Despite unprecedented access to candidate data, organizations struggled to efficiently identify and attract the right talent. As a mission-driven company committed to transforming hiring, Hattl aimed to make recruitment precise, unbiased, and exceptionally effective.
Today's recruiting process accompanies by:
- Excessive Time and Resources Wasted: Teams spent weeks manually reviewing resumes, wasting valuable resources.
- Poor candidate relevance: Up to 90% of applicants were unqualified or poorly matched.
- High risk of bad hires: Traditional methods often resulted in mismatched hires, causing significant costs and turnover.
Hattl embarked on a mission to revolutionize hiring through advanced AI technology, provided by Outter's founding team - making recruitment smarter, faster, and reliably precise.
The Challenge:
Despite vast amounts of candidate data, hiring remains inefficient, error-prone, and slow. The problem isn’t just about digitalization—it’s about fundamentally rethinking how hiring should work.
Job Descriptions Are Vague, Generic, and Misaligned with Hiring Needs:
- Most hiring managers don’t know how to write clear job descriptions.
- Job postings are often copy-pasted from old listings or generic templates that fail to capture real job requirements.
- Mismatched expectations – Hiring teams think they need X, but the actual job requires Y.
- Result: Companies attract the wrong candidates, leading to months of wasted hiring time.
Assessments Are Static, Non-Specific & Easily Gamed
- Traditional assessments are one-size-fits-all, failing to measure the actual skills needed for a job.
- Assessments evaluate candidate abilities to answer questions correctly, not their real skills and capabilities.
- Companies waste months onboarding weak hires, only to realize later that they made a bad hiring decision.
Resume-Based Screening Ignores Real Skills & Fit
- Keyword matching is outdated – resumes list skills, but don’t measure competency.
- Recruiters & hiring managers focus on prestige (degrees, previous companies) rather than abilities.
- Soft skills, cultural fit, and job-specific performance are ignored—leading to bad hires.
Hattl aims not to just digitize hiring, but fundamentally reinvent it with AI support from Outter team.
"You don’t have anyone looking at people’s names or what school they went to... Rather, you have the ability to look at skills, paired with an assessment, to ensure you have a candidate with the right qualities for the role",
Global Operations Executive
The Solution: AI-Powered Job Matching & Candidate Assessments
Step 1: AI-Generated Job Descriptions – The Right Role, Defined Correctly
Model: Large Language Model (LLM) fine-tuned for role-specific job description generation
Technology: NLP-based Job Ontology + Outter Content Generation Module
How It Works:
- AI learns from millions of job postings & real-world hiring data – creating precise, skill-based job descriptions.
- Understands hiring intent – AI refines generic, vague listings into clear, actionable job profiles.
- Automated benchmarking – AI compares roles across industries to ensure competitive salary & requirements.
Step 2: AI-Generated Role-Specific Assessments – Skills, Not Buzzwords
Model: LLM-powered Adaptive Assessment Generator + Gamified Skill Testing
Technology: Outter Content Generation Module
How It Works:
- For every job, AI dynamically creates a custom gamified assessments – tailored to the role’s real-world requirements.
- Assessments are interactive & scenario-based, measuring problem-solving, technical ability, and soft skills.
- AI continuously improves test quality – learning from previous hiring data and role success rates.
Step 3: AI-Powered Candidate Matching & Shortlisting
Model: Embeddings-Based Candidate Scoring ModelTechnology: Outter Matching Module
How It Works:
- AI reads resumes like a hiring manager, ranking candidates based on true job fit.
- Beyond keyword matching – AI assesses real experience, skills, cultural fit, and job readiness.
- Smart ranking & auto-shortlisting – hiring teams only interview the top 10% of candidates.
"The opportunity is real and exciting, especially when it comes to freeing up recruiters to spend more time on relationship building and widening talent pools...",
Managing Director and Partner, BCG
Tech Stack:
- Custom talent embeddings models trained on extensive hiring datasets.
- LLM-driven semantic matching & candidate assessments.
- Outter content generation module - for creating tailored assessments for each opening at scale
- Django 5.1 with PostgreSQL 16 for scalable backend
- Next.js with Material UI for user-friendly interfaces
The Results:
- 95% Reduction in Screening Time — Weeks of manual CV reviews now replaced by instant AI-driven matching.
- 80% Increase in Candidate Quality — Recruiters exclusively interview top-tier talent.
- 3× Faster Time-to-Hire — AI assessments remove hiring inefficiencies.
- Automated Job-Specific Assessments — Dynamic AI-generated tests customized for each role.
"For the first time, we hire based on skill, not guesswork. AI assessments and live-ranked shortlists have changed everything.",
HR & BP, Bond Recruitment
