal lifecycle for NICL in Lahore, Punjab.
Items being procured:
- AI-Powered Employee Performance & Appraisal Management System (Total Cost=One Time+Yearly)
The scope includes process study, customization gathering, system configuration, workflow setup, dashboard and report development, user acceptance testing, deployment, capacity building, documentation, and six months of post-deployment support and maintenance. The solution must provide centralized employee profiles, performance tracking, KPI management, AI-driven scoring and analytics, multi-rater 360° feedback, competency assessments, and interactive dashboards for employees, managers, HR, and executives. It must also incorporate explainable AI (XAI) features ensuring transparency and auditability of AI-generated outputs.
The system must support robust security features including role-based access control (RBAC), multi-factor authentication (MFA), AES-256 encryption, and comprehensive audit logging. The total delivery and operationalization timeline is six months from contract signing, with a mandatory six-month warranty and technical support period.
Eligibility criteria include a minimum of five years relevant experience, valid SECP registration, NTN and sales tax registration, active taxpayer status, EPADS registration, and a verifiable AI-specific technology partnership with a recognized global AI provider. Bidders must demonstrate successful completion of at least three comparable projects, including at least one government or financial institution implementation serving over 500 users. The solution must be commercially available, production-ready, and capable of live demonstration.
Bids must be submitted electronically via EPADS v2.0 by **September 24, 2026, 11:00 AM**. Manual submissions will not be accepted. Bid security in the form of a Pay Order or Bid Securing Declaration is required. Technical bids will be evaluated first, with a minimum qualifying score of 70 out of 100. Financial bids of only technically qualified bidders will be opened. The award will be based on QCBS.
A practical tip for bidders: Ensure your technical proposal clearly highlights compliance with the preference for market-proven, pre-built AI solutions that operate independently of recurring commercial AI subscriptions, as NICL gives strong preference to zero-subscription AI solutions. Also, provide detailed documentation and evidence of your AI technology partnership and relevant project experience to maximize technical evaluation scores.
