Change rarely feels comfortable, especially when it redefines how businesses serve their customers. For decades, enterprises have relied on large contact center teams, training them tirelessly to deliver consistency, empathy, and efficiency. But today, a new shift is underway: customer interactions are moving from human agents to AI-native agents powered by generative models.
This isn’t just a technological upgrade. It’s a workforce transformation, a cultural shift, and a trust challenge rolled into one. Businesses today largely face a question: Can AI really handle millions of sensitive, high-stakes interactions without losing the reliability and empathy customers expect?
ASAPP answers that question with confidence. Built for the realities of enterprise service—not consumer experiments—it has spent more than a decade engineering AI that enterprises can depend on.
Under the leadership of its CEO, Priya Vijayarajendran, ASAPP combines relentless innovation with uncompromising safeguards, enabling organizations to adopt generative AI without sacrificing quality, compliance, or trust. Its research focuses on the toughest enterprise challenges: scaling generative AI safely, ensuring reliability across millions of interactions, and embedding human collaboration where it matters.
ASAPP guides clients through this transition with proven best practices and hands-on training, ensuring teams can continue applying these skills long after deployment. Its R&D team has spent over a decade advancing enterprise-grade AI—not consumer experiments—supported by a deliberate patent strategy that protects originality while safeguarding customer outcomes.
ASAPP differentiates itself by managing innovation with discipline. Breakthroughs are delivered rapidly, yet every advancement is rigorously validated for enterprise scale, performance, and compliance before reaching customers. This ensures speed never comes at the expense of reliability, which is a critical aspect for enterprise trust.
She expresses, “Our patents signal more than legal protection: they demonstrate originality, defensibility, and future-proofing.” In a crowded market of off-the-shelf solutions, ASAPP stands apart as a trusted provider of differentiated, enterprise-tested AI that empowers organizations to innovate confidently while maintaining safety, accuracy, and measurable business impact.
She shares, “Our approach is grounded in over ten years of experience deploying, optimizing, and scaling generative AI in enterprise contact centers, giving us deep expertise in operational workflows. We also prepare clients for emerging AI-driven roles. Supervisors gain full visibility into GenerativeAgent performance, investigating high-impact anomalies, filtering conversations in real time, and monitoring aggregate reporting to identify and prioritize improvements.”
In this process, the program can make lightweight optimizations—updating knowledge bases, task instructions, or personas—safely after testing. Supervisors collaborate with CX developers using low-code tooling to configure logic, simulate scenarios, and validate changes. Policy analysts review transcripts, listen to recordings, and surface issues for supervisors to address.
The outcome? Enterprises transition confidently to AI-native operations, with people and AI working seamlessly together to deliver quality, compliance, and trust—while unlocking faster resolution and improved customer experiences.
Reliable AI Operations Through Human-AI Collaboration
ASAPP’s GenerativeAgent® is built for the realities of enterprise customer service, where context shifts quickly, accuracy is critical, and every industry has unique rules.
It is built to improve continuously while keeping clients in full control. Its optimization loop provides visibility, governance, and actionable insights post-deployment.
It combines robust observability and low-code development tools that empower clients to expand, manage, and optimize AI performance for sustained value. Unlike off-the-shelf LLMs, GenerativeAgent uses model orchestration to select the best model for each task, always grounded in a company’s business data, workflows, and safeguards from day one.
The company ensures context through its three layers:
- Domain intelligence encodes industry rules, compliance needs, and brand tone.
- Deep integration connects to knowledge bases, prior interactions, and real-time systems for precise, personalized responses.
- Safeguards at scale validate answers for accuracy, compliance, and brand standards before reaching the customer.
When extra support is required, GenerativeAgent never raises concerns to the customer; however, the company engages its Human-in-the-Loop Agent (HILA™). Working behind the scenes, HILA has full conversation context and can provide approvals or answers instantly, while GenerativeAgent maintains the flow to final resolution.
GenerativeAgent also learns continuously from escalations, sentiment and outcomes. Its multi-model architecture separates intent, retrieval, generation, and supervision, preserving context end-to-end, even in niche, high-stakes scenarios.
“The result: fewer escalations, quick resolution, and higher satisfaction. Enterprises can trust GenerativeAgent to autonomously handle complex issues with safety, precision, and reliability,” reveals Priya.
GenerativeAgent monitors 100% of interactions—voice and chat—and flags potential issues based on configurable thresholds. Real-time alerts notify supervisors or quality analysts, who can immediately review the full conversation context. Dashboards highlight flagged conversations by business impact, helping teams focus where it matters most.
Changes—from knowledge base updates to task refinements or policy adjustments—can be implemented in natural language, simulated, and tested against historical interactions to prevent regressions. More complex changes move to development teams, using the same testing framework before supervisors approve deployment.
“We partner closely with leadership to embed these practices, building internal expertise and ensuring consistent oversight. By combining automation, human judgment, and continuous feedback, ASAPP ensures GenerativeAgent not only performs but evolves responsibly, delivering better outcomes over time while maintaining enterprise confidence,” she describes.
From Risk Assessment to Responsible Deployment
ASAPP performs enterprise-grade risk assessments across all stages of deployment, adhering to strict frameworks for data privacy, compliance, and model governance. Every interaction is logged and monitored, giving clients full visibility and traceability. In addition, it regularly navigates each client’s InfoSec process, ensuring that GenerativeAgent meets internal security standards before deployment.
Its optimization loop balances efficiency with oversight. Configurable thresholds flag conversations that could affect compliance or customer experience, ensuring issues are reviewed promptly. Updates to knowledge bases, task instructions, or policies are tested against historical interactions to prevent regressions.
Leadership is deeply engaged, embedding governance practices that ensure transparency, accountability, and consistent oversight. By pairing automation with human supervision and robust safeguards, ASAPP allows enterprises to scale AI efficiently without ever compromising the trust, reliability, or compliance that underpin their customer relationships.
Built-In Safety Through Multi-Layered Defenses
ASAPP has also developed the Human-in-the-Loop Agent (HILA™) not as a fallback, but as a way for humans and AI to collaborate seamlessly. HILA allows humans to step in without a formal handoff, preserving both speed and efficiency. GenerativeAgent determines when to engage HILA, providing a structured workflow where the human acts as a “biological API” when system integration is absent or policy requires human approval.
HILA also runs in Approver Mode, letting a human review, edit, or approve each AI response before it reaches the customer. Enterprises use this to launch new intents and train AI directly through agent input. One airline leveraged HILA Approver Mode to shape GenerativeAgent’s responses to match its “Crew Members’” voice, ensuring AI interactions consistently reflected the brand.
Safety is embedded throughout the platform. Multiple LLMs are orchestrated for distinct roles, including QA and safety checks. Input evaluators block malicious prompts, guardrails enforce task scope, and safety agents validate outputs against client data, policies, and knowledge bases. In one instance, GenerativeAgent successfully defended against a prompt injection attempt, preventing a potential security issue.
The leadership lesson is clear: enterprise trust in AI comes not from replacing humans, but from designing systems where safety is native, and humans are empowered to guide, control, and elevate AI performance. These innovations directly translate into trusted capabilities that drive resilience, compliance, and measurable ROI for clients.
Guardrails for Ethical and Reliable AI Operations
At ASAPP, enterprise-grade AI security and safety are foundational, not optional. Security protects the system from external threats, while safety ensures the AI operates ethically, reliably, and in line with enterprise policies.
The majority of AI security focuses on foundational controls common to all applications: data protection, encryption, PII redaction, authorization, secure APIs, and strict access controls. These measures form a robust first line of defense against both traditional and AI-specific risks. Input safety mechanisms detect and block malicious prompts or code injections, while advanced firewalls and continuous monitoring ensure rapid detection and response to suspicious activity. Alerts and logging provide full visibility, and a tested incident response plan allows the security team to contain and mitigate potential threats.
Safety guardrails address AI-specific risks such as bias, hallucinations, and unintended outputs. Diverse training data, output monitoring, content filters, and human-in-the-loop workflows help ensure fairness, accuracy, and ethical decision-making. Supervisors or agents can intervene when critical decisions require human judgment, preventing harm before it reaches the customer.
By combining foundational security, layered AI-specific defenses, continuous monitoring, and human oversight, ASAPP ensures that GenerativeAgent remains resilient, ethical, and trustworthy throughout its entire lifecycle—protecting both enterprises and their customers.
Strategic, Phased Deployment for Maximum Impact
The team at ASAPP works closely with each client to determine which interactions are best suited for automation. It considers risk tolerance, desired outcomes, and use case volume and complexity—for example, whether a use case requires API calls or complex decision logic. Some clients embrace innovation quickly for faster time to value, while others prefer longer testing phases to mitigate risk.
Its phased deployment approach prioritizes quick wins: it launches high-volume, lower-complexity interactions to deliver immediate impact, then expands to more sophisticated, high-ROI intents. This ensures clients realize value early while building confidence in AI-driven operations.
Quick-win KPIs focus on efficiency, including average handling time, cost per interaction, containment rate, live agent volume reduction, average speed to answer, agent occupancy, and internal transfer rate. High-ROI KPIs extend beyond efficiency to include first-contact resolution, customer effort, CSAT/NPS, cost per interaction, revenue growth, and fraud reduction.
By combining strategic assessment, phased deployment, and outcome-driven KPIs, ASAPP helps enterprises automate the right interactions safely and effectively, unlocking measurable efficiency gains, improving customer experiences, and mitigating operational risk—all while ensuring AI adoption is sustainable and trusted.
Transforming Customer Service with GenerativeAgent
A major global airline sought to elevate its digital customer service on a scale. Already handling high volumes with ASAPP’s Messaging Platform virtual assistant, the airline wanted to prove that GenerativeAgent could deliver faster, more accurate support and improve customer experience (CX) during both routine and irregular operations (IROPs).
ASAPP partnered closely with the airline, forming a dedicated implementation team and following a structured approach:
- Identify use cases by analyzing intent volume, containment rates, and API availability.
- Prioritize use cases, balancing simple inquiries (e.g., policy questions) with complex tasks (e.g., flight rebooking).
- Simulate & test responses to refine AI behavior before launch.
- Optimize configurations, fine-tuning instructions and settings prior to production rollout.
- Launch with confidence, deploying fully tested and safe GenerativeAgent workflows.
- Expand automation iteratively through evaluation and optimization.
The first use case—a pet travel policy—was both simple and nuanced, requiring accurate handling of highly variable rules. This deployment became a model for subsequent implementations.
Results:
- 26× fewer errors than human agents
- >4× faster resolution times
- CSAT lift thanks to faster, more accurate support
Leadership and architectural choices—including model orchestration, Human-in-the-Loop oversight, and iterative simulation/testing—ensured reliability and measurable business impact, allowing the airline to scale AI confidently while improving customer satisfaction.
Thriving with Proactive Governance and Continuous Protection
Its Head of Security, Trust, and IT, Kash Kiani, recently highlighted in Forbes the novel risks generative AI introduces—and what buyers should look for when selecting a vendor. Prompt injection, where malicious inputs manipulate AI behavior, is among the most serious, while data poisoning and unexpected agent-to-agent interactions also pose emerging threats.
At ASAPP, trust and safety are built into GenerativeAgent from the ground up. Defense begins with foundational security: input validation, strong data protection, strict authorization, encryption, and secure API access. Secure API endpoints prevent unauthorized access, forming the first line of defense against both traditional and AI-specific risks.
The organization adds layered AI-specific safeguards: input safety mechanisms block exploit attempts, anomaly detection flags unusual voice or behavioral patterns, and advanced firewalls protect against injection-style attacks. Continuous monitoring and logging make every interaction auditable, while real-time alerts enable rapid response to suspicious activity.
Leadership and governance are embedded throughout, supported by clear incident response plans and operational workflows. By combining foundational and layered defenses with proactive oversight, and ongoing research and development, ASAPP ensures GenerativeAgent remains resilient against emerging threats—giving enterprises confidence to scale AI safely, securely, and reliably.
Leading with Data-Driven Vision and Enterprise AI Expertise
Priya Vijayarajendran currently serves as the Chief Executive Officer of ASAPP, where she and her team are transforming contact centers with AI-powered automation. ASAPP is a leading provider of enterprise AI SaaS solutions for contact centers.
With a strong commitment to transformative technology, Priya specializes in engineering scalable enterprise software. A deeply data-driven leader, she focuses on building cloud-native, scalable SaaS AI products and services. Priya is passionate about bringing the value of transformative technology to solve real business challenges. She thrives as an intrapreneur, identifying and developing top talent and building high-performing, scalable teams.
Priya is a dedicated listener to the voice of the customer and users, serving as their trusted technology advisor and partner. She believes strongly in engineering solutions with a customer-centric focus. With her deep experience, Priya is well-qualified to assist boards in differentiating and validating technology choices, ensuring strong execution paths. She brings a data-driven approach with insights and analytics to enhance business decisions.
With over three decades of experience in enterprise product development, product strategy, application development, enterprise architecture, customer co-innovation, services, and field enablement, Priya is a recognized thought leader. She advises and participates in entrepreneurial and tech meetups around the Bay Area and is a regular speaker at many enterprise computing, data, and AI forums. As a woman of color in tech, Priya is a committed advocate for democratizing AI and mentoring the next generation of talent, especially women in tech.
