Case study · Moonhub
AI recruiting that sources, matches and ranks candidates against each job's real requirements
50% faster time-to-hire
At a glance
- Client
- Moonhub
- Industry
- HR & Recruiting
- Services
- AI Agent Development, AI for HR & Recruitment, Enterprise AI Search, MVP Development for Startups
- Stack
- FastAPI, Python, SQLAlchemy, Elasticsearch, React
- Time-to-hire · client-reported
- −50%
- Search
- Elasticsearch + AI
The challenge
- Manual sourcing took hours per role, and screening was slow and inconsistent.
- Strong passive candidates were missed.
- Agency fees were too high for startups.
- Large talent databases were hard to search.
Our approach
- Built candidate sourcing from LinkedIn data into a searchable talent pool.
- Added AI candidate-to-job matching with fit scores.
- Ranking and one-click shortlisting.
- Fast search and filters across very large candidate pools.
Results
- Days of sourcing reduced to reviewing a ranked shortlist.
- Candidates ranked by fit to the job, beyond keyword matches.
- Client-reported: 50% reduction in time-to-hire. The Moonhub team later announced joining Salesforce.
“The Bit & Bytes team delivered an exceptional experience while building the AI recruitment platform. They ensured smooth integration of complex features, maintained clear communication, and stayed aligned with the project goals from day one. Their focus on quality, efficiency, and innovation made the development process seamless and successful.”
Moonhub · Moonhub
Free resource · PDF checklist
AI Candidate Screening Fairness Checklist
Fairness and human-review checks for AI that ranks or filters candidates.
Related services
Services used on this project
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Next step
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- You own all code from day one