Back to all projects
Web App
AI
SaaS
Full-Stack

TruckRecruit

An AI-assisted recruiting platform that centralizes applicants, automates screening, and helps teams hire faster with structured pipelines, scoring, and messaging workflows.

TruckRecruit

Role

Lead Developer & Architect

Timeline

5 months

Year

2024

Stack

Next.js
TypeScript
PostgreSQL
Prisma
OpenAI
Redis
TailwindCSS
Vercel

The Problem

Recruiting teams juggle applications across email, spreadsheets, forms, and DMs. Screening is inconsistent, follow-ups get missed, and hiring decisions are based on incomplete notes. High-volume recruiting needs speed and quality without chaos.

The Solution

Built a full pipeline system that tracks candidates end-to-end — from application through screening, interviews, and offers to hired — with AI-assisted screening, structured rubrics, and automation that reduces manual work while improving decision consistency.

Key Features

The capabilities that make it work.

Kanban-style pipeline stages with configurable workflows per role

Custom application forms with role-specific screening questions

AI-assisted summaries of candidate answers and red-flag detection

Scoring rubrics, notes, tags, and reviewer assignments

Automated follow-ups with email/SMS templates, reminders, and SLA timers

Candidate profile timeline tracking messages, score changes, and stage moves

Funnel metrics with apply-to-hire conversion tracking

Time-to-hire and time-in-stage analytics by recruiter

TruckRecruit screenshot 1
TruckRecruit screenshot 2
TruckRecruit screenshot 3

Architecture

Role-based access control with team permissions and audit trails. PostgreSQL schema spanning candidates, stages, evaluations, messages, tasks, and audit logs. Background jobs handle automations including follow-ups, reminders, and status checks. AI layer uses guardrailed summarization and classification with mandatory human review.

Challenges Solved

The hard problems behind the polished surface.

01

Designing a flexible pipeline engine that adapts to different hiring workflows without becoming overly complex

02

Building a policy-compliant AI screening layer that assists without replacing human judgment

03

Scaling automated follow-ups with SLA timers across thousands of concurrent candidates

The Outcome

Reduced average time-to-hire by 40%. Screening consistency improved across the team with structured rubrics replacing ad-hoc notes. Automated follow-ups eliminated missed candidate touchpoints.

What's Next

Where this product goes from here.

Interview scheduling with calendar sync

Source attribution analytics for recruitment channels

Candidate quality scoring by interviewer performance