AI app for human resources · no coding needed
Transparent applicant ranking and shortlist workbench
Cut screening hours while keeping every ranking decision explainable and reviewable.
Made for: Recruiters and hiring managers filling several roles at once
What it does for you
The problem
High application volumes bury relevant candidates, ranking criteria stay hidden, and pipeline updates live in separate tools.
What it gives you
Recruiter-approved shortlist with visible reasons and stage history
What you give it
Uploaded resumescustom application formsranking criteriapipeline notes
Build your own version of AI Applicant Sorting, Runway and more
One app with what these 5 AI tools do, yours to keep and change: AI Applicant Sorting, Runway, Hirenga, Covey Scout Inbound, Dover MCP.
Everything these tools do, in one app
- Resume parsing and scoring Automatically extracts and evaluates candidate information from resumes to assess job relevance.Found in AI Applicant Sorting, Hirenga
- Customizable ranking criteria Allows users to define specific parameters for ranking applicants based on company priorities.Found in AI Applicant Sorting, Runway
- Applicant ranking and categorization Orders and groups candidates according to their fit for the job.Found in AI Applicant Sorting, Runway
- Bulk application processing Handles multiple applications at once to efficiently manage large volumes.Found in AI Applicant Sorting
- Multiple job opening support Manages hiring for several roles simultaneously within the same system.Found in AI Applicant Sorting
- Resume upload and analysis Enables uploading resumes for automated analysis and ranking.Found in Runway
- Combined criteria ranking Ranks applicants using a mix of factors such as skills, culture fit, and experience.Found in Runway
- Simple user interface Provides an easy-to-use interface for quick candidate evaluation.Found in Runway, Hirenga
- Custom application forms Collects candidate data through tailored job application forms.Found in Hirenga
- Visual candidate tracking Uses a Kanban-style pipeline to visually organize and track candidates.Found in Hirenga
- Automated email communication Sends personalized emails for invites, rejections, and updates automatically.Found in Hirenga
- AI-driven lead scoring Scores leads based on engagement and behavior patterns to prioritize follow-ups.Found in Covey Scout Inbound
- CRM integration Connects with popular CRM platforms for smooth data flow.Found in Covey Scout Inbound
- Customizable routing rules Assigns leads to appropriate sales representatives based on defined rules.Found in Covey Scout Inbound
- Analytics and reporting Provides detailed insights and reports on candidate pools or lead performance.Found in AI Applicant Sorting, Covey Scout Inbound
- AI assistant integration Allows managing hiring workflows directly through AI tools like ChatGPT, Claude, and Cursor.Found in Dover MCP
- Interview scheduling Schedules interviews and prepares question sets via natural language prompts.Found in Dover MCP
- Pipeline management Adds notes, updates candidate stages, and moves candidates through the hiring pipeline.Found in Dover MCP
- Pipeline health check Identifies which roles need more sourcing by checking pipeline status.Found in Dover MCP
- Email and calendar coordination Coordinates interviews and candidate communication using connected email and calendars.Found in Dover MCP
How it works, step by step
- Parse uploaded resumes into structured candidate fields
- Score candidates against the role's stated criteria
- Let users define and weight ranking criteria per role
- Rank and group applicants by combined fit factors
- Process bulk applications for several open roles at once
- Collect candidate data through custom application forms
- Track candidates on a Kanban-style pipeline
- Send personalized invite, rejection and update emails
- Score inbound leads on engagement and behavior signals
- Route leads to the right recruiter by defined rules
- Sync candidate and lead records with connected CRM platforms
- Report on candidate pools, funnel stages and lead performance
- Manage stages, notes and shortlists through an AI assistant
- Schedule interviews and prepare question sets from prompts
- Flag roles that need more sourcing from pipeline status
- Coordinate interview times through connected email and calendars
- Compare the reviewed shortlist with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned recruiter-approved shortlist with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Transparent applicant ranking and shortlist workbench with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Transparent applicant ranking and shortlist workbench with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Cut screening hours while keeping every ranking decision explainable and reviewable. For recruiters and hiring managers filling several roles at once, convert uploaded resumes, custom application forms, ranking criteria and pipeline notes into a recruiter-approved shortlist with visible reasons and stage history. The benefit is a testable hypothesis, measured through screened applicants per recruiter hour and shortlist acceptance after interview; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect uploaded resumes, custom application forms, ranking criteria and pipeline notes, then follow this sequence: 1. Parse uploaded resumes into structured candidate fields. 2. Score candidates against the role's stated criteria. 3. Rank and group applicants by combined fit factors. Resolve uncertain cases with qualified reviewers, approve recruiter-approved shortlist with visible reasons and stage history, and measure screened applicants per recruiter hour and shortlist acceptance after interview against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate scores and shortlist drafts for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed set of roles and criteria; final shortlist and rejection decisions remain with the recruiter. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve candidate privacy, source attribution, scoring accuracy and usage permissions. Recruiters approve substantive ranking changes and rejection scope. One fixed set of roles and criteria; final shortlist and rejection decisions remain with the recruiter. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed set of roles and criteria; final shortlist and rejection decisions remain with the recruiter. Implement one approved input format, a bounded representative case set and the first two task modules: parse uploaded resumes into structured candidate fields; score candidates against the role's stated criteria. Support the third module with operator review: rank and group applicants by combined fit factors. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Recruiter-owned resume files, authorized application forms and permitted job-board sources. Cloud file storage, email and calendar providers, CRM platforms and export formats. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Role and criteria setup, Candidate review board, Shortlist and communication. Use a thumbnail gallery for open roles, a central Kanban pipeline for candidates, and a right-hand panel for score reasons, notes and stage history. Let users compare candidates side by side. Display new, screened, shortlisted and rejected states. Provide a hiring-manager preview link with comments anchored to the relevant candidate. Make the task-specific outcome recruiter-approved shortlist with visible reasons and stage history visible beside its evidence, review state and value baseline.
