Power Hire Application – Implementation Case Study

The client operates in a high-volume recruitment environment where speed, candidate quality, and decision accuracy directly impact business outcomes.

Existing hiring workflows relied heavily on external ATS platforms, manual shortlisting, and recruiter judgment, resulting in inconsistent evaluations, increased recruiter workload, and longer hiring cycles. To address these challenges, the client engaged Artus to design a lightweight yet intelligent ATS layer that integrates seamlessly with existing systems while introducing AI-led screening and evaluation capabilities.

Problem Statement

Key Challenges

·       Heavy dependency on external ATS with limited screening intelligence

·       Manual resume-based shortlisting at early hiring stages

·       Lack of standardized candidate assessment process

·       Limited visibility into candidate evaluation data

·       High recruiter effort spent on repetitive screening tasks

Business Impact

·       Longer hiring cycles due to manual evaluations

·       Inconsistent candidate quality progressing through the pipeline

·       Recruiter fatigue and reduced productivity

·       Missed high-potential candidates due to subjective filtering

·       Low scalability as hiring volumes increased

Without an intelligent screening layer, early-stage recruitment remained inefficient, subjective, and difficult to scale.

Solution Overview – PowerHire powered by Artus

Artus was responsible for end-to-end solution ownership, including requirement discovery, solution architecture, application design, and validation.

The Mini ATS was designed as an intelligent screening and decision-support layer, integrating seamlessly with the client’s existing ATS while enhancing recruiter workflows through AI-driven assessments and structured evaluation data.

Key Artus Contributions

·       ATS integration architecture connecting Ceipal to fetch candidates already present in the active hiring pipeline

·       Centralized candidate management dashboard providing recruiters with real-time visibility into active profiles

·       AI-driven assessment orchestration, enabling recruiters to trigger candidate assessments directly from the UI

·       AI-generated screening assessments with 10 structured questions per candidate to standardize early evaluation

Key Artus Benefits vs. Traditional Approach

Implementation Approach

Artus followed a consultative, intelligence-led approach, ensuring clarity, alignment, and scalability from the outset.

Phase 1: Discovery & Requirements
 AI-assisted requirement engineering ensured early clarity and surfaced key edge cases.
Phase 2: Architecture & Design
 Workflow modeling and UI prototyping enabled early stakeholder validation.
Phase 3: Integration & Configuration
 API-based integration ensured seamless connection with the existing ATS.
Phase 4: Deployment & Enablement
 Controlled rollout supported smooth recruiter adoption.
Phase 5: Scalability & Evolution
 Design-for-scale principles ensured long-term extensibility and future readiness.

Key Implementation Insights

·       Early AI-led screening significantly improves hiring efficiency

·       Standardized assessments reduce subjectivity in candidate evaluation

·       Seamless ATS integration is more effective than system replacement

·       Treating ATS as a decision-support platform, not just a tracking tool, is critical

Results & Outcomes

·       Accelerated early-stage candidate screening

·       Consistent and standardized evaluation across roles

·       Improved recruiter productivity and reduced manual effort

·       Enhanced visibility into candidate quality and pipeline health

·       Scalable foundation for advanced hiring intelligence

Before vs. After Snapshot