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Transform Your Hiring with AI-Powered Resume Intelligence

Screen hundreds of resumes in minutes, not days. EVASPA's intelligent evaluation platform automates candidate assessment, eliminates unconscious bias, and helps you find the perfect hire faster than ever before.

90%
Faster Screening
2x
Better Matches
100%
Bias-Free
! 92% AI

The Hiring Challenge

Traditional resume screening is broken. Here's why your team is struggling:

Time-Consuming Manual Review

Recruiters spend an average of 23 hours per week manually reviewing resumes. For high-volume roles, this can mean reviewing hundreds of applications, each getting only 6-8 seconds of attention before being sorted into yes/no/maybe piles. This superficial screening means qualified candidates slip through while unqualified ones advance.

  • Each resume receives only 6-8 seconds of review time
  • High-volume positions can receive 250+ applications
  • Recruiters juggle 30-40 open positions simultaneously
  • Manual tracking in spreadsheets leads to lost candidates

Unconscious Bias in Hiring

Human reviewers, despite best intentions, bring unconscious biases to the screening process. Research shows that identical resumes with different names receive significantly different callback rates based on perceived gender, ethnicity, or age. This leads to less diverse candidate pools and potential legal liability.

  • Name bias affects 64% of initial resume reviews
  • Education institution prestige creates halo effects
  • Employment gaps judged differently by gender
  • Age discrimination through graduation year assessment

Inconsistent Evaluation Criteria

Different recruiters evaluate the same resume differently, leading to inconsistent hiring decisions. One recruiter might prioritize years of experience while another values specific technical skills. This inconsistency becomes amplified when multiple team members are involved in the screening process.

  • Inter-rater reliability as low as 52% between reviewers
  • Criteria shift based on reviewer's mood and workload
  • Earlier candidates judged more critically than later ones
  • No standardized scoring methodology across team

Missing Qualified Non-Traditional Candidates

Keyword-based screening misses excellent candidates with transferable skills who don't use exact terminology. Career changers, self-taught professionals, and candidates from diverse backgrounds often have exactly the skills you need but describe them differently on their resumes.

  • 40% of qualified candidates filtered out by keyword mismatch
  • Career changers with transferable skills overlooked
  • Alternative education paths (bootcamps, self-taught) undervalued
  • International candidates using different terminology missed

Poor Candidate Experience

Candidates apply and hear nothing for weeks or months. 68% of candidates say that their experience during the hiring process reflects how a company treats its people. Slow, unresponsive screening processes damage your employer brand and cause top candidates to accept other offers.

  • Average time-to-first-response: 2-3 weeks
  • 52% of candidates accept other offers while waiting
  • Negative reviews on Glassdoor cite slow hiring process
  • Top talent lost to competitors with faster processes

Lack of Data-Driven Insights

Manual screening provides no analytics or learning over time. You can't track which criteria actually predict successful hires, can't identify bottlenecks in your screening process, and can't demonstrate compliance with EEOC regulations. Every hiring cycle starts from zero instead of building on past learnings.

  • No visibility into screening bottlenecks or drop-off points
  • Unable to correlate screening criteria with hire success
  • Compliance reporting requires manual data compilation
  • No machine learning from past hiring decisions

Meet EVASPA: Your AI-Powered Hiring Assistant

EVASPA transforms the chaos of resume screening into a streamlined, intelligent process that finds the best candidates faster while eliminating bias and improving candidate experience.

01

Intelligent Resume Parsing

Our AI doesn't just look for keywords. It understands context, recognizes transferable skills, and evaluates candidates based on what they can do, not just what buzzwords they use. Whether a resume is formatted as a PDF, Word document, or plain text, EVASPA extracts and structures all relevant information.

02

Smart Job Description Analysis

EVASPA reads your job descriptions using natural language processing to automatically identify must-have qualifications, preferred qualifications, and nice-to-have attributes. It understands nuance and context, not just exact keyword matches.

03

Comprehensive Candidate Scoring

Every candidate receives a detailed scorecard evaluating relevant experience, technical skills, educational background, career progression, and achievement indicators. Scoring is weighted based on your priorities and fully customizable for different roles.

04

Bias Detection & Mitigation

EVASPA actively identifies and eliminates bias from your screening process. Redact identifying information, flag potentially discriminatory requirements, ensure diverse candidate slates, and get full explainability for every scoring decision.

500 Applications Initial Resume Submission AI Processing 30 seconds 87 Qualified Meets Basic Requirements Deep Analysis Skills & Experience 23 Strong Matches High Compatibility Score Top 10 for Review

Comprehensive AI-Powered Features

Everything you need to transform your hiring process from start to finish

INTELLIGENT PARSING

Advanced Resume Understanding

EVASPA's parsing engine goes far beyond simple keyword extraction. Using state-of-the-art natural language processing and machine learning models, our system understands the semantic meaning and context of resume content.

Format-Agnostic Processing

Handles PDF, Word documents, plain text, HTML, and even scanned images with OCR. Regardless of how candidates format their resumes - single column, two-column, creative layouts, or traditional formats - EVASPA extracts accurate information.

Structured Data Extraction

Automatically identifies and categorizes: work experience with company names, job titles, dates, and responsibilities; education including institutions, degrees, majors, graduation dates, and GPA; skills both explicitly listed and inferred from job descriptions; certifications and licenses with issuing organizations and validity dates; achievements, publications, patents, and awards; projects and portfolio links; languages with proficiency levels; volunteer work and professional affiliations.

Contextual Understanding

Recognizes skill synonyms and related technologies (e.g., understands that React.js, ReactJS, and React all refer to the same framework). Infers technical capabilities from job titles and responsibilities even when not explicitly listed. Distinguishes between different contexts (Java the programming language vs. Java the island). Handles abbreviations, acronyms, and industry-specific terminology.

Timeline Construction

Builds comprehensive career timelines, calculating total years of experience, years in specific roles or industries, employment gaps and their durations, and career progression patterns including promotions and lateral moves.

EXPERIENCE Software Engineer 5 years EDUCATION BS Computer Science MIT, 2018 SKILLS React Python AWS Node.js AI OCR & Text Extraction NLP Processing & Entity Recognition Semantic Analysis & Data Structuring
Job Description Senior Frontend Developer Required: 5+ years React experience, TypeScript, state management, REST APIs Preferred: Next.js, GraphQL, AWS Nice to have: Mobile development MUST HAVE React (5+ years) TypeScript State Management REST APIs 40% PREFERRED Next.js GraphQL AWS 35% NICE TO HAVE Mobile Development 25% Weighted Scoring Model Must Have (40%) Preferred (35%) Nice to Have (25%)
SMART ANALYSIS

Job Description Intelligence

EVASPA doesn't require you to manually configure criteria for every job. Our NLP engine reads your job descriptions and automatically understands what you're looking for.

Automatic Requirement Classification

Distinguishes between must-have requirements (deal-breakers that candidates must possess), preferred qualifications (strong advantages that improve candidacy), and nice-to-have attributes (bonus skills that differentiate among qualified candidates). Recognizes implicit requirements mentioned in responsibilities sections. Identifies equivalent qualifications (e.g., "or equivalent experience" statements).

Customizable Weighting

Default intelligent weights based on requirement classification, but fully customizable per role or per organization. Set different importance levels for technical skills vs. soft skills vs. education vs. experience. Create role-specific templates that can be reused across similar positions. Adjust weights based on market conditions (e.g., prioritize availability over perfect skill match in competitive markets).

Bias Detection in Job Descriptions

Flags potentially discriminatory language before it reaches candidates (e.g., "recent graduate," "digital native," "energetic"). Identifies unnecessarily gendered language (e.g., "he will be responsible for"). Warns about requirements that may exclude qualified diverse candidates (e.g., "must have bachelor's degree" when experience could substitute). Suggests more inclusive alternative phrasings.

Skill Taxonomy Understanding

Recognizes technology ecosystems and related skills (knowing React implies JavaScript knowledge). Understands skill progression and seniority levels (Junior vs. Senior vs. Staff vs. Principal). Maps similar skills across different domains (SQL Server, PostgreSQL, MySQL all indicate database expertise). Identifies complementary skill combinations that indicate well-rounded candidates.

COMPREHENSIVE EVALUATION

Multi-Dimensional Candidate Scoring

EVASPA evaluates candidates across multiple dimensions, providing a holistic view of each applicant's qualifications and potential fit.

Relevant Experience Analysis

Total years in similar roles, weighted by recency (more recent experience scored higher). Industry background matching (e.g., fintech experience for fintech roles, healthcare for healthcare). Company size and type experience (startup, scale-up, enterprise, public sector). Technology stack overlap with your current environment. Domain expertise in relevant areas (e.g., payments, security, ML/AI). Remote work experience for remote positions.

Technical Skills Assessment

Explicitly mentioned skills with proficiency indicators (years of use, certification levels). Inferred skills from job responsibilities and projects. Programming languages, frameworks, tools, and platforms. Breadth vs. depth analysis (specialist vs. generalist profiles). Currency of skills (actively used vs. outdated). Skill acquisition rate and learning agility indicators.

Educational Background Evaluation

Degrees, majors, and relevance to the role. Institution prestige (when relevant, but with bias awareness). GPA and academic honors (when provided). Relevant coursework and academic projects. Alternative education paths: bootcamps, online courses, self-study. Continuing education and professional development. Advanced degrees and specializations.

Career Progression Patterns

Promotions and title advancement over time. Increasing scope of responsibility. Leadership and management experience. Lateral moves that indicate breadth-building. Job stability vs. job-hopping patterns. Career trajectory alignment with the opportunity you're offering. Entrepreneurial experience and side projects.

Achievement Indicators

Quantified results and impact metrics (revenue growth, cost savings, performance improvements). Leadership roles in professional organizations. Awards, recognition, and honors. Publications, patents, and speaking engagements. Open source contributions and community involvement. Notable projects and their outcomes. Recommendations and endorsements.

Candidate Scorecard Sarah Chen - Senior Frontend Developer 92 Overall Match Relevant Experience 90% 8 years React, 6 years TypeScript Technical Skills 95% All required + Next.js, GraphQL Educational Background 85% BS Computer Science, Stanford Career Progression 95% 2 promotions in 4 years Achievement Indicators 80% Led team of 6, OSS contributor STRONG RECOMMENDATION - SCHEDULE INTERVIEW
❌ Traditional Screening John Smith Harvard University, 2018 123 Main St, San Francisco Name bias School prestige bias Age inference (grad year) Location bias Biased Decision ✓ EVASPA Screening Candidate #47 BS Computer Science 5 years experience Skills-based evaluation Experience quality Achievement focus Objective criteria Fair Decision Bias Mitigation Features Information Redaction Optional removal of names, locations, graduation years, and other identifying data Diverse Slate Guarantee Ensures shortlists include candidates from diverse backgrounds when qualified Explainable AI Clear reasoning for every score, showing exactly which criteria influenced decisions Compliance Reporting EEOC/OFCCP reporting with adverse impact analysis and documentation
FAIR HIRING

Advanced Bias Detection & Mitigation

EVASPA is built from the ground up to identify and eliminate unconscious bias from your hiring process, ensuring fair evaluation for all candidates.

Configurable Information Redaction

Choose which identifying information to redact during initial screening: candidate names (to eliminate name-based bias), graduation years (to prevent age discrimination), geographic location (to avoid location bias), educational institution names (to reduce prestige bias), gender indicators, photos and images. Redacted information can be revealed for later stages of the hiring process once candidates have been objectively evaluated.

Job Description Bias Flagging

Automatically identifies potentially biased or exclusionary language in job descriptions before they're posted. Flags age-related terms ("digital native," "recent graduate," "energetic"). Identifies gendered language and pronouns. Warns about unnecessarily restrictive requirements. Suggests more inclusive alternatives. Checks for language that may discourage diverse applicants.

Diverse Candidate Slate Assurance

Ensures that shortlists include qualified candidates from diverse backgrounds when available in the candidate pool. Monitors for adverse impact in screening decisions. Provides transparency into demographic distribution at each stage of the funnel. Alerts hiring teams when screening criteria may be disproportionately affecting certain groups. Does not use quotas or preferential treatment - simply ensures qualified diverse candidates aren't overlooked.

Explainable AI & Audit Trails

Every scoring decision includes detailed explanation of contributing factors. Shows which qualifications increased or decreased scores and by how much. Provides full audit trail of all screening decisions. Enables compliance officers to verify fair and consistent evaluation. Supports EEOC and OFCCP reporting requirements. Documents the business necessity of all screening criteria.

Continuous Bias Monitoring

Analyzes screening outcomes over time to detect patterns of bias. Compares advancement rates across demographic groups. Identifies criteria that may have disparate impact. Provides recommendations for adjusting screening criteria to improve fairness. Learns from your hiring decisions to become more equitable over time.

Additional Advanced Capabilities

Knockout Question Evaluation

Automatically evaluate responses to knockout questions (e.g., "Do you have legal authorization to work in the US?", "Can you work evening shifts?"). Parse natural language responses to yes/no questions. Score applicants based on their answers to custom screening questions. Flag candidates who don't meet absolute requirements early in the process.

Cultural Fit Assessment

When you provide company values and cultural attributes, EVASPA can evaluate alignment. Analyzes language used in resumes and cover letters for value alignment. Identifies indicators of work style preferences (collaborative vs. independent, fast-paced vs. methodical). Flags potential cultural mismatches early. Note: Used as one factor among many, not as a primary filter.

Success Prediction Modeling

Learns from your past hiring decisions and outcomes. Identifies patterns in successful hires vs. unsuccessful hires or early departures. Predicts likelihood of candidate success based on similarity to your best performers. Provides confidence scores alongside match scores. Continuously improves as you provide feedback on candidate quality.

Flight Risk Assessment

Analyzes career patterns to identify potential retention concerns. Flags frequent job changes (job-hopping patterns). Identifies candidates likely seeking short-term roles. Notes candidates in active job-seeking mode (recently updated LinkedIn, multiple applications). Helps prioritize candidates likely to stay long-term when retention is important.

Salary Expectation Estimation

Estimates likely salary expectations based on current compensation, years of experience, role level and seniority, geographic location, and industry standards. Helps prioritize candidates within budget. Flags candidates who may be overqualified and expecting higher compensation. Supports compensation planning and budget allocation.

Commute Time Calculation

For on-site or hybrid roles, estimates commute times from candidate locations to your office. Flags candidates with excessively long commutes who may decline offers or experience burnout. Supports local candidate prioritization when proximity is important. Considers public transit and driving options. Helpful for discussing remote/hybrid flexibility during interviews.

?

Interview Question Generation

Creates customized interview questions based on each candidate's background. Generates behavioral questions targeting specific experiences mentioned in resumes. Creates technical questions appropriate to the candidate's claimed skill level. Suggests probing questions for resume gaps or unclear statements. Provides follow-up questions to verify achievements and claims.

Batch Processing for High-Volume Hiring

Process hundreds or thousands of resumes simultaneously. Upload bulk resume files (zip archives of PDFs, folders of Word documents). Maintain consistent evaluation criteria across large candidate pools. Generate comparative analyses and ranked lists. Export results for further analysis. Ideal for campus recruiting, retail hiring, seasonal hiring spikes, and mass recruitment campaigns.

How EVASPA Works

From job posting to shortlist in three simple steps

1

Define Your Requirements

Either paste your job description and let EVASPA automatically extract requirements, or manually configure scoring criteria and weights. Choose which bias mitigation features to enable. Set knockout questions if needed. Create custom scoring rubrics or use our intelligent defaults.

Paste job description... Auto-Extract Manual Setup Enable bias detection Redact identifying info
2

Upload Resumes

Upload resumes in bulk via file upload (PDF, Word, TXT), direct integration from your ATS, email forwarding to a dedicated EVASPA address, or API integration for programmatic submission. EVASPA processes all formats and layouts, extracting structured data from each resume within seconds.

resume1.pdf resume2.docx resume3.pdf
3

Review Ranked Results

Receive ranked candidate lists with detailed scorecards for each applicant. Review AI-generated resume summaries highlighting key qualifications. Compare candidates side-by-side. Access full explainability for every score. Export shortlists to your ATS or download as CSV/PDF. Schedule interviews with top candidates directly from the platform.

1. Sarah Chen 92% match 92 2. Michael Rodriguez 88% match 88 3. Jessica Park 85% match 85 Schedule Interviews

Time Savings Comparison

Traditional Manual Screening

2-3 weeks
  • Resume collection: 3-5 days
  • Initial review: 1-2 weeks
  • Shortlist creation: 2-3 days
  • Interview scheduling: 3-5 days

EVASPA Automated Screening

2-3 hours
  • Resume upload: 5 minutes
  • AI processing: 30 seconds - 2 minutes
  • Review ranked results: 30-60 minutes
  • Interview scheduling: 30-60 minutes
90% Time Savings

Seamless Integration with Your Hiring Stack

EVASPA works with the tools you already use, syncing candidates and updating records automatically

Native ATS Integrations

Connect EVASPA to your Applicant Tracking System with just a few clicks. Our integrations automatically sync candidate data, update application statuses, and push screening results back to your ATS.

Greenhouse

Bi-directional sync with Greenhouse ATS. Candidates automatically import, scores sync to scorecards, and interview scheduling integrates seamlessly.

Lever

Real-time integration with Lever. Resume parsing results and candidate scores sync automatically to candidate profiles and feedback forms.

Workday

Enterprise-grade integration with Workday Recruiting. Supports complex workflows, custom fields, and multi-stage approval processes.

BambooHR

Full integration with BambooHR's applicant tracking features. Candidate evaluations and hiring recommendations sync to hiring reports.

Integration Capabilities

  • Automatic Candidate Sync: New applications automatically flow into EVASPA for screening
  • Bi-Directional Updates: Status changes in EVASPA reflect in your ATS and vice versa
  • Scorecard Push: Detailed candidate scores and evaluations appear as feedback in your ATS
  • Custom Field Mapping: Map EVASPA data to your ATS's custom fields
  • Webhook Support: Trigger EVASPA screening based on ATS events
  • SSO Integration: Single sign-on with your existing authentication system
Greenhouse Lever Workday BambooHR EVASPA AI Screening Platform Candidates In Scores Out Status Sync Analytics REST API Custom Integration

Developer-Friendly API

For custom integrations or building EVASPA into your own workflows, our comprehensive REST API provides programmatic access to all platform features.

Resume Submission API

Submit resumes programmatically via API. Supports base64-encoded file upload, public URL ingestion, and raw text submission.

Candidate Scoring API

Retrieve candidate scores and detailed evaluations. Access full scorecards with breakdowns by criteria.

Job Configuration API

Create and manage job screening configurations programmatically. Set criteria, weights, and screening rules via API.

Webhook Notifications

Receive real-time notifications when screening completes, high-scoring candidates are found, or status changes occur.

Ready to Transform Your Hiring Process?

Get started with EVASPA today and experience the future of resume screening. Our team will set up your account, configure your first screening criteria, and train your team on the platform.

Email

hello@evaspa.app

Phone

(614) 999-2449

Response Time

Within 24 hours

Support

24/7 Chat Available

Company Information

EVASPA, INC.

819 S Flower St

Los Angeles, CA 90017

United States

Get Started Today

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