Level AI
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About
Level AI is an enterprise-grade intelligence system designed to unify and transform the customer experience journey. By leveraging advanced natural language understanding (NLU) and a specialized semantic engine, the platform captures and analyzes 100% of customer interactions across voice, email, and chat. Its primary goal is to turn unstructured conversation data into actionable insights that drive containment, resolution, and revenue while maintaining high service standards. Unlike traditional systems that rely on manual oversight, this tool provides a comprehensive view of every touchpoint in the customer life cycle. The platform operates through a suite of integrated tools including AI virtual agents that handle routine requests with human-level quality and understanding. For human-led interactions, the "QA-GPT" feature automatically scores interactions against custom organizational rubrics with near-human accuracy. It also provides "Agent Assist" for real-time guidance during live calls and "Agent Screen Recording" to provide visibility into the QA process. This allows managers to see exactly how tickets are handled, identifying gaps in agent workflows and resources that audio-only analysis might miss. This solution is specifically built for high-stakes industries such as financial services, insurance, healthcare, and retail where accuracy, governance, and real outcomes are paramount. It serves various roles within the organization: contact center leaders use it to streamline operations and identify growth drivers; quality assurance teams use it to replace manual sampling; and frontline agents benefit from personalized coaching plans based on their actual performance history. By bridging the gap between human and machine intelligence, it helps organizations scale their support without losing the quality of personal interaction. What distinguishes Level AI from traditional contact center tools is its move from manual sampling (typically 1-2% of calls) to 100% automated analysis. Its ability to combine screen recording with conversation intelligence provides a 360-degree view of the agent-customer interaction. Furthermore, its focus on "Agentic AI" ensures that virtual agents do not just chat but can take real actions across backend systems, facilitating a seamless handoff to human agents with full context when necessary. This results in significant improvements in customer satisfaction (CSAT) and agent efficiency.
Pros & Cons
Analyzes 100% of customer interactions instead of traditional 1-2% manual sampling.
Provides real-time agent assistance and guidance during live customer conversations.
Automates QA scoring with near-human accuracy using the specialized QA-GPT engine.
Integrates screen recording with audio analysis for a complete view of agent performance.
Supports high-stakes industries with specific solutions for healthcare, finance, and insurance.
Requires a demo request to access pricing and specific plan information.
Full implementation for complex contact centers may require dedicated setup time.
Use Cases
Contact Center Leaders can monitor team-wide performance indicators and identify business growth drivers through a unified dashboard.
Quality Assurance Teams can automate the scoring of every interaction across all channels, reducing manual monitoring time by up to 90%.
Customer Experience (CX) Leaders can detect sentiment shifts and root causes across thousands of interactions using Voice of the Customer insights.
Support Agents receive personalized coaching plans and real-time guidance based on their actual strengths and behavioral gaps.
Platform
Features
• voice of the customer insights
• real-time agent assist
• agent screen recording
• automated coaching plans
• omni-channel analytics
• ai virtual agents
• 100% interaction analysis
• qa-gpt auto-scoring
FAQs
How does Level AI improve the quality assurance process?
Level AI uses its QA-GPT engine to automatically score nearly 100% of interactions across all channels based on your custom rubrics. This replaces traditional manual sampling methods that typically only review 1-2% of total calls.
What is Agent Screen Recording and why is it useful?
Agent Screen Recording adds a visual layer to QA analysis by capturing the agent's screen during interactions. This helps managers identify if agents are struggling with specific software tools or if there are gaps in internal resources.
Can Level AI integrate with my existing contact center tech stack?
Yes, Level AI is designed to integrate with existing contact center software and tools. This allows the AI virtual agents to take real actions across your systems and ensures data consistency across teams.
How does Agent Assist help during live calls?
Agent Assist provides real-time guidance and prompts to support agents during live customer interactions. It ensures responses stay consistent with company policy and relevant to the specific context of the conversation.
Pricing Plans
Enterprise
Unknown Price• 100% automated QA scoring
• QA-GPT semantic engine
• Real-time Agent Assist
• AI Virtual Agents
• Agent Screen Recording
• Voice of the Customer insights
• Custom analytics dashboards
• Agent coaching plans
• Omni-channel support (Call, Chat, Email)
• Compliance redaction
Job Opportunities
DevOps Engineer
Optimize contact center performance and drive customer satisfaction through automated QA scoring, real-time agent coaching, and human-quality AI virtual agents.
Benefits:
Market-leading compensation
Education Requirements:
Bachelor's or above with a good academic background
Experience Requirements:
2-4 years of meaningful work experience in DevOps handling complex services
Other Requirements:
Strong expertise with GCP, Docker, Kubernetes, CI/CD, and Jenkins
Extensive experience with Terraform
Experience with HELM and Kubernetes HPA
Observability monitoring using Prometheus, Grafana, Loki, or Datadog
Responsibilities:
Design and build ML system infrastructure (cloud and on-premise)
Build operating dashboards for system tracking and root cause analysis
Identify and evaluate relevant tools to improve processes
Collaborate with AI team to drive ML projects to completion
Monitor system health, security, scalability, and reliability
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FullStack Engineer (Noida, India)
Optimize contact center performance and drive customer satisfaction through automated QA scoring, real-time agent coaching, and human-quality AI virtual agents.
Benefits:
Market-leading compensation
Education Requirements:
B.E/B.Tech/M.E/M.Tech from Tier-I engineering institutes
Experience Requirements:
1–3 years of full-stack development
1+ years of web application development
Other Requirements:
Strong proficiency in JavaScript/TypeScript, HTML, and CSS
Solid understanding of React ecosystem
Experience with Django (Python) and Node.js (NestJS)
Experience with relational (PostgreSQL) and NoSQL (MongoDB) databases
Experience with Redis or similar caching systems
Responsibilities:
Own end-to-end development of features and tools
Build web interfaces for customer-facing products
Turn designs and wireframes into production-ready code
Develop features using React and backend services (Django, NestJS)
Design and implement RESTful APIs and GraphQL interfaces
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Senior Machine Learning Engineer - NLP
Optimize contact center performance and drive customer satisfaction through automated QA scoring, real-time agent coaching, and human-quality AI virtual agents.
Benefits:
Market-leading compensation
Education Requirements:
B.E/B.Tech/M.E/M.Tech/PhD from tier1 engineering institutes
Computer science or mathematics-related fields
Experience Requirements:
3+ years of experience in AI/ML
Other Requirements:
Strong coding skills in Python
Familiarity with LangChain or Transformers
Interest in LLMs, agents, and open-source AI ecosystem
Responsibilities:
Assist in building LLM-powered agents
Support prompt engineering, RAG, and tool integrations
Collaborate on experiments with open-source and commercial LLMs
Implement and evaluate reasoning, planning, and memory modules
Deploy and monitor AI features in production
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