# Cekura: testing and monitoring for voice agents

Canonical: https://mudpie.ai/companies/cekura-ai/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Cekura: testing and monitoring for voice agents](https://mudpie.ai/companies/cekura-ai/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

## What it does

Cekura tests and monitors voice and chat agents across their lifecycle. It simulates conversations before launch, benchmarks providers and models, red-teams for jailbreaks and PII leakage, monitors production calls and helps teams turn failures into version-controlled fixes. The [YC profile](https://www.ycombinator.com/companies/cekura-ai) says it works across healthcare, BFSI, logistics, recruitment and retail; the [current homepage](https://www.cekura.ai/) shows the self-improving loop.

The fit is a conversational-AI team that needs confidence before deploying a voice agent and visibility after it reaches production. Cekura is useful when a static eval set is not enough: teams need adversarial scenarios, live drift, latency and a repeatable deploy gate.

## Why I’d look closer

The product surface is unusually complete. Cekura shows scenario runs for appointment booking, cancellations, insurance questions, emergency escalation, Spanish-accent billing and adversarial refunds. It supports synthetic conversations, provider bake-offs and production monitoring, with findings linked to failure evidence. The company says it works with 75+ customers; that is company-reported traction.

The founder backgrounds match the technical and operational problem. The [YC biographies](https://www.ycombinator.com/companies/cekura-ai) describe Tarush Agarwal with low-latency quant systems and IIT Bombay CS/statistics, Shashij Gupta with quant research, Google NLP research and ETH Zurich work, and Sidhant Kabra with consulting, customer-experience and growth experience.

## What I’d ask

Which scenarios and scoring rubrics are customer-controlled, how are PII and call recordings handled, and what happens when a test finds a dangerous behavior? I’d run a small synthetic suite, compare Cekura’s scoring to human review, then monitor one production workflow with approval gates for prompt or routing changes. The source set did not expose a numeric pricing card in this profile.

## My editorial take

Cekura is a strong fit for teams that understand conversational quality is a lifecycle problem. The benchmark and monitoring loop is more valuable than a one-time red-team report. The buyer should judge it on reproducible failure detection and safe improvement, not a green dashboard.

## Quick facts

| Field | Sourced detail |
|---|---|
| Buyer fit | Teams shipping voice/chat agents in high-stakes or high-volume workflows |
| Product | Simulation, evals, red-teaming, monitoring and self-improvement |
| Traction signal | Company reports 75+ customers |
| Public pricing | Not established in the fetched pricing surface |

## Sources checked

Checked 2026-09-19.

| Source | Used for |
|---|---|
| [YC company profile](https://www.ycombinator.com/companies/cekura-ai) | Product, founders and customer claim |
| [Cekura homepage](https://www.cekura.ai/) | Current testing/monitoring workflow |
| [Cekura pricing](https://www.cekura.ai/pricing) | Pricing surface check |
| [Voice AI latency guide](https://www.cekura.ai/blogs/voice-ai-latency-guide) | Public technical content surface |

## Cohort context

Cekura is listed in Fall 2024. In our 2026-09-18 directory snapshot, 57 of 94 listed companies in that cohort have YC’s primary industry label B2B (60.6%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:14:32.727Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Observed |
| Pricing link | Observed |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.