NLP Engine v2.0 Active

Review CVs at
Machine Speed.

Stop keyword matching. Our engine parses, scores, and explains every candidate profile against your JD using advanced NLP.

INPUT_STREAM

Alex Chen
Senior Backend Engineer

7 years experience in building scalable APIs.

Tech Stack: Node.js, PostgreSQL, AWS.

Candidate
Alex Chen
Role Match
Senior Backend
Skills
Node, AWS, SQL

Why Traditional Screening
Fails Modern Hiring.

Our platform turns job descriptions and CVs into structured, machine-readable data. Then an AI scoring engine evaluates every candidate against your exact requirements – no guesswork, no manual triage.

HR teams drown in CVs

and still miss the best fits.

Volume overwhelms human capacity. You spend more time filtering noise than engaging with top talent.

Manual screening is inconsistent

biased, and impossible to scale.

Subjective criteria change daily. Fatigue leads to errors, and unconscious bias creeps into decision making.

Tools are just databases

they don't understand the JD.

Most ATS systems rely on simple keyword matching. They miss context, seniority, and transferable skills.

The Workflow

From JD to Shortlist in Three Steps

1

Add Your Job Description

  • Paste or upload your JD.
  • System parses skills, experience, & seniority.
  • HR adjusts weights (e.g. skills vs tenure).
2

Upload CVs

  • Bulk upload PDFs/DOCX or import via ATS.
  • Parsed into structured profiles.
  • Stored in searchable talent bank.
3

AI Scoring & Reasoning

  • 1-100 scale weighted scoring.
  • Detailed component breakdown (experience, fit).
  • Natural language explanation for every candidate.
Extracted Criteria
React.js
Node.js
5+ Years
Fintech
SKILL WEIGHTHigh
The Framework

Candidate Score Calculation

A structured, weighted scoring model evaluates candidates objectively based on CV data and job requirements.

Experience & Relevance

Weight: 35%

Depth, duration, and alignment of past roles with the JD (tools, technologies, seniority).

Company & Industry Fit

Weight: 15%

Match with target industries (e.g., Fintech) and company types (Startup vs Enterprise).

Job Tenure Stability

Weight: 10%

Consistency of employment, transitions, job-hopping patterns, and gaps.

Career Progression

Weight: 10%

Growth trajectory, promotions, increasing responsibilities, and role diversity.

Reasons for Job Changes

Weight: 30%

Validity and consistency of transitions where available in CV / profile data.

Live Scoring Engine
ID: CAND-4921
35%
Score
4.5/5
15%
Score
4/5
10%
Score
5/5
10%
Score
3.8/5
30%
Score
4.2/5
WEIGHTED SUMMATION
Total Candidate Score
87.5
High RelevanceStrong Fit
PARSING_CV_STRUCTURE... DONE
MATCHING_SEMANTIC_KEYWORDS... DONE
CALCULATING_ML_SIGNALS... ACTIVE

Turn Your CV Pile Into
Hiring Intelligence.

Go beyond the shortlist. Visualize your funnel, understand source quality, and audit every scoring decision.

Funnel Analytics

Track candidates from application to interview.

Source Quality

Compare LinkedIn vs Agencies vs Direct apply.

Audit Logs

Track who changed scoring weights and when.

Hiring Funnel
Applications1,240
Screened1,240
Qualified (>80%)142
Shortlisted24
Time to Screen
45s -98%
Top Source
LinkedIn
Encrypted
SOC 2 Ready
Zero Trust Architecture

Enterprise-Grade
Security by Design.

We process sensitive candidate data with the same rigor as a bank. Fully compliant, fully encrypted, and fully controlled by you.

AES-256 Encryption

Data is encrypted at rest and in transit using banking-grade standards.

SOC 2 Type II

Certified compliant infrastructure with continuous monitoring and auditing.

Role-Based Access

Granular permission controls for Recruiters, Hiring Managers, and Admins.

Data Residency

Host your data in US, EU, or APAC regions to meet local compliance.

Ready to deploy

Start Screening
Intelligently.

Contact the engineering teams that have reduced hiring time by 90% and improved candidate quality.