Cekura: Securing Conversational AI at Scale

As conversational AI rapidly moves from experimentation to production, the stakes around reliability, safety, and compliance have never been higher. Enterprises are no longer asking whether they should deploy voice and chat AI agents, but whether those agents can be trusted in real-world, high-risk environments. This is the space where Cekura has positioned itself.

Founded in 2024 and headquartered in San Francisco, Cekura is a startup focused on testing, quality assurance, and observability for voice AI and chat AI agents. The company helps conversational AI teams ship reliable, secure, and compliant agents by providing automated QA across the entire agent lifecycle—from pre-production simulations to live production monitoring.

With a team of 15 and backing from Y Combinator’s Fall 2024 batch, Cekura operates at the intersection of AI safety, enterprise readiness, and software reliability. Its platform is already used by more than 75 customers across regulated and high-impact industries such as healthcare, BFSI, logistics, recruitment, and retail, including some of the largest CCaaS players and enterprise organizations.

In a market where conversational AI failures can lead to legal exposure, reputational damage, or regulatory penalties, Cekura’s role is not optional—it is foundational.

Why Is Quality Assurance So Hard for Conversational AI?

Traditional software testing relies on predictable inputs, deterministic outputs, and well-defined edge cases. Conversational AI breaks all three assumptions. Voice and chat agents must handle unstructured language, ambiguous intent, adversarial users, and constantly evolving model behavior.

Manual testing—often described as “vibe-based testing”—quickly becomes insufficient. Human testers cannot realistically simulate thousands of attack vectors, linguistic variations, or malicious prompts. Even worse, many failures only appear in production, where real users behave in unexpected and sometimes hostile ways.

For enterprises operating in regulated sectors, this unpredictability creates a deployment bottleneck. Security teams, legal departments, and compliance officers frequently block launches because there is no concrete evidence that an AI agent will behave safely under pressure.

Cekura exists to close this gap by bringing rigor, automation, and scale to conversational AI QA.

How Does Cekura Support the Entire AI Agent Lifecycle?

Cekura’s approach to QA is not limited to a single testing phase. Instead, it spans the full lifecycle of an AI agent, ensuring reliability before launch and safety after deployment.

In pre-production, Cekura enables simulation-based testing, where agents are evaluated against a wide range of synthetic and adversarial scenarios. These simulations help teams identify vulnerabilities early, long before real users encounter them.

As agents move into production, Cekura provides continuous monitoring of live conversations. This allows teams to detect regressions, emerging risks, and unexpected behaviors in real time. Rather than reacting to incidents after damage has occurred, organizations can proactively intervene.

Beyond tooling, Cekura also assists teams in integrating QA into their CI/CD pipelines. This ensures that every model update, prompt change, or workflow adjustment is automatically tested before being released, bringing conversational AI closer to the reliability standards of traditional software engineering.

What Makes Cekura’s Red Teaming Approach Different?

One of Cekura’s most distinctive offerings is its Red Teaming capability, designed specifically for conversational AI systems.

Red Teaming, in this context, means actively attempting to break an AI agent before real users do. Instead of assuming good-faith usage, Cekura simulates adversarial behavior at scale, acting as the “bad actor” that probes for weaknesses across multiple vulnerability categories.

Cekura’s Red Teaming runs thousands of adversarial simulations in minutes, far exceeding what any human QA team could accomplish manually. These simulations are not generic stress tests; they are structured, repeatable, and measurable, allowing teams to track improvement over time.

This approach shifts AI safety from a subjective discussion to an evidence-based process, something enterprise security teams can actually trust.

How Does Cekura Test for Jailbreaks and Prompt Injection?

Jailbreaking remains one of the most serious threats facing conversational AI. Through carefully crafted prompt injection attacks, users may attempt to override system instructions, extract internal prompts, or force agents to behave outside their intended scope.

Cekura systematically tests for these vulnerabilities by simulating sophisticated jailbreak attempts. These tests examine whether an agent can be tricked into ignoring safeguards, revealing internal logic, or performing restricted actions.

By identifying jailbreak weaknesses before deployment, teams can harden their agents against real-world attacks that would otherwise go unnoticed until it is too late.

Why Are Bias and Fairness Central to AI Red Teaming?

In industries such as finance, healthcare, and recruitment, biased AI behavior is not just unethical—it can be illegal.

Cekura includes bias and fairness testing as a core component of its Red Teaming framework. The platform evaluates whether agents produce unfair, discriminatory, or non-compliant responses when dealing with sensitive topics such as creditworthiness, medical advice, or hiring decisions.

These tests help organizations ensure that their AI agents adhere to regulatory standards and internal ethics guidelines, reducing both legal and reputational risk.

How Does Cekura Prevent Toxic and Unprofessional AI Behavior?

Conversational AI agents represent a company’s brand in every interaction. A single offensive or unprofessional response can undermine user trust instantly.

Cekura actively attempts to provoke toxic behavior by exposing agents to hostile, abusive, or manipulative user inputs. The goal is to see whether the agent maintains professionalism under pressure or devolves into harmful responses.

This form of testing is especially critical for customer-facing agents operating at scale, where even rare failures can impact thousands of users.

What Role Does PII and Data Leakage Testing Play?

Data leakage is among the most severe risks in conversational AI. Agents that inadvertently expose personally identifiable information, internal credentials, or sensitive business data can cause catastrophic damage.

Cekura’s Red Teaming includes targeted attempts to extract unauthorized data, such as credit card numbers, internal API keys, or user information the agent should not have access to.

By identifying these vulnerabilities early, organizations can reinforce access controls, tighten response policies, and ensure compliance with standards like HIPAA and PCI DSS.

What Is Red Teaming as a Service and Why Does It Matter?

While automated libraries cover many common attack patterns, no two enterprises face identical risks. This is where Cekura’s Red Teaming as a Service (RTaaS) becomes critical.

Through RTaaS, Cekura deploys Forward Deployed Engineers who work directly with customers to build custom adversarial test cases tailored to specific industries and use cases. Healthcare teams may focus on HIPAA compliance, while fintech companies prioritize PCI DSS and financial disclosure risks.

This hybrid model—combining scalable automation with human expertise—allows Cekura to address both breadth and depth, something purely self-serve tools struggle to achieve.

Who Are the Founders Behind Cekura?

Cekura was founded by three individuals who met more than eight years ago during their undergraduate studies at IIT Bombay, bringing together complementary expertise across engineering, research, and enterprise operations.

Tarush Agarwal, Co-Founder and CEO, comes from a quantitative finance background. He spent three years building ultra-low latency systems measured in nanoseconds and helped transform a loss-making trading strategy into a desk generating millions in MRR within three months. His experience in high-stakes, performance-critical systems informs Cekura’s obsession with reliability.

Shashij Gupta, Co-Founder and CTO, brings deep research expertise. He previously worked as a quantitative researcher and conducted NLP research at Google Research. His first-author paper on testing transformers, produced during his time at ETH Zurich, has received over 50 citations, underscoring his focus on making AI systems testable and trustworthy.

Sidhant Kabra, Co-Founder and President, comes from a consulting and operations background. He has advised Fortune 500 CEOs in FMCG and medical devices, managed large customer experience teams, and driven rapid growth at an edtech startup that scaled from zero to over 200,000 users in six months.

Together, the founding team combines deep technical rigor with real-world enterprise understanding—a combination well suited to the challenges of conversational AI QA.

Why Is Cekura Positioned for the Next Phase of Conversational AI?

As conversational AI becomes infrastructure rather than novelty, expectations around safety, compliance, and reliability will only increase. Enterprises will demand the same level of assurance from AI agents that they expect from traditional software systems.

Cekura is building the tooling, processes, and standards required to meet that demand. By focusing on automated QA, observability, and adversarial testing, the company addresses one of the most critical bottlenecks in AI adoption.

In a future where AI agents handle sensitive conversations, financial decisions, medical guidance, and customer relationships, testing is not a feature—it is a prerequisite. Cekura’s mission is to ensure that conversational AI earns the trust required to operate at scale, safely and responsibly.