Notchup – AI-Powered Career & Team Co-Pilot

Client

Notchup

Platforms

Web

Transforming Notchup: The Shift from Job Board to Career & Team Co-Pilot

Challenge

Notchup started as a traditional job-matching tool, but it quickly became clear that the platform was falling short of the growing needs of both sides of the talent ecosystem.

For engineering managers, fragmented PeopleOps tools made it challenging to source, evaluate, and build high-performing teams. Hiring cycles were slow, visibility into skills was limited, and onboarding processes lacked efficiency.
For engineers, the platform offered limited support for career progression, providing only job listings without guidance on skill development or long-term growth. Users sought clearer pathways for upskilling, career moves, and role alignment.

The core challenge was to break out of the traditional “ob board” model and reimagine Notchup as an AI-driven platform that created dual-sided value: precision tools for leaders to build stronger teams, and intelligent career pathways for engineers to accelerate professional growth.

Key Objectives

Conversational AI onboarding – streamline user intake by capturing goals and skills progressively, without lengthy forms.
AI-powered job matching – recommend roles aligned with skills, experience, and career objectives.
Personalized career roadmaps – highlight skill gaps and suggest tailored learning resources.
PeopleOps automation – enable engineering managers to source, screen, and onboard with greater speed and efficiency.

Solution

Tech Stack & Architecture

Frontend: React.js, Next.js, Tailwind CSS for a clean, responsive, and accessible UI.
Backend: Node.js, Express.js for API services; integrated with AWS Lambda for scalable compute.
Database: PostgreSQL for structured data; Redis for real-time caching.

AI/ML Integration

LLM Integration: OpenAI GPT, Anthropic Claude, LLaMA for conversational AI & content generation.
Vector Search: Pinecone for semantic job–profile matching.
Custom ML Models: Python (scikit-learn, TensorFlow) for skills analysis & recommendations.
Data Pipelines: Airflow & AWS S3 for structured ingestion and preprocessing.

AI Agents Built

Career Coach Agent – guides profile completion, career paths, job matches.
Talent Scout Agent – autonomously shortlists relevant candidates.
Upskilling Advisor Agent – recommends courses, certifications, projects.
Engagement Agent – sends timely nudges to improve retention.

Backend Functionality

The backend orchestrates real-time data flow between LLMs, ML models, and the database. User interactions are processed via the inference layer, enriched with job and skill data, routed through AI agents (job matching, skill gap detection), and delivered instantly to the UI with caching for speed.

Technology used

Results

With this relaunch, Notchup evolved into a next-generation talent intelligence platform, achieving measurable impact:

40% reduction in time-to-hire for engineering managers.
Faster skill discovery – engineers identify and close gaps in weeks instead of months.
Higher user engagement – chatbot-led onboarding minimized drop-offs.
Enhanced employer branding – hiring managers reported improved candidate experiences and stronger pipelines.

Most importantly, Notchup has shifted from being a transactional job board to a transformational career and team co-pilot for Engineering Managers, setting a new standard in the future of PeopleOps and career mobility with the use of AI Co-Pilot.