Sep 18, 2025·8 min
How to Build a Recruiting Web App That Matches Candidates
Learn how to build a recruiting web app that matches candidates to jobs. Covers core features, data model, matching logic, UX, integrations, and launch.
Learn how to build a recruiting web app that matches candidates to jobs. Covers core features, data model, matching logic, UX, integrations, and launch.
Before you sketch screens or pick a tech stack, get specific about what problem your recruiting web application solves—and for whom. “Candidate job matching” can mean anything from a simple keyword filter to a guided workflow that helps a recruiter move a role from intake to placement.
Start with the people who will log in every day. For an app for recruiting agencies, these are usually:
A helpful exercise is to write 2–3 “top tasks” per user. If a task doesn’t support those, it’s probably not MVP.
Avoid vague goals like “better matches.” Pick metrics that reflect business outcomes and reduce manual work:
These metrics later inform your recruiting analytics and help validate whether your matching algorithm is improving results.
Recruitment workflow is more than matching. Document the stages and what data is created at each step:
Sourcing → Screening → Submitting → Interviewing → Offer → Placement
For each stage, note the “objects” involved (candidate, job, submission, interview), the key actions (log call, send email, schedule interview), and the decision points (reject, move forward, hold). This is where ATS and CRM features often overlap—be intentional about what you track.
Your MVP should deliver a usable loop: create a job requisition → add candidates (manual or basic resume parsing) → match → review → submit.
Common v1 inclusions:
Common later features (nice-to-have at first):
By defining users, metrics, workflow, and scope upfront, you prevent the project from becoming “an everything ATS” and keep the build focused on faster, more confident shortlists.
A recruiting web application lives or dies by its data model. If candidates, jobs, and their interactions aren’t structured cleanly, matching gets noisy, reporting becomes unreliable, and the team ends up fighting the tool instead of using it.
Start with a Candidate entity that supports both document storage and searchable fields. Keep the original resume/CV (file + extracted text), but also normalize key attributes you’ll need for candidate job matching:
Tip: separate “raw” data (parsed text) from “curated” fields recruiters can edit. That prevents parsing errors from silently corrupting profiles.
Create a Job (requisition) entity with consistent fields: title, seniority, required vs. nice-to-have skills, location/remote policy, salary range, status (draft/open/on hold/closed), and hiring manager details. Make requirements structured enough to score, but flexible enough for real job descriptions.
Most activity happens between candidates and jobs, so model relationships explicitly:
Define access early: agency-wide vs. team-only candidates, client-specific visibility, and edit rights by role (recruiter, manager, admin). Tie permissions to every read/write path so private candidates or confidential jobs don’t leak through search or matching results.
Recruiters move fast: they scan, filter, compare, and follow up—often between calls. Your UX should make those “next clicks” obvious and cheap.
Start with four core pages plus a matching view:
Recruiters expect search to behave like a command bar. Provide global search plus filters for skills, location, years of experience, salary, status, and availability. Allow multi-select and saved filters (e.g., “London Java 5+ years under £80k”). Keep filters visible, with clear chips showing what’s active.
Bulk actions save hours when dealing with long lists. From candidate list or match view, support: tagging, changing status, adding to a job shortlist, and email export. Include an “undo” toast and show how many records will be changed before confirming.
Make the UI keyboard-friendly (focus states, logical tab order) and readable (good contrast, large tap targets). On mobile, prioritize the list → detail flow, keep filters in a slide-over panel, and ensure key actions (shortlist, email, status) are reachable with one thumb.
Matching is the engine of a recruiting web application: it decides who shows up first, who gets hidden, and what recruiters trust enough to act on. A good MVP starts simple—clear rules first, scoring second—then adds nuance as you learn from real hiring outcomes.
Begin with non-negotiables that must be true before a candidate is considered. These rules keep results relevant and prevent “high-scoring but impossible” matches.
Typical gates include required skills/certifications, location or work-authorization constraints, and salary overlap (e.g., candidate expectations must intersect with the job’s budget range).
Once a candidate passes the gates, compute a score to rank matches. Keep the first version transparent and adjustable.
A practical scoring mix:
You can express this as a weighted score (weights tuned over time):
score = 0.45*skill_match + 0.20*recency + 0.20*seniority_fit + 0.15*keyword_similarity
Model job requirements as two buckets:
This prevents strong candidates from being excluded over preferences, while still rewarding better fit.
Recruiters need to know why a candidate matched—and why someone didn’t. Show a short breakdown right on the match card:
Good explainability turns matching from a black box into a tool recruiters can confidently use, tune, and defend to hiring managers.
Candidate data quality is the difference between “matching” and “guessing.” If profiles arrive in inconsistent formats, the best matching algorithm will still produce noisy results. Start by designing intake paths that are easy for recruiters and candidates, then progressively improve parsing and normalization.
Offer multiple ways to create a candidate profile so teams don’t get blocked:
Keep a clear “confidence” indicator on fields (e.g., “parsed,” “user-entered,” “verified by recruiter”) so recruiters know what to trust.
In the MVP, prioritize reliability over perfect structure:
Always let recruiters edit parsed fields, and keep an audit trail of changes.
Matching works better when “JS,” “JavaScript,” and “Javascript” map to the same skill. Use a controlled vocabulary with:
Apply normalization at save-time (and re-run it when the vocabulary updates) so search and matching stay consistent.
Duplicates will quietly poison your pipeline metrics. Detect potential duplicates using email and phone (plus optional fuzzy checks on name + company). When a conflict appears, show a guided merge screen that:
This keeps the database clean without risking accidental data loss.
A matching app is only as good as the jobs inside it. If requisitions are inconsistent, missing key details, or hard to update, recruiters stop trusting the results. Your goal is to make job intake fast, structured, and repeatable—without forcing users into long forms.
Recruiters typically start jobs in three ways:
In the UI, treat “Duplicate job” as a first-class action on the jobs list, not a hidden option.
Free-text job descriptions are useful for humans, but matching needs structure. Capture requirements in consistent fields:
Keep it lightweight: a recruiter should be able to add skills in seconds, then refine later. If you have a parsing step, use it only to suggest fields—not to auto-save them.
Make the hiring pipeline explicit and job-specific. A simple default works well:
New → Shortlisted → Submitted → Interview → Offer → Placed
Each candidate-job relationship should store the current stage, stage history, owner, and notes. This gives recruiters a shared source of truth and makes your analytics meaningful.
Templates help agencies standardize intake for common roles (e.g., “Sales Development Rep” or “Warehouse Picker”). A template should prefill stages, screening questions, and typical must-have skills—while still allowing quick edits per client.
If you want a consistent flow, route job creation directly into matching and shortlisting, then into the pipeline, rather than scattering these steps across different screens.
Security is easiest to get right when it’s designed into the first version. For a recruiting web application, the goal is simple: only the right people can access candidate data, and every important change is traceable.
Start with email + password authentication, plus password reset and email verification. Even for an MVP, add a few practical safeguards:
For larger agencies, plan a future upgrade path to SSO (SAML/OIDC) so they can use Google Workspace or Microsoft Entra ID. You don’t have to build SSO on day one, but you should avoid choices that make it hard to add later.
At minimum, define two roles:
If your product includes an optional client/hiring manager portal, treat it as a separate permission set. Clients typically need limited access (e.g., only candidates submitted to their jobs, with restricted personal details depending on your privacy model).
A good rule: default to the least access needed, and add permissions intentionally (e.g., “can export candidates,” “can view compensation fields,” “can delete records”).
Recruiting involves many handoffs, so a lightweight audit trail prevents confusion and builds trust internally. Log key actions like:
Keep these logs searchable in-app, and protect them from being edited.
Resumes are highly sensitive. Store them in private object storage (not public URLs), require signed/expiring download links, and scan uploads for malware. Restrict access by role, and avoid sending attachments around by email when a secure in-app link will do.
Finally, encrypt data in transit (HTTPS) and at rest where possible, and make secure defaults non-optional for new workspaces.
Recruiting apps handle highly sensitive data—CVs, contact details, compensation, interview notes. If candidates don’t trust how you store and share that information, they won’t engage, and agencies take on unnecessary legal risk. Treat privacy and compliance as core product features, not add-ons.
Different agencies and regions rely on different lawful bases (consent, legitimate interest, contract). Build a configurable tracker on each candidate record that captures:
Make consent easy to review and update, and ensure sharing actions (sending profiles to clients, exporting, adding to campaigns) check those settings.
Add retention settings at the agency level: how long to keep inactive candidates, rejected applicants, and interview notes. Then implement clear flows:
Keep these actions auditable and reversible only when appropriate.
Support exporting a candidate’s record for access requests where applicable. Keep it simple: a structured JSON export plus a human-readable PDF/HTML summary can cover most needs.
Use encryption in transit and at rest, separate environments, and strong session management. Default roles to least privilege: recruiters shouldn’t automatically see compensation, private notes, or every client submission.
Add an audit log for viewing/exporting/sharing candidate data, and link the policy details from /privacy so agencies can explain your safeguards to candidates.
Integrations decide whether your recruiting web application fits naturally into a recruiter’s day—or becomes “yet another tab.” Aim for a small set of high-impact connections first, and keep everything else behind a clean API layer so you can add more without rewriting core workflows.
Start with email because it directly supports outreach and creates valuable activity history.
Connect to Gmail and Microsoft 365 to:
Keep it simple: store message metadata (subject, timestamp, participants) and a safe copy of the body for search. Make logging explicit so recruiters can choose which threads belong in your system.
Calendar can wait if it threatens your timeline, but it’s a strong upgrade. With Google Calendar / Outlook Calendar you can create interview events, propose times, and record outcomes.
For early versions, focus on: creating events + adding attendees + writing the interview details back to the candidate pipeline stage.
Many agencies already use an ATS/CRM. Provide webhooks for key events (candidate created/updated, stage changed, interview scheduled) and document your REST endpoints clearly so partners can connect fast. Consider a dedicated page like /docs/api and a lightweight “integration settings” screen.
Job board posting and inbound applicants are powerful, but they introduce complexity (ad policies, duplicate applicants, source tracking). Treat them as phase 2:
Design your data model now so “source” and “application channel” are first-class fields later.
Your tech stack should optimize for shipping a reliable MVP quickly, while leaving room for better search and integrations later. Recruiting apps have two distinct needs: transactional workflows (pipelines, permissions, audit logs) and fast search/ranking (matching candidates to jobs).
For a modern JavaScript stack, React + Node.js (NestJS/Express) is a common choice: one language across frontend and backend, lots of hiring-market libraries, and straightforward integration work.
If you want faster CRUD and strong conventions, Rails or Django are excellent for building the core ATS/CRM workflows with fewer decisions. Pair either with a lightweight frontend (Rails views, Django templates) or React if you need richer UI.
If your main bottleneck is speed-to-prototype (especially for internal tools or early validation), a vibe-coding platform like Koder.ai can help you build an end-to-end MVP from a structured chat spec: core screens, workflows, and a baseline data model. Teams often use it to iterate quickly with planning mode, then export the source code when they’re ready to take the project in-house. Snapshots and rollback also make it easier to test matching changes without breaking the app for recruiters.
Use a relational database (usually PostgreSQL) as your source of truth. Recruiting data is workflow-heavy: candidates, jobs, stages, notes, tasks, emails, and permissions all benefit from transactions and constraints.
Model “documents” (resumes, attachments) as stored files (S3-compatible storage) with metadata in Postgres.
Start with Postgres full-text search for keyword queries and filters. It’s often enough for an MVP and avoids running another system.
When matching and search become a bottleneck (complex ranking, synonyms, fuzzy queries, high volume), add Elasticsearch/OpenSearch as a dedicated index—fed asynchronously from Postgres.
Maintain separate staging and production environments so you can test parsing, matching, and integrations safely.
Set up automated backups, basic monitoring (errors, latency, queue depth), and cost controls (log retention, right-sized instances). This keeps the system predictable as you add more recruiters and more data.
Matching gets better when you measure outcomes and capture the “why” behind recruiter decisions. The goal isn’t vanity metrics—it’s a tight loop where every shortlist, interview, and placement makes your recommendations more accurate.
Start with a small set of KPIs that map to agency performance:
Keep KPIs filterable by client, role type, seniority, and recruiter. That makes the numbers actionable instead of average-and-vague.
Add lightweight feedback right where decisions happen (on the match list and candidate profile): thumbs up/down, plus optional reasons (e.g., “salary mismatch,” “missing certification,” “location/visa,” “industry experience,” “poor response rate”).
Tie feedback to outcomes:
This lets you compare your scoring to reality and adjust weights or rules with evidence.
Create a few default reports:
Dashboards should answer “what changed this week?” in one screen, then allow drill-down. Make every table exportable to CSV/PDF for client updates and internal reviews, and keep the definitions visible (tooltip or /help) so everyone reads the same metrics the same way.
A recruiting app succeeds when it works reliably on real roles, real candidates, and real timelines. Treat launch as the start of learning—not the finish line.
Before inviting your first users, make sure the basics are not just built, but usable end-to-end:
You don’t need a massive test suite, but you do need the right tests:
Pilot with 1–3 agencies (or internal teams) that will give weekly feedback. Define success metrics up front: time-to-shortlist, fewer back-and-forth emails, and recruiter confidence in match explanations.
Run a two-week cadence: collect issues, fix the top blockers, and ship improvements. Publish changes in a lightweight changelog (a simple /blog post works well).
Once the core workflow is stable, prioritize:
As you add tiers (e.g., portal access, integrations, advanced analytics), keep packaging clear on /pricing.
Start with a closed-loop workflow that a recruiter can complete daily:
If a feature doesn’t directly support that loop (e.g., job board posting, complex automation, hiring manager portal), defer it to phase 2.
Pick 2–3 “top tasks” for each primary user and design around them.
If you include hiring managers in v1, plan the permission model and notification rules upfront.
Use measurable, workflow-linked metrics rather than “better matches.” Good starters:
These metrics also help you validate whether scoring changes improve outcomes.
Keep the core entities simple and model workflow as relationships:
This structure keeps matching, reporting, and audit trails consistent as features grow.
Separate what you store from what you search.
This prevents parsing errors from silently overwriting recruiter-approved data and improves match quality over time.
Start with transparent rules first, then add scoring.
Explainability is what makes recruiters trust (and correct) the system.
Model requirements in two buckets:
This avoids filtering out strong candidates for preferences while still rewarding better fits.
Build permissions into every read/write path (including search and matching):
Default to least privilege and add capabilities intentionally (e.g., “can export candidates”).
Treat compliance as product behavior, not a document.
Link policies from a simple page like /privacy and keep all sensitive actions auditable.
Launch with reliability and learning in mind:
Ship small changes frequently and keep a lightweight changelog (e.g., /blog).