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Observe.AI is the Conversation Intelligence Platform for Contact Centers that uncovers insights from 100% of customer interactions to maximize frontline team performance and drive outcomes across the business, from more sales to higher retention and better compliance.
Accelerate CX testing, increase quality across digital and voice channels, and assure omnichannel customer journeys end-to-end.
Vattara AI is the observability and evaluation layer for enterprise voice agents built to catch failures before they reach your customers. We simulate thousands of real-world call scenarios pre-deployment, monitor every live conversation in production, and pinpoint exactly where your voice stack breaks across the full STT → LLM → TTS chain. Purpose-built for voice AI teams in BFSI, Healthcare, and E-Commerce who need more than platform-native metrics — they need a neutral, audit-grade system that holds their agents to a higher standard.
Evalgent is an AI-powered voice agent evaluation platform designed to test, monitor, and improve conversational AI systems before and after deployment. Key features: - Scenario-based testing to validate end-to-end workflows - User behaviour-based stress testing (interruptions, accents, speech pace, etc.) - Evidence-based evaluations with actionable recommendations - Custom metrics to benchmark agent performance - Human-in-the-loop review for nuanced quality validation How it works? 1. Define: Create real-world scenarios and define clear success criteria 2. Run: Test them across diverse and unpredictable user behaviors 3. Measure: Understand successes, failures, and performance boundaries 4. Act: Identify what to fix, optimize, or scale with confidence Evalgent is for AI teams, product teams, and enterprises building voice agents for customer support, sales, and operations. Don’t ship agents that only work in demos. Test real-world reliability with Evalgent.
Sherlock Calls is a call intelligence platform for teams running voice AI in production. When a call fails, evidence is split across multiple vendors — Twilio telephony events, ElevenLabs audio and TTS behaviour, webhooks, CRM records, etc. with timestamps that rarely align. Most teams resort to manual log correlation across four or five dashboards before they can identify the root cause. The process commonly takes two to three hours. Sherlock Calls connects to a team's existing stack via OAuth and correlates provider data into a structured incident case file posted directly in Slack. The case file contains a single cross-provider timeline, a root cause hypothesis with layered evidence, and first checks in triage order. The output lives in the same Slack thread where the on-call alert fires — no additional context or dashboard switching during an incident.
Isara is an independent monitoring and QA layer for AI agents. As companies hand customer support, customer success, and financial interactions over to AI, the only record of what those agents actually said and did usually lives inside the platform that sold you the AI — the same platform that grades its own performance. Isara gives you an independent view. Isara connects in minutes (no code changes) and analyzes every interaction across major customer service platforms — not a sample. It surfaces the things vendor dashboards structurally can't show you: hallucinations, wrong answers, unauthorized actions, policy and compliance risks, and the "resolved" conversations that quietly left customers frustrated enough to churn. Each finding links back to the exact conversation and gives your team the precise fix, building an independent audit trail as you go. You refine your agents, Isara remeasures the impact, and improvement becomes systematic rather than guesswork. Built for Support, Customer Success, and Compliance teams who need to verify AI quality, protect revenue, and catch retention risk before it shows up in renewals. Integrates with Intercom, Zendesk, Salesforce, HubSpot, Slack, Gainsight, and more. Scales from hundreds to millions of interactions with real-time monitoring.
TekVision's Automated Web Chat Testing solution is designed to optimize automated chat systems, ensuring seamless digital customer interactions without the need for live agent support. By thoroughly emulating live customer interactions, this solution identifies and addresses shortcomings in chatbot programming, enhancing both customer satisfaction and organizational efficiency. Key Features and Functionality: - Comprehensive Testing: Accurately emulates live customer interactions to identify issues such as invalid prompts, inaccurate data returns, poor intent prediction, and incorrect agent routing. - Intuitive User Interface (UI): Facilitates easy creation of test cases, reducing the time and guesswork associated with manual testing methods. - Pinpoint Accuracy: Determines the exact location, timing, and nature of issues within the conversation flows of NLU-based web chat systems. - Limitless Scalability: Allows testing and verification of every possible interaction scenario, regardless of the size of the knowledge base. - Actionable Insights: Captures precise data to correct issues or adjust training promptly, fostering continuous customer experience improvement. Primary Value and Problem Solved: The Automated Web Chat Testing solution empowers organizations to deliver flawless, 24/7 customer support through automated website chatbot services. By proactively identifying and rectifying issues, it ensures that customers receive accurate and timely responses, reducing frustration and enhancing trust in the brand. This leads to faster service for customers, reduced workloads for live agents, and improved operating costs, ultimately resulting in increased positive self-service outcomes and a superior overall customer experience.
Bespoken.ai offers a comprehensive Model Testing solution designed to validate and ensure the accuracy, functionality, and safety of Automatic Speech Recognition (ASR), Large Language Models (LLMs), and Natural Language Understanding (NLU) models. This service assists companies in integrating LLM-based chat and voice bots, providing confidence that their applications deliver correct information and protect users from misleading or harmful responses. Key Features and Functionality: - Automated Test Case Generation: Bespoken.ai automatically generates test cases to streamline the validation process. - Four-Stage Validation Pipeline: 1. Entity-Based Validation: Ensures the model accurately identifies and responds to entities like people, places, and objects. 2. Rules-Based Validation: Verifies the model adheres to predefined rules and guidelines. 3. LLM-Based Validation: Utilizes large language models to test and identify potential issues within the application. 4. Manual Validation: Involves human reviewers to confirm all tests are passed and to detect any additional problems. - Continuous Monitoring: Employs LLMs to repeatedly query and verify applications during development and post-deployment phases. - Detection of Hallucinations: Identifies and alerts users to incorrect or misleading responses generated by the model. Primary Value and Problem Solved: Bespoken.ai's Model Testing solution addresses the critical need for reliable and safe LLM implementations in chat and voice bots. By providing a thorough validation pipeline and continuous monitoring, it ensures that applications function correctly, deliver accurate information, and safeguard users from potential risks associated with incorrect or harmful responses. This comprehensive approach allows companies to confidently deploy LLM-based solutions, enhancing customer trust and satisfaction.
Chatbot dashboards tell you how much your bot did, not how well it did. Containment, CSAT and response time can look healthy while the bot gives wrong answers or frustrates customers who never fill out a survey. Inquio reads your real transcripts and returns a Bot Report Card: a prioritized list of what went wrong, how often, and what to fix first. Upload by CSV or connect via API. Analysis runs in minutes. What Inquio finds: • Incorrect and outdated answers, delivered confidently • Missed and misrouted intents that never triggered a fallback • Customer frustration and silent abandonment no survey captures • Safety incidents and policy violations (Defender) • Buying signals and churn risk buried in support chats (Sales Radar) The Inquio Score tracks quality over time and benchmarks against industry norms. Issues are ranked by severity, so teams fix the expensive problems first. For CX teams, conversation designers, and anyone running a live bot.