Transparent opportunity matching and shortlist platform

For

Job seekers and hiring teams who need transparent matching and shortlists

Solves

Job seekers and hiring teams waste time on opaque matching, scattered applications and unclear shortlists.

Delivers

Reviewed shortlist with visible match reasons

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce time to a qualified shortlist while keeping candidates informed.

1. Scan job listings and surface roles matching skills, experience and preferences.
2. Monitor job markets daily and add new listings.
3. Learn preferences through conversational intake.
4. Connect candidates directly with hiring managers or founders.
5. Provide personalized resume improvement advice.
6. Recommend insider contacts within companies.
7. Parse and rank candidate resumes against job requirements.
8. Distribute job postings across multiple platforms.
9. Show an interactive candidate matching dashboard with real-time updates.
10. Refine searches with customizable filters.
11. Report hiring analytics and process efficiency.
12. Source candidates from job boards and social media.
13. Schedule interviews and coordinate communication.
14. Provide customizable job posting templates.
15. Offer step-by-step interview preparation and feedback.
16. Submit applications on official career pages after user approval.
17. Generate resumes, cover letters and application answers in the user's voice.
18. Create detailed role descriptions with responsibilities and expectations.
19. Support transparent team communication about roles.
20. Provide role templates for common roles.
21. Report role distribution and overlap analytics.

Everything these tools do, in one app

• Job matching Scans job listings and surfaces roles that match the user's skills, experience, and preferences.Found in Jobright, Dex, Standout and 1 more
• Daily job scanning Continuously monitors job markets and adds new listings every day.Found in Jobright, Dex, Wobo - AI Job Search
• Conversational preference intake Uses chat to learn what matters most to the user, such as goals, technologies, and work environments.Found in Dex, Standout
• Direct hiring manager introductions Connects candidates directly with hiring managers or founders, bypassing traditional application barriers.Found in Standout
• Resume improvement advice Provides personalized feedback and suggestions to enhance resumes and applications.Found in Jobright
• Insider connection recommendations Suggests potential insider contacts within companies to give users a competitive edge.Found in Jobright
• Automated resume parsing Extracts and ranks candidate information from resumes based on job requirements.Found in OpenJobs AI
• Job posting distribution Automatically posts job openings across multiple platforms.Found in OpenJobs AI
• Candidate matching dashboard Provides an interactive dashboard with real-time updates to manage candidate matches.Found in OpenJobs AI
• Customizable filters Allows users to refine candidate or job searches using customizable filters.Found in OpenJobs AI
• Hiring analytics Offers analytics and reporting tools to track hiring metrics and process efficiency.Found in OpenJobs AI, OmniJobs
• Automated candidate sourcing Automatically sources candidates from multiple job boards and social media platforms.Found in OmniJobs
• Interview scheduling Integrates interview scheduling and communication tools to streamline coordination.Found in OmniJobs
• Job posting templates Provides customizable templates for job postings to maintain consistent branding.Found in OmniJobs
• Interview preparation Offers step-by-step interview preparation, feedback, and ongoing support.Found in Standout
• Application submission Automatically submits applications on official company career pages after user approval.Found in Wobo - AI Job Search
• Application writing Generates resumes, cover letters, and application answers in the user's voice.Found in Wobo - AI Job Search
• Role definition Allows users to create detailed role descriptions with specific responsibilities and expectations.Found in RolesHQ
• Team collaboration Facilitates transparent communication about roles across departments and teams.Found in RolesHQ
• Role templates Provides customizable templates to quickly set up common roles within an organization.Found in RolesHQ
• Role analytics Offers insights into role distribution and overlaps to optimize team structure.Found in RolesHQ

What goes in, what comes out

The workflow

1. In
   Start with

   Candidate profiles, role definitions and preferences

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect candidate profiles

4. 3

   Role definitions and preferences

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed shortlist with visible match reasons

AI does the heavy lifting, people stay in charge

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 role taxonomy and approved data sources; final hiring decisions and candidate communication remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Preference intake and profile, Match dashboard and shortlist, Application and interview workspace. Use a list of roles or candidates, a central detail view with match reasons, and a right-hand panel for filters, notes and status. Let users compare candidates or roles side by side. Display sourced, matched, shortlisted and hired states. Provide a shared link for hiring teams with comments anchored to the relevant profile or role. Make the task-specific outcome reviewed shortlist with visible match reasons visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, profile versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Candidate-owned profiles, authorized job boards and permitted company career pages. Cloud storage, calendar and email systems, and ATS destinations. Start with file exchange and validate destination specifications before promising direct posting. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: scan job listings and surface roles matching skills, experience and preferences; monitor job markets daily and add new listings. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will job seekers and hiring teams who need transparent matching and shortlists use it to solve "job seekers and hiring teams waste time on opaque matching, scattered applications and unclear shortlists"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Shortlist acceptance rate and time to first qualified interview.
4. Measure, then decide. Track shortlist acceptance rate and time to first qualified interview; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed role taxonomy and approved data sources; final hiring decisions and candidate communication remain human. Implement one approved input format, a bounded representative case set and the first two task modules: scan job listings and surface roles matching skills, experience and preferences; monitor job markets daily and add new listings. Support the third module with operator review: learn preferences through conversational intake. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed shortlist with visible match reasons. Retain the explicit scope boundary: One fixed role taxonomy and approved data sources; final hiring decisions and candidate communication remain human.

What the build depends on. Profile upload and preview, asynchronous matching jobs, editable version history, reviewer access and tested export formats. High-fidelity matching requires specialist recruiting QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed role taxonomy and approved data sources; final hiring decisions and candidate communication remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined hiring package. Offer a monthly matching allowance after repeat demand. Quote complex enterprise integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed shortlist with visible match reasons. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce time to a qualified shortlist while keeping candidates informed. Demonstrate a concrete reviewed shortlist with visible match reasons using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Job seekers and hiring teams who need transparent matching and shortlists professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed shortlist with visible match reasons from a small authorized input set, with a transparent calculation of shortlist acceptance rate and time to first qualified interview and no promised savings.

The first 30 days

1. Week 1: interview five job seekers and hiring teams who need transparent matching and shortlists and inspect a recent example of opaque matching, scattered applications and unclear shortlists.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure shortlist acceptance rate and time to first qualified interview, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Shortlist acceptance rate and time to first qualified interview. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Shortlist acceptance rate and time to first qualified interview; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed shortlist with visible match reasons. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved role definitions, match reasons and review examples, together with reliable delivery for a narrow hiring niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for job seekers and hiring teams who need transparent matching and shortlists. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Hunter, your AI career agent, Jobright, OpenJobs AI, Dex, OmniJobs, Standout, Wobo - AI Job Search, RolesHQ. Compare this product with the buyer's present method on shortlist acceptance rate and time to first qualified interview. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data source access, storage, reviewer hours, client revision rounds and licensed assessment content. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed shortlist with visible match reasons. Track cost per accepted shortlist, including correction work, unsuccessful cases and support.

Preserve candidate privacy, source attribution, consent and usage permissions. Candidates approve substantive changes and application scope. One fixed role taxonomy and approved data sources; final hiring decisions and candidate communication remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.