4.7 (200+ reviews)
Full-stack AI for customer experience
Proprietary infrastructure from the ground up. GPU hardware, CX-native AI models, and the apps your team uses.
Level AI Platform
AI agents, insights, coaching, and QA for customer experience.
Level AI Platform
Full suite of AI-native applications for customer experience
Model Harness
A control layer for routing, guardrails, and governance in production
Level AI Latitude
Seven proprietary fine-tuned models for domain-specific tasks
Orba
transcription
Automatic Speech Recognition (ASR) built for contact center speech.
Redactor
redaction
Dual-layer redaction across transcript and audio.
Attune
intent detection
NLU that provides 400x more coverage than keyword libraries.
Crux
summarization
Actionable intelligence from every conversation.
Veridia
inferred csat
Analyzes satisfaction in 100% of conversations without surveys.
Qualix
automated qa
Scores every interaction against your QA & compliance rubrics.
Tenor
voice of customer
Daily classification of contacts into 3-level hierarchy.
Data foundation
Integrations with your existing data to feed unified customer intelligence
Integrations
Voice
Chat
CRM
Tickets
Knowledge
Surveys
Metadata
Organizational context
Topics
Rules
Teams
Agents
QA rubrics
Custom filters
Entity resolution
Evaluation & control
Transcripts
Sentiment
Insights
QA scores
Trends
Volume
Cohorts
Compute
Owned infrastructure for the performance, cost efficiency, and reliability enterprises demand
Infrastructure
Owned Nvidia GPU cloud
Runtime performance
Cost efficiency
Reliability
Runtime
Session persistence
Async streaming
Fault tolerance
tokens processed per year
Lower cost to serve
Purpose-built models reduce the cost to serve across high-volume CX workloads
Higher throughput
Optimized routing and specialized models process more customer interactions faster
lower latency
Lower-latency inference keeps AI usable in live customer-facing workflows
features
Built to run directly inside the enterprise
Permissions, calibration, metadata, review paths, evidence, and feedback are configured as part of the system.
Feedback loops
Corrections route through review and shape future behavior in a controlled way.
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Review paths
Auto, human review, and escalate routes coexist in one model.
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Permissions
Visibility and workflows match enterprise operating structures.
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Calibration cadence
Recalibrated against human QA leads on a monthly cadence.
Metadata fusion
Context is fused in before downstream decisions.
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Evidence
Outputs remain tied to supporting interaction signals and reasoning.
Deployment surface
Runs across cloud and VPC surfaces without rework.
Audit trail
Every output keeps a reviewable trail across teams and time.
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