AI app for human resources · no coding needed
Transparent opportunity matching and shortlist platform
Reduce shortlist assembly time while keeping every match explainable.
Made for: Recruiters and hiring managers building shortlists from live web data
What it does for you
The problem
Candidate and task lists are scattered across platforms, and shortlists are hard to explain or reproduce.
What it gives you
Reviewer-approved shortlists linked to source evidence
What you give it
Plain-English criterialive web profilesenrichment data
Build your own version of Exa Websets, Stackpointer and more
One app with what these 3 AI tools do, yours to keep and change: Exa Websets, Stackpointer, Serra (YC S23).
Everything these tools do, in one app
- Natural Language Queries Search using plain English to find specific leads, candidates, or tasks based on detailed criteria.Found in Exa Websets, Serra (YC S23)
- Live Web Data Results are curated from the live web, ensuring fresh and relevant information.Found in Exa Websets
- Automatic Enrichment Enhances search results with additional data such as emails, funding details, and categorized tags.Found in Exa Websets
- One-Click Export Easily export curated lists to CSV or share them with colleagues instantly.Found in Exa Websets
- Customizable AI Columns Allows users to tailor the data output to fit unique workflows and preferences.Found in Exa Websets
- Automated Task Prioritization Automatically prioritizes tasks based on project deadlines and dependencies.Found in Stackpointer
- Real-Time Progress Tracking Tracks project progress in real-time with detailed analytics and reporting.Found in Stackpointer
- Integration with Development Tools Integrates with popular development tools and platforms.Found in Stackpointer
- AI Resource Suggestions Provides AI-driven suggestions for resource allocation and workload balancing.Found in Stackpointer
- Customizable Dashboards Allows users to customize dashboards to monitor key project metrics.Found in Stackpointer
- Vector-Based Search Uses a vector-based search engine to access a large database of candidates from multiple platforms.Found in Serra (YC S23)
- Automatic Filter Generation Automatically generates and customizes keyword filters such as past job titles and company funding stages.Found in Serra (YC S23)
- Candidate Summaries Provides summaries highlighting why profiles match the search criteria for quick evaluation.Found in Serra (YC S23)
- ATS Integration Integrates with applicant tracking systems (ATS) to streamline workflows.Found in Serra (YC S23)
- Save Search Results Allows users to save search results and candidate lists within the platform.Found in Serra (YC S23)
How it works, step by step
- Accept plain-English search criteria
- Curate results from the live web
- Enrich results with emails, funding details and tags
- Generate and customize keyword filters
- Summarize why each profile matches
- Save searches and candidate lists
- Export curated lists to CSV or share them
- Tailor output with customizable AI columns
- Prioritize tasks by deadline and dependency
- Track progress with real-time analytics
- Suggest resource allocation and workload balance
- Provide customizable dashboards
- Search a large multi-platform candidate database
- Integrate with applicant tracking systems
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
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 opportunity matching and shortlist platform 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 opportunity matching and shortlist platform 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 links3 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data195 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
Reduce shortlist assembly time while keeping every match explainable. For recruiters and hiring managers building shortlists from live web data, convert plain-English criteria, live web profiles and enrichment data into reviewer-approved shortlists linked to source evidence. The benefit is a testable hypothesis, measured through accepted shortlist entries per recruiter hour and corrections after shortlist approval; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect plain-English criteria, live web profiles and enrichment data, then follow this sequence: 1. Accept plain-English search criteria. 2. Curate results from the live web. 3. Enrich results with emails, funding details and tags. Resolve uncertain cases with qualified reviewers, approve reviewer-approved shortlists linked to source evidence, and measure accepted shortlist entries per recruiter hour and corrections after shortlist approval against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 search scope and licensed data sources; final hiring decisions and eligibility checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve candidate privacy, source attribution, consent accuracy and usage permissions. Recruiters approve substantive changes and shortlist scope. One fixed search scope and licensed data sources; final hiring decisions and eligibility checks remain human. 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 search scope and licensed data sources; final hiring decisions and eligibility checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept plain-English search criteria; curate results from the live web. Support the third module with operator review: enrich results with emails, funding details and tags. 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 criteria, authorized profiles and permitted research sources. Cloud data storage, ATS import/export and job board destinations. 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: Search brief and criteria, Editable shortlist preview, Client proof and delivery. Use a thumbnail gallery for searches, a large central shortlist canvas, and a right-hand panel for criteria, enrichment and comments. Let users compare candidates side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant profile. Make the task-specific outcome reviewer-approved shortlists linked to source evidence visible beside its evidence, review state and value baseline.
