Build Trust, Hire Fast: Your Startup's Guide to Privacy-First AI recruitment with Federated Learning

As a startup founder, you need an amazing team, fast. AI can help you find top talent, but it comes with a big problem: more data means more privacy risk. 62% of job seekers worry about how companies use their data, and fines for data breaches are soaring. Traditional AI, which gathers all sensitive candidate data in one place, is a huge risk for your brand and budget. How do you use AI without risking privacy? The answer is Federated Learning.

This game-changing approach to privacy-first AI lets your systems learn from many different data sources. Crucially, it does this without ever collecting or exposing raw candidate information. In this guide, you'll discover how Federated Learning works, how to use it in recruitment, and how to implement this cutting-edge technology. Build an ethical, efficient, and trustworthy hiring engine for your startup. Ready to transform how you hire? Let's begin.

The Urgent Need for Privacy-First AI in Startup Recruitment

Now that we've introduced Federated Learning, let's explore why this privacy-first approach is essential for any startup aiming to succeed in today's competitive talent market. AI is quickly changing how we hire, but with great power comes great responsibility—especially when handling sensitive candidate data.

Why Data Privacy is Your Startup's Competitive Edge

In the race for top talent, trust is your most valuable asset. Imagine losing a stellar candidate because they worry about how your company handles their personal information. This isn't just a fear; it's a real concern. A 2024 survey shows that 62% of job seekers are concerned about how companies use their personal data during recruitment, affecting their willingness to apply (Glassdoor Candidate Privacy Report 2024). For a startup, where every hire can make or break your success, building this trust from day one is crucial.

Focusing on startup hiring data protection isn't just about following rules; it's a key way to stand out. By using privacy-first AI, you show candidates you respect their data. This builds a positive employer brand that attracts the best. This proactive step also greatly lowers the risk of expensive data breaches and harm to your reputation. Startups using privacy-enhancing technologies like federated learning for talent acquisition are projected to see a 15-20% higher candidate trust score and a 10% reduction in data breach risks by 2025 (CB Insights Future of HR Tech Report 2024). The market clearly demands this: The global market for privacy-enhancing technologies (PETs) is expected to grow from $2.9 billion in 2023 to $11.6 billion by 2028 (MarketsandMarkets Privacy-Enhancing Technologies Market Report 2023), driven by new data rules and consumer demand for privacy.

As Josh Bersin, a global industry analyst, wisely states, "Federated learning isn't just a technical solution; it's a strategic imperative for building trust in AI-powered recruitment. Startups that embrace it early will differentiate themselves as ethical leaders in talent acquisition." (Josh Bersin, HR Tech Conference 2024 Keynote). Take TalentGuard, a hypothetical YC startup, for example. They used federated learning for their AI skill assessment platform. By processing anonymized skill data locally and only sharing aggregated model updates, they boosted candidate trust and client adoption without ever seeing sensitive candidate project details.

The Limits of Traditional AI with Sensitive Candidate Data

AI is rapidly changing HR—with 70% of organizations expected to use some form of AI in their HR processes by 2026 (Gartner Hype Cycle for HR Technology 2025). Yet, a big challenge remains: data privacy and ethical AI are top concerns for 45% of these implementations (Gartner Hype Cycle for HR Technology 2025). Traditional AI hiring privacy models often need to centralize huge amounts of sensitive candidate data. This includes resumes, performance scores, assessment results, and even chat logs, all in one database for training.

This centralized method, while easy for model development, creates a single weak point. It greatly increases the risk of cyber threats. It makes confidential talent acquisition a risky balancing act, raising the chance of breaches, misuse, and potential algorithmic bias if not managed carefully. For startups, with fewer resources and often less mature security, this risk is even higher. As Jeanne Meister, EVP at Future Workplace, notes, "For early-stage companies, data privacy isn't a luxury; it's a foundational element of their brand and compliance strategy." (Jeanne Meister, Forbes HR Tech Column, October 2024).

Consider HealthHire, a hypothetical health tech startup that recruits for the healthcare sector. They faced strict HIPAA and GDPR rules. By using federated learning, they built an AI model that predicted candidate fit based on anonymized professional profiles. The data never left the healthcare organizations. This allowed them to offer highly accurate, compliant candidate recommendations, cutting down hiring time for key roles while keeping strict data privacy recruiting standards.

Key Takeaways for Founders:

• Prioritize Privacy Early: Build privacy into your AI recruitment strategy from the start.
• Build Trust, Attract Talent: Use privacy-first methods to make your startup stand out and attract top candidates.
• Mitigate Risk: Reduce your chances of data breaches and compliance fines by decentralizing how you handle sensitive data.

Continue to What is Federated Learning and How It Works for Recruitment

What is Federated Learning and How It Works for Recruitment

Building on the need for privacy and trust, let's explore a groundbreaking approach that's changing how AI can be used responsibly in recruitment: Federated Learning. For startups, this isn't just a new technology; it's a strategic advantage in the race for top talent.

Understanding the Core Principles of Federated Learning

At its core, Federated Learning (FL) is a machine learning method that trains AI models using data from many different, separate sources. Imagine you want to build a powerful AI model for matching skills or finding the right candidate. But you can't—or shouldn't—gather all sensitive candidate data from various companies or individual profiles into one central place.

With FL, the raw data never leaves its local device or organization. Instead, a central AI model is sent to many local devices (like a company's HR system, a candidate's browser, or an ATS). Each local device then trains the model using its own private data. The key is that only aggregated model updates—not the raw data itself—are sent back to the central server. These updates are then combined to improve the global model, which is then sent out again for another round of training. This cycle lets the AI learn from a huge, diverse dataset without ever risking individual data privacy. It's a powerful idea for Federated Learning Recruitment, allowing shared intelligence without centralizing data.

By 2026, 70% of organizations will have implemented some form of AI in their HR processes, with data privacy and ethical AI being top concerns for 45% of these implementations (Gartner Hype Cycle for HR Technology 2025).

How Federated Learning Boosts Data Privacy in Hiring

For startups navigating the tough talent market, FL offers a crucial edge. It protects individual data privacy while using collective intelligence—a must-have for today's candidates. A 2024 survey indicates that 62% of job seekers are concerned about how companies use their personal data during the recruitment process, impacting their willingness to apply (Glassdoor Candidate Privacy Report 2024).

This makes it a strategic necessity for building trust in AI-powered recruitment. As Josh Bersin, a global industry analyst, rightly points out, "Federated learning isn't just a technical solution; it's a strategic imperative for building trust in AI-powered recruitment. Startups that embrace it early will differentiate themselves as ethical leaders in talent acquisition." (Josh Bersin, HR Tech Conference 2024 Keynote).

Consider TalentGuard, a hypothetical YC startup focused on AI skill assessment. They used federated learning to train their skill matching algorithms. Instead of collecting raw code samples or detailed project histories, they processed anonymized skill data locally on client devices. Only aggregated model updates were shared. This allowed them to improve matching accuracy across different tech stacks without ever seeing sensitive candidate project details. This greatly boosted candidate trust and client adoption, showing the power of secure AI recruitment through privacy-enhancing technologies.

By using FL, startups can build more accurate and fair AI models for tasks like candidate matching, skill assessment, and even predicting cultural fit. All this happens while following strict data protection rules like GDPR and CCPA. This not only lowers big risks but also builds your reputation as a privacy-first employer.

Key Takeaways for Founders:

• Decentralize Data, Centralize Intelligence: Train powerful AI models without ever moving sensitive candidate data.
• Build Unprecedented Trust: Make your startup stand out by showing a commitment to candidate privacy, attracting top talent who are increasingly wary of data usage.
• Future-Proof Your Recruitment AI: Embrace technology that aligns with evolving data rules and ethical AI standards, lowering compliance risks.
• Gain a Competitive Edge: Use collective insights from diverse talent pools securely, leading to more accurate and unbiased hiring decisions.

Continue to Why Federated Learning is Essential for Your Startup's Recruitment AI

Why Federated Learning is Essential for Your Startup's Recruitment AI

For your startup, using AI in recruitment isn't just about being efficient; it's about building a foundation of trust and innovation. To future-proof your AI recruitment and gain a competitive edge, the Federated Learning Recruitment benefits are a critical differentiator. It's not just a technical upgrade; it's a smart strategic move that tackles the main concerns of today's talent market.

Building Unprecedented Candidate Trust and Engagement

In a world where data privacy is crucial, candidates are increasingly careful about how companies handle their personal information. A 2024 survey indicates that 62% of job seekers are concerned about how companies use their personal data during the recruitment process, impacting their willingness to apply (Glassdoor Candidate Privacy Report 2024). This concern directly affects your ability to attract talent. By using privacy-enhancing technologies (PETs) like federated learning, your startup can clearly show its commitment to AI hiring privacy.

Startups adopting privacy-enhancing technologies like federated learning for talent acquisition are projected to see a 15-20% higher candidate trust score (CB Insights Future of HR Tech Report 2024). Imagine how this boosts your employer brand! For example, a hypothetical startup like SkillSync, which connects freelancers with projects, could use federated learning to improve its recommendation engine. Freelancers train local models on their portfolios, sending only aggregated updates. This keeps their sensitive data private. This builds a reputation for data protection, attracting top talent who value their privacy.

Ensuring Regulatory Compliance and Mitigating Risk

Data breaches are more than just a PR nightmare; they can be financially devastating, especially for new companies. ...and a 10% reduction in data breach risks by 2025 (CB Insights Future of HR Tech Report 2024). Federated learning greatly reduces these risks by keeping sensitive candidate data decentralized. As Jeanne Meister, Executive Vice President at Future Workplace, notes, "For early-stage companies, data privacy isn't a luxury; it's a foundational element of their brand and compliance strategy. Federated learning offers a pathway to advanced AI capabilities while mitigating the significant risks associated with handling personal data." (Forbes HR Tech Column, October 2024).

This approach is vital for data privacy recruiting, helping your startup navigate complex and ever-changing rules like GDPR and CCPA. Consider HealthHire, a hypothetical health tech startup recruiting for the healthcare sector. Facing strict HIPAA compliance, they used federated learning to train an AI model on anonymized professional profiles across many healthcare organizations. Data never left the organizations. This allowed HealthHire to offer highly accurate, compliant candidate recommendations, showcasing truly secure AI recruitment.

Gaining a Competitive Edge in Talent Acquisition

Beyond trust and compliance, federated learning gives your AI recruitment superior intelligence. Dr. Rumman Chowdhury, a leading voice in responsible AI, highlights this, stating, "Federated learning allows us to leverage collective intelligence from diverse talent pools without centralizing sensitive candidate data, a game-changer for fair and unbiased hiring." (AI Ethics in Recruitment Summit 2024). This means your AI can learn from a wider, more diverse dataset without ever needing to gather raw, private information. This leads to more accurate and fair hiring decisions.

Josh Bersin, a global industry analyst, emphasizes the strategic advantage: "Federated learning isn't just a technical solution; it's a strategic imperative for building trust in AI-powered recruitment. Startups that embrace it early will differentiate themselves as ethical leaders in talent acquisition." (HR Tech Conference 2024 Keynote). By securely using collective intelligence, your startup can develop more advanced predictive models for candidate fit, skill matching, and even cultural alignment, all while maintaining the highest privacy standards.

Key Takeaways for Your Startup:

• Prioritize Trust: Candidate privacy is a top concern. Federated learning directly addresses this, boosting your employer brand.
• Mitigate Risk Early: Use federated learning to proactively reduce data breach risks and ensure compliance with new data regulations.
• Innovate Responsibly: Use this technology to get richer, more diverse data insights for your AI. This leads to fairer and more effective hiring, without compromising privacy.

Embracing federated learning isn't just about adopting a new technology; it's about building an AI recruitment system that is ethical, secure, and inherently more powerful.

Continue to How to Implement Federated Learning in Your Recruitment AI Strategy

How to Implement Federated Learning in Your Recruitment AI Strategy

You now understand how federated learning can transform your AI recruitment by making it ethical and powerful. The next question for any founder is: "How do we actually do this?" Implementing federated learning might seem complex, but with a smart, phased approach, your startup can successfully add this privacy-enhancing technology to your AI hiring privacy strategy.

Identifying Key Use Cases for Federated Learning in Recruitment

The best way to start your Federated Learning Recruitment implementation is with a focused pilot project. Don't try to change your entire AI system at once. Instead, find a specific, high-impact use case where data privacy is critical and a decentralized approach offers clear benefits.

For example, improve your skill matching algorithms. Instead of gathering huge amounts of sensitive candidate data (like detailed project histories or performance reviews) in one place to train your models, use federated learning. Remember TalentGuard (our hypothetical YC startup)? They improved developer skill matching by processing anonymized skill data locally on client devices. Only aggregated model updates were shared, greatly boosting candidate trust and client adoption. This method lets your AI learn from diverse skill sets across different companies or candidate profiles without ever directly accessing their raw, personal data. A 2024 survey indicates that 62% of job seekers are concerned about how companies use their personal data during the recruitment process, impacting their willingness to apply (Glassdoor Candidate Privacy Report 2024).

A Step-by-Step Guide for Startups

Once you've chosen your pilot use case, here’s a practical roadmap for integrating federated learning:

1. Start Small with a Pilot: As mentioned, pick a specific, manageable problem. Good starting points include skill matching, predicting candidate fit for a certain role, or even detecting bias in initial screening. This lets you test the technology, gather insights, and improve without a huge upfront investment.
2. Leverage Open-Source Frameworks: To make development easier and faster, don't start from scratch. Use established open-source federated learning frameworks. Tools like TensorFlow Federated or PySyft provide strong foundations for building custom federated solutions. These frameworks handle much of the complex technical work, letting your engineering team focus on the specific AI logic for recruitment.
3. Focus on Data Minimization: Even with a federated setup, using the least amount of data possible is key. Make sure only the absolutely necessary data is used for local model training. This means carefully designing your data to include only relevant features. Where possible, use techniques like differential privacy to add noise to data, further hiding individual contributions while keeping overall patterns. This strengthens your secure AI recruitment steps.
4. Educate Stakeholders on Privacy Benefits: Federated learning is a powerful differentiator, but its benefits aren't always clear to non-technical people. Clearly explain how this approach protects candidate privacy, reduces data breach risks, and builds trust. Tell hiring managers, candidates, and investors that your AI learns from collective intelligence without ever seeing individual sensitive data. Startups adopting privacy-enhancing technologies like federated learning for talent acquisition are projected to see a 15-20% higher candidate trust score and a 10% reduction in data breach risks by 2025 (CB Insights Future of HR Tech Report 2024). This transparency is vital for gaining adoption and positioning your platform as an ethical leader, as Josh Bersin calls it a "strategic imperative for building trust." (Josh Bersin, HR Tech Conference 2024 Keynote).

By following these steps, your startup can confidently begin its federated learning journey, building AI recruitment that is not only intelligent but also inherently trustworthy and compliant.

Continue to Overcoming Challenges and Common Mistakes in Federated Learning for Startups

Overcoming Challenges and Common Mistakes in Federated Learning for Startups

We've seen that federated learning is vital for building trust. But like any advanced technology, using it in a startup brings its own challenges. Overcoming these Federated Learning Recruitment challenges is key to using its full power without making common AI hiring privacy mistakes.

Addressing Implementation Complexity and Resource Constraints

Federated learning needs specialized AI/ML engineering skills. These can be hard to find and expensive for early-stage startups, leading to significant Federated Learning Recruitment challenges. Managing distributed training and secure data aggregation also puts a strain on limited computing resources.

Actionable Steps:

• Start Small with a Pilot: Begin with a clear, smaller project. For instance, TalentGuard, a hypothetical YC startup, successfully tested federated learning for anonymized skill assessment. They improved accuracy without handling raw code.
• Leverage Open-Source Frameworks: Tools like TensorFlow Federated or PySyft greatly cut down development time and complexity.
• Partner with Experts: Hire AI ethics or privacy consultants. They can help design privacy-first systems and fill expertise gaps, offering strong startup hiring data protection solutions from the start.

Ensuring Model Performance and Mitigating Bias

A common hurdle is making sure the global AI model works well and performs optimally with data that is spread out and varied. Also, AI hiring privacy mistakes can happen if biases in local datasets are not managed carefully, potentially leading to unfair hiring.

Actionable Steps:

• Iterative Development and Monitoring: Use an agile approach. Constantly check model performance and look for potential biases. SkillSync, a hypothetical SaaS, improved its recommendation engine by letting freelancers train local models. This made recommendations more relevant while keeping data private.
• Focus on Data Minimization: Even in a federated setup, use only the data absolutely needed for local model training. This further boosts privacy and lowers bias risk.
• Educate and Communicate: Clearly explain privacy benefits to candidates. A 2024 survey indicates that 62% of job seekers are concerned about how companies use their personal data during the recruitment process, impacting their willingness to apply (Glassdoor Candidate Privacy Report 2024). Clear communication significantly builds candidate trust. Learn more about communicating privacy benefits

Navigating Regulatory Compliance and Stakeholder Communication

Dealing with changing data privacy rules (like GDPR and CCPA) and proving federated learning's compliance can be tricky. Explaining the "privacy-first" benefits to non-technical people, like investors or hiring managers, is also a challenge.

Actionable Steps:

• Proactive Compliance Design: Bring in legal and privacy experts early. Ensure your federated learning system is designed to be compliant from the start. HealthHire, a hypothetical health tech startup, successfully met strict HIPAA and GDPR rules for predicting candidate fit.
• Build a Narrative of Trust: Position federated learning as a key part of your startup hiring data protection solutions and ethical AI strategy. Startups adopting privacy-enhancing technologies like federated learning for talent acquisition are projected to see a 15-20% higher candidate trust score and a 10% reduction in data breach risks by 2025 (CB Insights Future of HR Tech Report 2024). This makes your brand stand out.
• Simplify Communication: Use simple examples and real-world benefits to explain federated learning to non-technical audiences. Emphasize how it protects sensitive data while giving powerful AI insights. Learn how to explain Federated Learning to non-technical audiences

By actively tackling these challenges, your startup can confidently implement federated learning. This builds AI recruitment that is not only smart but also inherently trustworthy and compliant.

Continue to Essential Tools and Resources for Your Federated Learning Journey

Essential Tools and Resources for Your Federated Learning Journey

You've learned about the challenges and benefits of federated learning. Now, let's look at the essential tools and resources that will power your journey. Think of these as your core toolkit, helping you build privacy-preserving AI solutions efficiently and effectively.

Open-Source Federated Learning Frameworks

For early-stage companies, using open-source frameworks is a game-changer. They offer strong foundations, lower development costs, and benefit from active communities. These are your primary Federated Learning tools.

• TensorFlow Federated (TFF): Developed by Google, TFF is an excellent choice for building custom federated learning solutions. It provides a powerful, flexible environment for managing distributed training. For example, a startup like our hypothetical TalentGuard, an AI-powered skill assessment platform, could use TFF to train its skill-matching algorithms. Instead of collecting raw code, they'd process anonymized skill data locally on a candidate's device, sharing only aggregated model updates. This approach greatly boosts candidate trust and client adoption, as highlighted by Startups adopting privacy-enhancing technologies like federated learning for talent acquisition are projected to see a 15-20% higher candidate trust score and a 10% reduction in data breach risks by 2025 (CB Insights Future of HR Tech Report 2024).
• PySyft: From OpenMined, PySyft is a Python library built for secure, private AI. It works smoothly with popular deep learning frameworks like PyTorch and TensorFlow. This makes it ideal for adding privacy-enhancing techniques such as federated learning, differential privacy, and homomorphic encryption into your existing machine learning workflows. This is especially valuable given that The global market for privacy-enhancing technologies (PETs), including federated learning, is expected to grow from $2.9 billion in 2023 to $11.6 billion by 2028 (MarketsandMarkets Privacy-Enhancing Technologies Market Report 2023).

Integrating with Existing Recruitment Platforms

Your current tech stack doesn't need a complete overhaul; it can become part of your federated learning system. This is where AI hiring privacy software truly shines, by integrating with platforms you already use.

• Applicant Tracking Systems (ATS): Platforms like Greenhouse, Lever, and SmartRecruiters are central to managing candidate pipelines. While they don't natively support federated learning, their strong APIs allow for integration. You can design your system to pull non-sensitive or locally processed, anonymized data from your ATS to feed into your federated model. For example, a startup could use anonymized job descriptions and hiring outcomes from their ATS to train a federated model that predicts job fit, without ever centralizing sensitive candidate profiles.
• Coding Assessment Tools: Tools such as HackerRank and CoderPad are invaluable for technical hiring. These platforms collect rich performance data. A federated approach could involve training models on anonymized performance metrics locally within a company's secure instance. Only aggregated insights would be shared to improve assessment fairness or predict future performance. This provides secure AI recruitment resources by using valuable data without compromising individual privacy. A 2024 survey indicates that 62% of job seekers are concerned about how companies use their personal data during the recruitment process, impacting their willingness to apply (Glassdoor Candidate Privacy Report 2024). By integrating federated learning, you directly address these concerns.

Secure Communication and Data Storage

Even with decentralized training, secure communication channels and data storage are paramount. Model updates, even when aggregated and anonymized, still need to be sent securely. Implement strong encryption for all data in transit and at rest. Use secure communication protocols (like HTTPS), and ensure robust authentication for everyone in your federated network. This commitment to security is not just a technical need but a strategic must for building trust in AI recruitment. As Josh Bersin notes, it ensures that your privacy-first approach is comprehensive, protecting sensitive information from end-to-end. (Josh Bersin, HR Tech Conference 2024 Keynote).

Key Actions for Founders:

• Start Small: Pilot federated learning with a specific use case using open-source frameworks.
• Audit Your ATS: Understand what data can be anonymized and how it can integrate with a federated model.
• Prioritize Security: Invest in strong encryption and secure communication protocols from day one.
• Educate Stakeholders: Clearly communicate the privacy benefits to candidates and hiring managers.

Continue to Conclusion: Embrace Privacy-First AI for a Future-Proof Recruitment Strategy

Conclusion: Embrace Privacy-First AI for a Future-Proof Recruitment Strategy

As we've explored, building an AI recruitment system that is both powerful and inherently privacy-preserving isn't just a good idea; it's becoming the industry standard. By prioritizing security, using smart tools, and educating your team, your startup can confidently set a new benchmark for ethical talent acquisition.

The Future of Ethical AI in Talent Acquisition

At the heart of this evolution is federated learning. It's a strategic must for any startup aiming for ethical and effective AI recruitment. As global industry analyst Josh Bersin aptly puts it, "Federated learning isn't just a technical solution; it's a strategic imperative for building trust in AI-powered recruitment. Startups that embrace it early will differentiate themselves as ethical leaders in talent acquisition." (Josh Bersin, HR Tech Conference 2024 Keynote).

This isn't just theory; the market is already changing. By 2026, 70% of organizations will have implemented some form of AI in their HR processes, with data privacy and ethical AI being top concerns for 45% of these implementations (Gartner Hype Cycle for HR Technology 2025). The demand for privacy is clear: 62% of job seekers are concerned about how companies use their personal data during the recruitment process, impacting their willingness to apply (Glassdoor Candidate Privacy Report 2024). By keeping sensitive candidate data decentralized, federated learning directly addresses these concerns. It builds unmatched trust and ensures strong compliance with evolving rules like GDPR and CCPA. This proactive approach offers a significant competitive advantage, especially for startups.

Consider the hypothetical YC startup, TalentGuard, an AI-powered skill assessment platform. They used a federated learning approach to train their skill matching algorithms. Instead of collecting raw code samples, they processed anonymized skill data locally on client devices, sharing only aggregated model updates. This allowed them to improve matching accuracy across various tech stacks without ever seeing sensitive candidate project details. This greatly boosted candidate trust and client adoption. This is the essence of a privacy-first AI hiring privacy strategy that defines the Federated Learning Recruitment future.

Your Next Step Towards Secure, Intelligent Hiring

The path to a privacy-first AI hiring privacy strategy is clear. Startups are uniquely positioned to lead this charge. Agile and free from old systems, early-stage companies can build privacy into their design from day one. This sets a new standard for the entire industry.

Here are your next steps:

• Educate Your Team: Make sure everyone, from engineers to recruiters, understands the principles and benefits of privacy-first AI.
• Pilot and Iterate: Start with a focused federated learning project, learn from it, and expand. Learn more about implementing Federated Learning in your startup
• Communicate Transparency: Clearly state your commitment to privacy to candidates. Startups adopting privacy-enhancing technologies like federated learning for talent acquisition are projected to see a 15-20% higher candidate trust score and a 10% reduction in data breach risks by 2025 (CB Insights Future of HR Tech Report 2024).

At Clera.io, we believe this future is not just a dream but achievable. We are committed to empowering startups like yours with secure, intelligent hiring solutions that use the power of privacy-first AI. Embracing federated learning isn't just about avoiding risks; it's about building a more ethical, effective, and ultimately, a future-proof Federated Learning Recruitment future strategy that attracts the best talent and fosters lasting trust. Let's build that future together with Clera.io.