This document explains how hackajob leverages artificial intelligence (AI) to deliver a smarter, fairer, and more efficient recruitment experience for all users — prioritising transparency, user control, and ethical practices while maintaining a human-centric approach.

This document may be revised periodically to reflect updates in our internal processes or products.

About hackajob

hackajob is an AI-native hiring platform trusted by employers globally to find and engage qualified talent. Teams source directly or work with hackajob’s AI agents, which help identify best-fit candidates from our 1M+ private community, inbound applicants, and beyond.

On hackajob, hiring criteria is captured down to the finest detail, matching people based on skills, preferences, and real capabilities rather than keywords. Every AI-driven recommendation includes an explanation of why someone matches, so hiring teams remain fully informed and in control.

Our approach is grounded in transparency. Job seekers choose how they engage, and employers receive clear, auditable insight into how matches are generated. This ensures both sides benefit from a fair, relevant, and consistent hiring process.

AI at hackajob 

hackajob intelligence 

hackajob intelligence, our AI-native matching engine, uses algorithms to match highly relevant candidate profiles to jobs. Large Language Models (LLMs) optimise hiring processes with components that are primarily focused on natural language processing (NLP) and statistical learning algorithms rather than embodied AI or related hardware. 

hackajob intelligence combines NLP models, ranking algorithms, knowledge graphs, and LLM-powered summarisation. Models are versioned, monitored, and evaluated continuously for fairness, drift, and performance.

Where we use AI

1.  Source

Source is a tool within the platform that lets users directly source talent, shortlist candidates, and send interview requests from hackajob’s private community. Its match and search capabilities:

• Simplify time-intensive sourcing activities such as keyword searches and complex queries, allowing more focus on higher-value tasks.

• Match candidates to job opportunities accurately and fairly, based solely on their skills, experience, and preferences.

• Support informed decision-making when reviewing candidates with clear AI insights. 

Source uses AI in the following features:

🔍 Search prompt

Your top job requirements are captured and highlighted in a single prompt which updates filters. 

↗ Job description upload

LLMs help parse job descriptions, extracting essential elements like job title, location, salary, and key skills to quickly generate a search prompt and update filters.

💼 Skills & background filters
Filters are automatically generated from the essential requirements in the search prompt. They can be edited or removed using free text or voice. Each filter includes a tooltip explaining how the platform interprets that requirement when reviewing candidate profiles.

✨ AI profile summary
LLMs provide summaries on candidate profiles highlighting how well they align with job criteria based on their skills and experience, to help inform decision making.

✅ Evidencing skills & experience
Key skills and experience are extracted and interpreted from candidate profiles, then mapped to the Skills & Background filters using natural language understanding and a knowledge graph. This highlights the evidence in each profile that aligns with the hiring criteria, providing clear, explainable reasoning behind why each profile is a strong fit.

2. Archer

Archer is an automated sourcing tool within the platform that removes the need for job creation and outreach. Archer connects to a live job feed via API to automatically create jobs and update requirements. Then displays relevant roles to matched candidates and invites them to apply directly.

• Automates time-intensive sourcing tasks, allowing time to focus on reviewing inbound
  applications, rather than engaging new talent.

• Match candidates to job opportunities accurately and fairly, based solely on their skills,
  experience, and preferences, with clear AI insights explaining why they match.

• Drives more relevant inbound applications by inviting only matched candidates to apply, increasing the proportion that progress to interview.

Archer uses AI in the following features:

Skills & background filters

Archer uses all available job information to create jobs and update the filters used in Source. It automatically displays jobs to candidates who match every Skills & background filter, as well as other essential filters like location and right to work. Candidate matches are determined by extracting and interpreting skills and experience and mapping them to filters using natural language understanding and a knowledge graph.

3. Thena

Thena is a tool within the platform that helps users screen high volumes of direct applications for selected roles. Thena screens applicants in hackajob via a direct ATS integration, ranking them in order of best fit with explainable recommendations. This supports users to:

• Improve application screening efficiency by displaying the strongest applicants first.

• Confidently make progression decisions with clear, explainable insights into why someone may be suitable.

• Provide a fairer and faster candidate experience by reviewing and responding to
  applicants sooner.

Thena uses AI in the following features:

Skills & background filters

Filters are generated from the hiring criteria defined by the employer. They are populated using natural language understanding and can be edited or removed using free text or voice. Each filter includes a tooltip explaining how the platform interprets the requirement when reviewing applicant profiles. Applicants are then ranked based on how closely they match these filters.

Evidencing skills & background on profiles

Key skills and experience are extracted and interpreted from candidate profiles, then mapped to the Skills & Background filters using natural language understanding and a knowledge graph. This highlights the evidence in each profile that aligns with the hiring criteria. This provides clear, explainable reasoning behind why an applicant is or isn’t a strong fit.

Thena can be switched on or off at any time, giving users full control when automated
screening is applied to a role.

User control & transparency

We understand the importance of trust when using AI-powered tools, ensuring that AI acts as a supportive assistant, rather than dictating user activity. 

AI transparency

• AI-driven features such as the profile summary are highlighted or labelled, so users know when their actions are supported by AI. 

• Explanations on filters and profiles provide clear evidence and explain why candidates are recommended.

User control

• Users are always in control. While AI provides insights and suggestions, progression decisions (interview, shortlist, or reject) are made by users, ensuring full control at every step.

• Users can fine-tune candidate matches using filters or by adjusting the search prompt at any time, to ensure results align with their preferences.

Ethics & bias-mitigation

We’re committed to ensuring fairness, mitigating bias, and adhering to ethical AI standards.

Fairness

Bias audits

• Regular reviews are conducted to detect and reduce biases that might unfairly favour certain groups.

Objective candidate ranking

• Candidates are ranked exclusively on objective data inputs they provide, such as skills, experience, and preferences–excluding irrelevant factors like gender or ethnicity.

• AI evaluates candidates’ profiles based on skills, experience, and preferences, prioritising fair and consistent assessments over factors like formatting or writing style.

Diverse datasets

• AI is trained using diverse datasets representing various skills, job types, and industries, promoting fair and inclusive recommendations that reduce bias.

Safeguarding

Data protection

• Strict security measures ensure that no personally identifiable information (PII), such as gender or ethnicity is exposed to AI models, protecting candidate anonymity.

  • Diversity data is collected with explicit consent from candidates to share with employers and never inferred.

Data minimisation & isolation

• Only the minimum data required to generate match explanations or insights is processed by AI components

• Customer and candidate data is logically isolated and cannot be cross-referenced between clients.

AI interaction 

• AI tasks are performed in a secure and controlled environment, without access to sensitive user data.  Monitoring tools detect and prevent unauthorized access or data leakage.

• We do not use customer data, candidate profiles, job descriptions, or ATS data to train general-purpose models.

• Data is used only for real-time inference and evaluation.

Continuous monitoring

Evaluating fairness

• Regular monitoring and periodic human validation detect and address unintended biases. 

Performance

• The system is monitored in real-time to detect anomalies and ensure it operates within accepted thresholds, with immediate action taken to address issues and maintain reliability and fairness. 

User feedback

• We actively collect user feedback to improve AI processes, keeping recommendations unbiased and aligned with user needs.

Security and data protection practices

We are committed to safeguarding user data and maintaining high standards of security, privacy, and compliance across all regions in which we operate. Our approach aligns with internationally recognised frameworks and evolving global regulatory requirements.

ISO 27001 Certification

hackajob operates a certified ISO 27001 Information Security Management System (ISMS), covering the people, processes, and technologies that support our platform. This includes:

• Risk assessment and continuous improvement processes

• Strong access control and least-privilege governance

• Encryption of data in transit and at rest

• Secure software development practices (SSDLC)

• Regular internal and third-party audits

SOC 2 Type 1 Certified

hackajob maintains a SOC 2 Type I report, demonstrating that our key controls relating to security, availability, processing integrity, confidentiality, and privacy are suitably designed at a point in time. These controls include:

• Logical and physical access controls

• System and network monitoring

• Third-party/vendor risk management

• Incident detection and response

Global Privacy & Data Protection Compliance

hackajob adheres to applicable data protection and privacy laws in the regions where we operate and process data, including:

• UK GDPR and the Data Protection Act 2018

• EU GDPR 

• US state privacy laws, including CCPA/CPRA and similar enabling state-level regulations

Our privacy practices include:

• Clear allocation of Controller/Processor roles depending on the product and use case

• Data minimisation and purpose limitation

• Support for data subject/consumer rights (access, deletion, correction, opt-out)

• No use of customer or candidate data to train shared or general-purpose AI models

• Logical separation of customer data and strict internal access controls

• Secure deletion processes at the end of the relationship or upon request, in line with legal obligations