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Level AI Tech Stack
AI agents and quality intelligence for sales and customer service
Level AI builds AI-native automation for enterprise sales and support teams. The stack reveals a company deeply invested in production ML: PyTorch, Transformers, and a palette of open LLMs (BERT, Llama, Qwen, Gemma, DeepSeek) paired with LangChain/LangGraph for agent orchestration, all running on Kubernetes across AWS/GCP/Azure. Active hiring is heavily engineering-weighted (16 of 24 roles), with senior and lead levels dominating—a signal that the core challenge is scaling inference pipelines and agent infrastructure at low latency, not selling or designing.
Tech Stack 88 technologies
What Level AI Is Building
◆Challenges
- Low-latency inference pipelines
- Speech-to-text pipeline challenges
- Scaling ai inference at scale
- High-volume enterprise environments
- Enhancing agent performance
- High throughput low latency inference
- Scaling ai agent infrastructure
- Ensuring efficient time to value
- Transcription accuracy issues
- Category displacement challenge
▲Active Projects
- Scalable production nlp pipelines
- Scalable backend systems powering ai agents
- Post-deployment monitoring and continual learning capabilities
- Technical discovery sessions
- Low-latency inference pipelines
- Agent orchestration frameworks
- Product positioning across platform
- Sales content library development
- End-to-end gtm for product launches
- Asr engine development
Hiring Activity
25 roles · 25 in 30d
Department
Seniority
Company intelligence