Hcomb,
AI-driven hiring and training platform that finds the right fit, faster
Hcomb — HR Tech Case Study
A hiring and training platform that uses AI to match candidates with roles, automates the job posting process, and conducts AI-powered interviews to help companies find the best talent quickly and efficiently.
Three numbers that
tell the story upfront.
Faster time-to-hire
A hiring and training platform that uses AI to match candidates with roles, automates the job posting process, and condu…
Reduction in manual screening
A hiring and training platform that uses AI to match candidates with roles, automates the job posting process, and condu…
Candidate match accuracy
A hiring and training platform that uses AI to match candidates with roles, automates the job posting process, and condu…
Before we got here,
here's what wasn't working.
Hcomb is an end-to-end AI-driven hiring and training platform that transforms how companies attract, evaluate, and onboard talent. It automates the entire job posting process, intelligently matches candidates to roles using AI scoring, and conducts AI-powered interviews — giving hiring teams a faster, smarter way to identify and secure the right fit.
We confirmed every challenge with the team before writing a line of production code. The right column lists the pain points we tackled head-on and how we approached each one.
What wasn't working
BEFOREResume parsing accuracy across 20+ resume formats
Combined PDF/DOCX text extraction with GPT-4 structured output to reliably extract skills, experience, and education regardless of format.
Vector search latency at scale
Tuned Pinecone index with ANN parameters and implemented a two-stage retrieval: fast approximate search followed by a re-ranking pass.
Coordinating async AI interview scoring with real-time recruiter dashboards
Used RabbitMQ to decouple interview processing from the dashboard, publishing scored results as events consumed by the live pipeline view.
Don't over-engineer.
Solve what matters, ship fast.
The instinct on complex projects is to rebuild everything from scratch. We pushed back. A full rewrite often means months of regressions while engineering rebuilds features that were already working.
We mapped the existing workflow against actual usage. The result was clear: a small set of features accounted for the majority of user time. We rebuilt the core, shipped fast, and extended outward.
On the technical side, we made clear decisions early and held them. The right column shows what we chose, and why.
4 decisions we made early
CHOSEN / REJECTEDAI Candidate Matching — how we built it
Custom AI Candidate Matching — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
Automated Job Posting & Descriptions — how we built it
Custom Automated Job Posting & Descriptions — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
AI-Powered Technical Interview — how we built it
Custom AI-Powered Technical Interview — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
Candidate Onboarding & Identity Verification — how we built it
Custom Candidate Onboarding & Identity Verification — built for this product's exact needs and data model.
Generic SaaS tool that would require heavy workarounds and compromise the user experience.
10 features that
define the product experience.
Every feature was scoped and shipped with the same priorities: speed, reliability, and staying out of the user's way.
AI Candidate Matching
Intelligently matches candidates to open roles using AI scoring and vector search, going beyond keywords to surface the best-fit talent from registered Vbees and LinkedIn contacts.
Automated Job Posting & Descriptions
Qbee (job providers) initiate the hiring process by creating detailed AI-generated job listings specifying qualifications, responsibilities, and expectations — then publish them to the candidate pool in one click.
AI-Powered Technical Interview
Candidates complete an asynchronous AI-driven interview that assesses technical skills, communication, and role fit through a video call and structured questions — producing a scored interview profile with graphs and statistics.
Candidate Onboarding & Identity Verification
Vbees (developers) complete a guided onboarding flow: identity verification with live photo capture and AI face check, personal bio, skill recording, video introduction, and resume upload — all tracked to profile completion.
Profile Progress & Completion Tracking
Candidates and recruiters can monitor profile completeness at a glance, with a step-by-step tracker showing which onboarding stages are done, in progress, or outstanding.
Potential Bees & LinkedIn Contact Pool
Drones (recruiters) can source potential candidates from LinkedIn users or manually added contacts identified by AI-based matching, then reach out via Contact Center AI through WhatsApp, email, and SMS.
Structured Hiring Pipeline
End-to-end hiring workflow from job creation, candidate pool review, profile review, interview scheduling, interview stage, background check, through to final hiring decision — all tracked in one place.
AI Training Module with Mentor AI
Built-in learning platform where admins manage courses, trainers upload content and modules, and trainees follow guided learning roadmaps with AI-powered mentor (Pollinator) providing instant answers and study support.
Trainee Progress & Video Completion Tracking
Trainees see per-course completion percentages, a visual learning roadmap, and video-level progress indicators — keeping learners on track through structured curricula.
Job Offers & Opportunity Discovery
Personalised job offer feed surfaces AI-matched roles for candidates, while a discovery view helps them explore additional opportunities aligned to their skills and experience.
What changed,
measured against the day-one baseline.
We benchmark before we touch anything, logging every metric below against its state before we wrote a line of production code.
Want to build
something like this?
We ship fast. Talk to us about your product idea and we'll tell you exactly how we'd approach it.