Layerup AI Agents vs Sesame

Comparing the features of Layerup AI Agents to Sesame

Feature
Layerup AI Agents
Sesame

Capability Features

100+ Accents
100
50+ Compliance Packages
50+ Compliance Packages
80+ Languages Supported
80
Adaptive Conversational Tone
Advanced Data Analytics
AI Agents for Claims Operations
Analytics and Insights
Automated Appointment Scheduling
Automated Compliance Checks
Available 24/7
Built-in Regulatory Compliance
CFPB Compliance
Comprehensive Audit Trail
Consistent Personality
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Cultural Sensitivity Training for AI
Custom Workflow AI Agents
Customer Service AI Agents
Customizable Autonomous Agents
Dataset Size
1 million hours
Dedicated Implementation
Dynamic Script Optimization
Emotional Intelligence
Engineer-led Implementation
Evaluation Suite
FDCPA Compliance
Gender Voice Options
GPT-5 Support
Inbound & Outbound Voice AI
Instant Onboarding
Loan & Mortgage Application Support
Machine Learning Predictions
Model Sizes
Tiny: 1B backbone, 100M decoderSmall: 3B backbone, 250M decoderMedium: 8B backbone, 300M decoder
Multichannel Support
phoneemailSMSWhatsApptext
Multiple Speaker Handling
No Overhead for Training/Space
Objective Metrics
Word Error RateSpeaker SimilarityHomograph DisambiguationPronunciation Consistency
Partial Multilingual Support Planned
Planned for 20+ languages
PCI DSS Compliance
Privacy Focused
Pronunciation Correction
Remembers Previous Interactions
Role-based Access
Secure by Design
Sentiment Analysis
Sequence Length
2048
Single-Stage Model
SOC 2 Compliance
Subjective Metrics
Comparative Mean Opinion Score
TCPA Compliance
Text and Audio Input
TextAudio
TILA Compliance
Training Epochs
5
UDAAP Compliance
Unlimited Scaling
User & Agent Activity Reports
Voice AI for Collections
Zero Data Retention Policy

Integration Features

GitHub Release
Integration Interoperability
Company's internal systems, ML models, on-prem apps, CRMs, ERPs
Integration with CRMs and ERPs
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer

Limitation Features

Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No AI Training on Customer Data
No Pre-trained Language Model Use
No Pricing Information
No Unnecessary Data Collection
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly

Pricing Features

Free Preview
Open Source
Apache 2.0