, a global technology services leader with 240,000 employees across 65+ countries, partnered with Ema to reimagine HR service delivery. By deploying a network of AI employees worldwide, Wipro transformed HR from a manual service center into a strategic enterprise capability.


The Challenge: Navigating Global Scale and Complexity

At Wipro’s scale, HR services operate as a highly complex global ecosystem. Teams must navigate more than 1,000 enterprise applications, 16,000 policy articles, 4,000 FAQs, and a constantly evolving landscape of regional labor regulations - all while delivering fast, accurate, and consistent support experiences worldwide.

Traditional automation was not enough. Legacy chatbots could answer simple questions but could not execute tasks, navigate systems, or resolve end-to-end employee requests. Expanding support teams was equally unsustainable. Wipro needed a fundamentally different approach, one that could combine deep enterprise knowledge with the ability to take action across systems at global scale.

Wipro's transformation began with a strategic decision to redesign its operating model around agentic AI. As associate expectations evolved and legacy ticketing systems struggled to keep pace, Wipro recognized that incremental automation would not be enough.

While many organizations remain stuck in AI experimentation, Wipro prioritized enterprise-scale deployment from day one.

Eliminating Fragmentation Through Intelligent Orchestration

By leveraging Ema as an intelligent orchestration layer, Wipro bypassed the need to rip-and-replace core HCM infrastructure. The new model replaced fragmented, people-dependent processes with a unified, on-demand experience.

Orchestrating workflows across 10+ enterprise systems, from SAP to regional operational platforms

Enabling 70+ live actions, including leave and timesheet submission, payslip access, benefits management, and service ticket resolution

Unifying HR, IT, payroll, and employee services through a single digital front door

By embedding Ema’s AI Employees directly into the existing flow of work, including on handheld devices and within the corporate intranet, Wipro removed the friction of adoption. Whether an associate is collaborating in a chat window or browsing internal resources, the AI Employee is available as a persistent, intelligent companion.

This transformation required replacing an existing solution, expanding globally, and improving the associate experience, all without compromising governance or compliance.

 Wipro and Ema cleared 100+ security and compliance checks, ensuring enterprise readiness, data sovereignty and global-scale deployment.

 150+ testers rigorously validated performance, accuracy, and reliability across diverse use cases.

The Blueprint for HR Leaders: Lessons from the Front Line

Wipro's experience offers five lessons for HR leaders looking to operationalize agentic AI at enterprise scale:

 Start with a clear understanding of who you're serving and the outcomes that matter most.

 Build security, privacy, governance, and integration into the foundation, not as afterthoughts.

 Use AI to redesign how work gets done, not simply digitize existing processes.

 Adoption requires more than technology. Even when AI resolves requests accurately, employees need time and trust to embrace AI-first workflows.

 Success comes from close collaboration, shared accountability, and a commitment to continuous improvement.

For Wipro, the AI Employee is no longer a pilot or a project. It is becoming the new operating model for delivering HR services at global scale.

How Wipro Reduced HR Complexity with Ema

Recruiting may be the only function where companies are encountering AI on both sides of the decision.

Candidates are using AI to write resumes. Employers are using AI to screen them.

Algorithms are evaluating content generated by other algorithms. This shift deserves far more attention than it is getting.

The conversation around AI in recruiting has largely focused on productivity.

How much faster can recruiters source candidates?

How quickly can applications be screened?

How many administrative tasks can be automated?

Those are important questions, but they are not the most important ones. The most important question is whether hiring systems are becoming better too, not simply replicating the biases inherent in human hiring.

For years, organizations have invested heavily in reducing bias from hiring. Structured interviews, diverse hiring panels, standardized assessments, and compliance frameworks have all emerged from a recognition that talent decisions shape the long-term trajectory of a company. AI changes the mechanics of that process, but it does not eliminate the responsibility. In fact, it increases it.

Many people assume AI will make hiring more objective. There is no guarantee of that. AI learns from existing data and existing hiring patterns. If those patterns contain bias, AI can reinforce it.

Researchers are already seeing signs of this. A

 found that every large language model tested for resume evaluation exhibited some degree of racial or gender bias, although the severity varied significantly by model. Another 

 found that LLMs acting as evaluators often preferred AI-generated content over content written by humans, a phenomenon researchers described as AI-AI bias. The point is not that AI is bad for hiring. The point is that AI does not automatically make hiring fair.

And fairness matters because hiring decisions directly impact business performance. The companies that consistently identify the best people build stronger teams, make better decisions, and execute faster. The companies that overlook qualified candidates because of flawed evaluation processes limit their own access to talent.

This is one reason diversity and inclusion matter. 

 found that companies in the top quartile for ethnic diversity among executive teams were 39% more likely to outperform their peers financially. The takeaway is not that diversity should be treated as a hiring target. The takeaway is that companies perform better when they are able to identify talent wherever it exists.

The challenge is that bias rarely appears as a single bad decision. It shows up through hundreds of small decisions across the hiring process. Which resumes get reviewed. Which candidates get shortlisted. Which skills get prioritized. Which interview feedback carries the most weight. Over time, those decisions determine who gets hired and who gets overlooked.

As AI becomes more deeply embedded in recruiting, organizations need systems that apply evaluation criteria consistently, make recommendations that can be explained, and keep humans accountable for final decisions. The goal is not to automate hiring. The goal is to make better hiring decisions.

What Enterprise Leaders Should Expect From Recruiting Technology

The market has spent the last decade optimizing recruiting for efficiency.

But modern business requires both efficiency and accountability. The next era of recruiting will demand both.

Enterprise leaders should expect more from their recruiting systems than simple automation and productivity gains.

Can we understand why a candidate was recommended by a tool?

Can we prove that every candidate was evaluated against the same criteria?

Can we show what information influenced each recommendation?

Can we prove that bias and protected characteristics did not influence rankings?

Can we reconstruct the decision six months later, if regulators, legal teams, or boards ask us to?

If the answer to those questions is no, organizations are introducing risk into one of the most consequential decision-making processes they operate.

These principles have shaped how we think about Ema’s AI Recruiter.

Our objective was never to build another sourcing tool or screening engine. The recruiting market has no shortage of those. The challenge facing enterprise hiring teams today is not access to automation. It is access to trust. We also recognized that bias can originate from the models themselves.

Candidates should be evaluated against the same role requirements regardless of who reviews them. That is why Ema Recruiter uses structured scorecards and standardized evaluation criteria throughout the process. The goal is not to remove human judgment. The goal is to ensure human judgment operates within a consistent framework.

: One of the biggest risks with AI-powered hiring systems is that they become black boxes. Recommendations appear without context. Rankings appear without explanation. Recruiters are expected to trust outputs they cannot interrogate.

Trust also requires removing signals that should not influence decisions.

Personally identifiable information is redacted before scoring. Demographic signals are stripped from ranking workflows. Candidate recommendations are generated based on qualifications, experience, skills, and role fit rather than attributes unrelated to job performance.

That approach creates risk for organizations and frustration for recruiters.

Every recommendation generated by Ema Recruiter includes supporting rationale. Recruiters can understand why a candidate was surfaced by the model, which qualifications contributed to the recommendation, and how the evaluation was conducted. Human judgment remains central because accountability remains human.

We also recognized that bias can originate from the models themselves.

Most AI applications rely on a single LLM. The challenge is that every model has strengths, weaknesses, assumptions, and blind spots. Our multi-model fusion layer architecture, 

, uses the right model-mix depending on the enterprise task, rather than relying on any single LLM for each query, making it more cost-efficient, accurate, and faster. No AI system is bias-free, but reducing dependence on any single LLM creates a more balanced, accurate, and resilient evaluation process.

Perhaps the most important design decision, however, is auditability.

The regulatory environment surrounding AI in hiring is evolving rapidly. Organizations are already facing increasing scrutiny around explainability, transparency, and bias monitoring. The 

highlighted a significant increase in global focus on responsible AI, governance, transparency, and accountability as AI systems become embedded in business-critical decisions.

Whether the conversation is driven by regulators, legal teams, boards, or candidates themselves, the expectation is becoming clear: companies must be able to explain how hiring decisions are made.

That is why we’ve made every score, recommendation, workflow action, and recruiter interaction inside Ema’s AI Recruiter logged and exportable.

Recruiters need more than automation. They need fairness, explainability, and auditability.

The recruiting industry often frames AI as a productivity story.

Can recruiters trust the recommendations?

Organizations that answer those questions successfully will build stronger talent pipelines than those focused solely on efficiency.

Recruiting is becoming a technology problem and a governance problem at the same time.

The challenge is no longer finding ways to automate more of the process. It is ensuring that automation produces decisions organizations can stand behind.

That requires more than AI. It requires systems designed for accountability from the start.

When AI Evaluates AI: Why Fairness Matters More Than Ever in Recruiting

AMS, a global talent solutions business operating across 90+ countries, partnered with Ema and PwC to transform the manual, system-fragmented processes at the core of their contingent workforce operations, moving from human-orchestrated execution to AI-orchestrated, agentic delivery.

Contingent Workforce Management sits at the operational core of what AMS delivers. Most of the processes running it were still manual.

Contingent workforce management (CWM) is process-intensive by design and spans across worker onboarding, compliance verifications, approvals, Internal system of records updates, extension & termination across multiple enterprise clients, each with their own rules and requirements.

Three structural factors made it the right place to start:

 Most CWM processes are AMS-owned rather than client-owned, giving AMS the leverage to redesign workflows without disrupting client systems

 CWM operations are concentrated within a single function, making end-to-end process mapping tractable

 The contingent technology stack is more standardised across enterprise clients than permanent hiring, reducing integration complexity

 - The cost of inaction amounted to missed SLAs and lost customer time leading to revenue leakage and limiting AMS's ability to scale delivery capacity without adding headcount.

A structured agentic transformation program — with Ema's platform and PwC's transformation expertise.

AMS, supported by Ema and PwC, established a structured change program to identify and prioritise agentic AI interventions across CWM operations. Ema’s platform capabilities and PwC’s transformation expertise helped map the end-to-end CWM lifecycle, evaluate use cases against business value, scalability, delivery effort, and integration risk — and create a clear, investment-ready roadmap for automation.

Of the use cases that advanced to proof of concept, two were selected for full-scale implementation based on deliberate evaluation of costs and risks – 

 — both high-volume, process-intensive workflows residing within AMS-managed operations.

Ema's platform provided the agentic execution and flexibility layer:

Integrated across AMS's full technology ecosystem

 — connecting the VMS and workforce management systems AMS operates across, including SAP Fieldglass, Beeline, TalentLink, ServiceNow, Outlook, and Workday, eliminating the manual handoffs that previously bridged each platform. Fully customised to follow AMS's onboarding and contract extension SOPs, with client- and region-specific nuances built into every workflow

 (Generative Workflow Engine) to create scalable, configurable workflows — extensible to multiple end clients without rebuilding from scratch for each, and customised to AMS's operational logic at every layer

Identity-aware unified front end via Ema's Frontier Apps

 — a fully customised app built on top of the GWE orchestration layer, giving AMS admins a single pane of glass to manage all onboarding and contract extension tasks across disconnected systems, with no tab-switching or manual reconciliation

Intelligent SLA tracking and task prioritisation

 — automated monitoring ensures SLAs are never missed, with intelligent task allocation to admins based on current workload, availability, and expertise;

 — agentic triggers automatically spawn background check workflows and additional approval processes based on worker and client context; direct ServiceNow and Outlook integration enables admins to draft and send communications to hiring managers and candidates without leaving the platform

 — approvals routed to the right person at the right moment, keeping AMS's admins in control while agents handled execution.

From manual coordination to orchestrated delivery across end clients, at scale.

 of contingent worker onboarding and contract extension management — workflows that previously relied on manual coordination across systems and teams

 across AMS's admins and enterprise clients — custom workflows per end client, consistent and auditable execution regardless of volume

 with every agent action logged, traceable, and reviewable, with human-approved decision points built into every critical step

 validated across initial use cases and ready to expand to additional CWM processes and client workflows

Agentic transformation takes two: a platform built for flexibility and a partner built for change

The AMS deployment reflects a broader pattern identified by Everest Group which is that the fastest route to agentic capability at enterprise scale combines a configurable agentic platform with a transformation partner that brings process expertise and change management depth.

Ema provided the execution intelligence. PwC provided the process architecture and business case validation. AMS provided the operational domain knowledge and leadership to make transformation stick.

This was a complete opera ting model shift for AMS, from reactive, human-coordinated execution to proactive, agent-orchestrated delivery.

See how Ema powers agentic transformation at enterprise scale.

How AMS Embedded Agentic AI Into Contingent Workforce Management 

Most enterprises struggling with agentic AI have the same problem. They deployed the technology on top of everything that already existed.

Same org chart. Same approval chains. Same performance metrics designed to count what a person does in a day. AI agents sit inside structures they were never built for — and then leadership wonders why the ROI isn't materialising.

85% of organisations say they want to be agentic within the next three years. But 76% say their current operations and infrastructure can't support that change.

 The gap between ambition and execution is not a technology problem. It is an organisational design problem. This is the gap 

 explored in a recent article, published last week in partnership with Ema. 

PwC's global CTO for workforce consulting and Chief AI Officer, Prasun Shan, has a phrase for what most enterprises are doing. He calls it sticky tape.

"They're embedding AI employees into what is a human operating model. This is like adding sticky tape to parts of an operating model that is breaking." 

, Global CTO for Workforce Consulting & Chief AI Officer, PwC UK

The problem with sticky tape is structural. If human bandwidth is still the binding constraint on approvals, escalations, and handoffs, AI assistance compresses cycle time at the margins. It doesn't change what the organisation can do at scale. AI assistance is not AI transformation.

The value in agentic AI lies in its capacity to execute entire workflows with limited human input — to coordinate complex tasks, make independent decisions, and iterate performance. 

It's already estimated that AI agents could accelerate business processes by 30–50%

 when deployed at scale. Getting to that value requires redesigning how the organisation works, not just adding AI to how it currently works.

What Agentic Business Transformation Actually Means

—because none of the existing vocabulary captured what actually needs to change to succeed with Agentic AI. (You can explore the full ABT framework at 

"Digital transformation was about moving from paper to software. AI transformation was about adding AI to existing processes. Copilot is about AI assisting humans. But ABT is something categorically different: it's the integration of AI agents into the fabric of the organisation." 

 — from basic task agents to fully autonomous AI workforces — and it requires redesigning three things simultaneously.

The existing technology stack was designed for human-operated, application-centric workflows. AI agents don't fit that model. Their value isn't as another layer on top of the stack — it's as connective tissue that moves between layers, coordinating high-level tasks and retrieving data across multiple systems simultaneously.

Organisations that make this architectural shift don't wait months for a software vendor to build a new feature. They configure an AI employee in natural language and connect it to the systems it needs. The time from business requirement to production workflow drops from months to days.

When AI agents can execute, coordinate, and optimise without managerial coordination, hierarchies blur. Managers take on new responsibilities: managing trust, explainability, and psychological safety in hybrid teams. 

McKinsey estimates that three-quarters of current jobs will require redesign, upskilling, or redeployment by 2030.

Activity metrics become misleading the moment AI employees enter the picture. An AI employee handles a thousand customer interactions in the time a human handles ten. Measuring interactions tells you the AI is fast. It tells you nothing about whether any of those interactions drove satisfaction, retention, or revenue.

When one of Ema's enterprise customers switched from metrics like cost per query and AI accuracy to outcomes like the percentage of contracts reviewed without human escalation, their measured ROI from agentic AI tripled within two quarters.

The organisations pulling ahead aren't waiting for certainty. They are redesigning for what work and the organization should look like. If you're responsible for running agentic AI in your enterprise, here's how you can learn how to lead through the shift:

, with 5 levels of agentic maturity and other key frameworks

Rethink Organizational Design for the Age of Agentic AI

Why Agentic Business Transformation Starts with Organisational Design

Every recruiter on your team has access to AI now. Job descriptions are drafted in seconds, and platforms suggest "best fit" candidates with a single click. Yet, hiring remains slower and more expensive than ever, which brings us to the 2026 problem 

The 2026 Problem: More Tools, Too Much Bloat

Traditional keyword screening has become meaningless in an era of AI-padded resumes. Candidates now generate "perfect" resumes tailored to any JD, forcing recruiters to invest in manual triage.

Recruiting teams thus spend a large amount of their time cross-referencing profiles against news signals, company databases, and GitHub just to find authentic talent. But most AI tools designed to help with this are only making the problem worse.

 Hiring teams operate across six to eight disconnected systems for every hire. LinkedIn, ATS, sourcing platforms, schedulers, screeners, and the inevitable spreadsheets.

 When you add up LinkedIn, applicant tracking systems, and specialized sourcing tools, the annual stack cost for enterprise organizations often exceeds $350,000 before a single hire is made.

 Recruiters lose a significant portion of their week manually moving data between tabs and keeping candidate records in sync.

Recruiting teams don’t just need another point solution with AI added on. They need a unified system of intelligence that can actually handle everything from position management, JD creation, and sourcing, to ranking, outreach and scheduling, pre-screening interviews, scoring and feedback, all the way through to onboarding the new employee.

How Ema's Recruiter Transforms Your Hiring



Beyond Point Solutions: A Unified System of Intelligence 

Ema’s AI recruiter is designed to eliminate the "tool sprawl" that drains your team’s productivity. She handles all seven stages of hiring, from position management to onboarding, natively in one workflow. By consolidating your stack, Ema enables your organization to fill roles 3x faster while cutting the cost per hire by 70%.

, a leading executive search firm, illustrates Ema transformative impact. By adopting Ema Recruiter, Artico cut time-to-hire by 67% and reduced cost-per-hire by 30%, all while raising candidate quality and client satisfaction.

As Mercedes Chatfield-Taylor, CEO of Artico, puts it “

With Ema, we don’t just scale our recruiting efforts; we compound our advantage.”

 across 30+ sources, including LinkedIn, technical communities, and internal ATS databases, in one ranked list.

Ema creates multichannel sequences across email and LinkedIn based on a candidate's specific work history and professional background.


 Ema manages interview scheduling, intent detection on replies, and candidate-led screening, eliminating the hours wasted on operational work.

Integrate With your ATS or Upgrade to a Tailor-Made Experience

Ema provides the ultimate flexibility, allowing you to either maintain a bidirectional sync with your existing tools or replace your legacy ATS altogether for a customized, smarter experience.

Ema connects with 100+ recruiting tools and 20+ ATS systems to fetch, evaluate, and update candidates directly within your current system.

 For teams seeking a unified experience, Ema can replace fragmented legacy systems to manage the entire lifecycle from prospect to first-day natively.

Whether integrating or replacing, Ema deduplicates entries and enriches profiles with external signals from real-time news and social media to ensure your database is a strategic asset.

Ema is designed to make hiring fairer and more transparent, adhering to ISO 42001 and GDPR standards.

Human-in-the-Loop at Every Decision Point:

 Ema grades candidates against your criteria with clear rationale, but every final hiring decision stays with your team. Every AI-generated score is editable to ensure human expertise leads the way.

Demographic signals are stripped and PII is redacted before scoring to ensure neutral evaluations. Structured scorecards are applied consistently to focus solely on proven skills.

Complete Audit Trail for EEOC, DEI, and Compliance:

 Every recommendation and action is logged and exportable, providing a defensible record for any audit. Your team stays audit-ready without the manual scramble for records.

Our usage-based subscription lets you dynamically scale resources to meet any hiring demand without friction.

Rejected candidates get a real close-out, turning them into a future referral pipeline.

We break down the cost barriers that prevent true team-based hiring.

 This approach unlocks the full productive capacity of your entire ecosystem rather than punishing you for growth.

A Candidate Experience That Turns Rejections Into Referrals

Candidates remember the worst moments of your process: lazy outreach, scheduling friction, or silence after a final round. Ema fixes these in the workflow itself.

Sequences reference a candidate's actual work and professional background for higher engagement.

Scheduling moves to the candidate's calendar with intent-detected follow-ups that adapt automatically.

You do not need a long customization period to see value. 

Starting today, anyone can access our sandbox environment for free. 

 with personalized, profile-based intelligence.

 against a unified system of intelligence without touching your live data.

Introducing Ema Recruiter: The Complete Agentic Recruiting Solution 

PCI DSS compliance is one of the most demanding security benchmarks in enterprise software, and one of the most meaningful signals of trust for any platform that touches financial data.

Here is what that means, why it matters for your organisation, and how it fits into the compliance framework we have built around the platform.

The Payment Card Industry Data Security Standard is a globally recognised security framework developed by the PCI Security Standards Council, established by Visa, Mastercard, American Express, Discover, and JCB. It defines the technical and operational requirements that any organisation storing, processing, or transmitting cardholder data must satisfy.

Version 4.0.1 is the latest revision, superseding PCI DSS 3.2.1, which reached end-of-life in March 2024. The update reflects a fundamental shift in philosophy: away from point-in-time compliance snapshots toward continuous, risk-based security. Key changes include stricter authentication requirements, targeted risk analysis for control customisation, expanded e-commerce and phishing protections, and more rigorous testing procedures across all 12 requirements.

For Ema, where enterprise customers deploy AI Employees across billing, finance, and customer operations workflows, PCI DSS compliance is not a commercial checkbox. It is a structural assurance that the controls protecting cardholder data are independently verified, continuously monitored, and built to hold under adversarial conditions.

How Ema Meets the 12 PCI DSS Requirements

PCI DSS 4.0.1 is organised into 12 core requirements across six control objectives. Here is how Ema's architecture maps to each.

Network Security Controls (Requirement 1)

Ema's network perimeter is anchored by Cloudflare Enterprise, providing Web Application Firewall protection, DDoS mitigation, bot management, and deep packet inspection at the edge. Internally, dedicated Virtual Private Clouds enforce network segmentation between environments. Istio service mesh enforces mutual TLS for all east-west service communication. Firewall rules are defined as code, version-controlled, and subject to change control review before any modification reaches production.

All infrastructure is provisioned via Terraform and Helm, with hardened baselines aligned to CIS Benchmarks Level 1 and 2. Configuration drift is continuously monitored through our Cloud-Native Application Protection Platform. No production system deploys without passing automated policy gates in CI/CD, which check for misconfigurations, insecure defaults, and exposed secrets before any code reaches a running environment.

Protecting Stored and Transmitted Data (Requirements 3 & 4)

Ema enforces AES-256 encryption at rest across all data stores and TLS 1.2+ in transit, with TLS 1.3 preferred wherever supported. Cardholder data minimisation is a design principle: our architecture reduces the volume of card data that traverses or is retained within the platform. Where PII and payment data must be handled, our automated redaction pipeline, powered by Google Cloud DLP, masks sensitive fields before they reach third-party LLM providers. Cardholder data never leaves our controlled environment unprotected.

Malicious Software and Secure Development (Requirements 5 & 6)

Ema's Secure Software Development Framework embeds security into every phase of the development lifecycle. Static Application Security Testing and Software Composition Analysis run on every pull request. Dynamic Application Security Testing and manual penetration testing happen quarterly and before every major release. Endpoint protection covers all corporate devices, managed via Kandji MDM with hardened macOS configurations and mandatory encryption.

Access at Ema is built on Zero Trust principles: no implicit trust, least privilege by default, and just-in-time elevation for privileged operations. Okta enforces phishing-resistant MFA across all production and administrative access. Role-Based Access Control is strictly enforced, with quarterly access reviews and automated deprovisioning on employee offboarding. Physical access controls are handled at the data centre level through GCP and Azure, both of which hold their own PCI DSS certifications.

Logging, Monitoring, and Testing (Requirements 10 & 11)

All access to in-scope systems is logged and centralised. Our SIEM platform aggregates, correlates, and alerts on anomalous behaviour in real time, with automated playbooks enabling rapid incident response. Penetration testing is conducted quarterly by accredited third parties, with findings tracked to remediation closure in our risk register. Vulnerability scans run continuously, with CVSS-based prioritisation and assigned SLAs for every finding.

PCI DSS compliance at Ema is not owned by a single team. It is embedded into how we operate. Security policies are codified, version-controlled, and reviewed annually or following material changes. All employees complete mandatory security awareness training at onboarding and annually thereafter. Our cross-functional Security and Governance Committee oversees risk, exceptions, and corrective actions, with named owners for every control.

PCI DSS compliance is not about Ema's internal controls alone. It is about what it enables for you.

 If your organisation operates in financial services, retail, or any sector handling card payments, Ema's PCI DSS 4.0.1 compliance means you can onboard us without requiring a bespoke security review to validate what is already independently assured.

 As an ICT provider operating under your data processing agreements, Ema's compliance directly strengthens your own PCI DSS posture. Our controls are designed to support your assessments, not complicate them.

Confidence in agentic workflows touching sensitive data.

 As you deploy Ema's AI Employees across finance, billing, and customer operations, the platform handling those workflows meets the highest standard of cardholder data protection.

 Our Trust Center at trust.ema.ai provides current certificates and compliance documentation. Deeper evidence, including penetration test executive summaries and control mappings, is available to qualified customers under NDA.

PCI DSS Within Ema's Unified Compliance Framework

Introducing Ema Recruiter: The Complete Agentic Recruiting Solution

How Wipro Reduced HR Complexity with Ema

When AI Evaluates AI: Why Fairness Matters More Than Ever in Recruiting

How AMS Embedded Agentic AI Into Contingent Workforce Management

Why Agentic Business Transformation Starts with Organisational Design

Ema Achieves PCI DSS 4.0.1 Compliance: What It Means for You

Ema Autopilot: The AI Employee That Builds and Manages All Your AI Employees