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-43% Technical Screening Time

Automated technical hiring with AI-driven coding evaluations and real-world skill testing.

Built an AI-powered developer assessment platform that streamlined technical hiring and improved decision confidence.

Project: CodeAid

AI HiringDeveloper AssessmentsAutomationSaaS

Executive Summary

A quick leadership snapshot of platform scope, delivery approach, and measurable outcomes.

Industry

AI / ML

Platform

Web SaaS

Tech Stack

AI Models, Node.js, PostgreSQL

Result

-43% Technical Screening Time

Timeline

12 weeks

Service Category

AI / ML

Type

Client Project

Live Product

Explore the live project surfaces across web and app platforms.

Problem

Client Background

CodeAid needed a scalable platform to evaluate developer skills with more consistency and less recruiter overhead.

Critical Risk Area

Recruitment teams were spending excessive effort on low-signal technical screening and inconsistent assessment outcomes.

  • Time-consuming manual technical screening
  • Inconsistent candidate evaluation standards
  • Limited ability to assess real-world engineering skills
  • Hiring workflows that did not scale

Solution

Delivery Outcome

Delivered an AI-powered assessment platform with dynamic coding challenges, automated scoring, and recruiter-facing evaluation dashboards.

Why this approach

Automating challenge generation and scoring was critical to reduce hiring latency while preserving evaluation quality at scale.

Dynamic coding challenge generator

Automated scoring engine

AI/ML skill assessment modules

Recruiter analytics dashboard

Process

How we made key decisions, handled technical complexity, and applied engineering expertise to deliver measurable outcomes.

1

Product & Architecture Decisions

  • Designed a modular backend evaluation engine for configurable assessment pipelines
  • Separated challenge generation, execution, and scoring services for reliability
2

Technology Selection Reasoning

  • AI model-assisted challenge design to improve role relevance
  • API-first architecture for integration with hiring workflows
3

Complexity Managed

  • Standardized scoring across varied candidate submissions
  • Improved throughput during high-volume hiring cycles
4

System Design Approach

Shipped core evaluation and scoring first, then iterated on AI-specific assessment modules and recruiter reporting layers.

Engineering Highlights

Key technical decisions that enabled production-grade reliability, maintainability, and system scale.

Backend Architecture Design

Designed a modular backend evaluation engine for configurable assessment pipelines

API Integrations

API-first architecture for integration with hiring workflows

Performance Optimization

Improved throughput during high-volume hiring cycles

Scalability Considerations

Designed a modular backend evaluation engine for configurable assessment pipelines

Data Processing Workflows

Automated scoring engine

Tech Stack

A modern technology stack selected to maximize performance, scalability, and delivery speed.

Our stack is selected for reliability, maintainability, and production scale.

Core Stack

AI Models
Node.js
PostgreSQL
Evaluation Engine

Supporting Tools

We also work with a wide range of modern technologies based on project requirements.

TypeScriptRedisQueue ProcessingAnalytics

Infrastructure / Workflow

Git
GitHub
GitLab
CI/CD
Code Reviews
Agile
Testing & QA

Results

Measured outcomes across efficiency, scalability, and system performance improvements.

Efficiency

-43%

Technical Screening Time

Automation

+36%

Evaluation Consistency

Scalability

+31%

Hiring Workflow Scalability

Avg. Screening Cycle

Before

5.1 days

After

2.9 days

Manual Evaluation Load

Before

100%

After

58%

Qualified Candidate Decision Time

Before

72 hrs

After

38 hrs

Business Impact Snapshot

  • Reduced technical screening cycle time and improved candidate evaluation consistency across hiring teams.
  • CodeAid improved hiring velocity and screening quality by replacing manual evaluations with scalable AI-assisted technical assessment workflows.

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