TL;DR Cekura helps companies ship and scale reliable voice & chat AI agents by providing end-to-end testing and observability

Watch our demo video here

Problem: Making Conversational AI agents reliable is hard. Manually calling your agents or listening through thousands of calls is slow, error-prone and does not provide the required coverage.

Our Solution: At Cekura, we work closely with you at each step of the agent-building journey and help you improve and scale your agents 10 times faster

Key Features:

Testing:

• Scenario Generation: Create varied test cases from agent descriptions automatically for comprehensive coverage.
• Evaluation Metrics: Track custom and AI-generated metrics. Check for instruction following, tool calls, and conversational metrics (Interruptions, Latency, etc).
• Prompt Recommendation: Get actionable insights to improve each of the metrics.
• Custom Personas: Emulate diverse user types with varied accents, background noise, and conversational styles.
• Production Call Simulation: Simulate production calls to ensure all the fixes have been incorporated.

Observability:

• Conversational Analytics: Provides customer sentiment, interruptions, latency and call analytics: ringing duration, success rate, call volume trends, etc
• Instruction Following: Identify instances where agents fail to follow instructions.
• Drop-off Tracking: Analyzes when and why users abandon calls, highlighting areas of improvement.
• Custom Metrics: Define unique metrics for personalized call analysis.
• Alerting: Proactively notifies users of critical issues like latency spikes or missed functions.

Ready to make your Voice & Chat Agents Reliable?

• Book a call: https://cal.com/kabrasidhant
• Email us: founders@cekura.ai

The Team:

We met over eight years ago during our undergraduate at IIT Bombay.

Tarush comes from quantitative finance, where he worked on simulations for ultra-low latency trading strategies (think nanoseconds!).

Shashij has previously researched NLP at Google Research and is the first author of a paper on testing AI systems reliably, which has 50+ citations from his work at ETH Zurich.

Sidhant comes from a consulting background advising CXOs at Fortune 500 companies in FMCG and medical devices. He managed P&L in a leading contact centre.