Supply
Recruiters need enough relevant talent to compare options, but volume alone does not create a strong hiring process.
Candidates
Find better-fit candidates for startup hiring and recruiter shortlists.
Diplotix helps recruiters and startup hiring teams understand candidate supply, source stronger-fit talent, and review profiles with AI-assisted context. The goal is not more noise; it is clearer shortlists for roles such as software engineers, product managers, designers, and marketers in India and remote-first teams.
Candidate sourcing
Candidate sourcing is the work of finding, understanding, and prioritizing people who may fit a role.
A strong sourcing workflow does more than collect resumes. It clarifies skills, experience, location, salary expectations, work preferences, readiness, and role intent before a recruiter spends time on outreach or interviews.
Context
Candidate context helps teams understand whether a profile is relevant for the role, stage, salary range, location, and work mode.
Review quality
AI-assisted signals can help recruiters prioritize review while keeping final judgment with the hiring team.
Who this page is for
Built for recruiters, founders, and candidates who care about fit.
The page explains how Diplotix thinks about candidate discovery without promising candidate counts, placement rates, or guaranteed hiring outcomes.
- Recruiters building shortlists for software, product, design, marketing, sales, HR, and support roles.
- Founders and startup hiring teams that need clearer candidate context before spending interview time.
- Indian hiring teams comparing AI sourcing, job discovery, and recruiter review workflows.
- Candidates who want roles matched to real skills, preferences, availability, location, and salary expectations.
Candidate types
Types of candidates recruiters often need to evaluate.
Different roles require different review signals. Diplotix is designed around structured candidate context so recruiters can compare fit more carefully.
Software engineers
Frontend, backend, full-stack, platform, and AI-adjacent builders whose skills, experience, work mode, and role intent need structured review.
Product managers
Product operators who can be evaluated through domain context, execution history, stakeholder work, and startup-stage fit.
Designers
Product, UX, UI, and brand designers where portfolios, systems thinking, tools, and problem scope matter more than keywords alone.
Marketers
Growth, content, performance, lifecycle, and brand marketers whose channel experience and measurable scope should be compared with role needs.
AI-assisted hiring
How AI-assisted candidate review works on Diplotix.
AI is most useful when it organizes signals for human review. Diplotix is built to help recruiters understand candidate-role fit, not to automate final hiring decisions.
Step 1
Profile signals are structured
Candidate profiles can include skills, resume context, experience, preferences, work mode, location, and salary expectations.
Step 2
Role requirements are compared
Recruiter criteria and job context help the system organize candidates by likely relevance instead of relying only on exact keywords.
Step 3
Recruiters review the explanation
AI-assisted matching supports human review by showing why a candidate may fit and where closer evaluation is still needed.
Step 4
Hiring actions stay human-led
Diplotix is designed to support recruiter judgment. Hiring decisions, outreach, interviews, and offers remain with the hiring team.
Startup hiring
Common startup hiring use cases.
Startups often need sharper context because each hire has a larger impact. Candidate sourcing should support speed without flattening judgment.
- Hiring the first engineering team without over-indexing on resume brands.
- Comparing product managers by stage fit, execution scope, and domain context.
- Finding designers whose portfolio work matches product maturity and design system needs.
- Building marketer shortlists for growth, content, demand generation, or launch work.
- Reviewing India-based, remote, hybrid, or city-specific talent with clearer preference signals.
- Reducing weak-fit review time while preserving recruiter control and candidate privacy.
Explore next
Related hiring and candidate resources.
Use these pages to move from candidate supply to recruiter workflows, AI recruiting concepts, sourcing resources, job discovery, and common product questions.
For recruitersSee how hiring teams can manage jobs, applicants, matching signals, and recruiter workflows.Discover jobsBrowse public job discovery and understand the candidate-side entry point.Candidate sourcing resourcesReview sourcing concepts, talent discovery, and recruiter review workflows.AI recruiting resourcesLearn how AI-assisted recruiting can support matching, screening, and workflow context.FAQRead common answers about candidates, recruiters, privacy, billing, and support.
FAQ
Candidate sourcing questions, answered.
What does pre-vetted candidate mean on Diplotix?
On this page, pre-vetted means candidate information is organized for recruiter review through profile, resume, skill, preference, and availability signals where available. It does not mean a hiring outcome is guaranteed.
How does AI candidate sourcing help recruiters?
AI-assisted sourcing helps recruiters compare candidate signals with role requirements, surface likely-fit profiles, and understand why a candidate may deserve review.
Is Diplotix only for software engineers?
No. Software engineering is a common startup hiring need, but Diplotix also supports product, design, marketing, sales, HR, and support hiring workflows.
Can Indian startups use Diplotix for hiring?
Yes. Diplotix is designed for modern hiring workflows, including India-based startup hiring, remote roles, hybrid teams, and recruiter-led shortlisting.
Does AI replace recruiter judgment?
No. Diplotix uses AI-assisted signals to organize candidate context. Recruiters remain responsible for review, interviews, decisions, and candidate communication.