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Sensi.AI builds a voice-first care intelligence platform for home care operators, using Python + Node.js + Kafka on AWS/Kubernetes to process audio streams. The tech stack reveals a data-intensive, LLM-native architecture: heavy adoption of LangChain, LangGraph, and Gemini signals a shift toward agentic AI workflows rather than traditional rule-based monitoring. Active projects center on automation (customer onboarding, knowledge capture, internal FP&A) and a shared backend platform, pointing to a transition from single-feature tool toward a broader care operations stack.
Tech Stack 42 technologies
Core StackPython Node.js AWS Kubernetes Kafka Langchain React React Native Docker HubSpot Terraform Jenkins GitLab n8n Tableau Looker Mixpanel Power BI LangChain TypeScript PostgreSQL Playwright JavaScript Confluence Jira AWS SQS LangGraph Aircall LlamaIndex GCP+11 more
What Sensi.AI Is Building
◆Challenges
- Manual repetitive processes across gtm
- Customer onboarding and retention
- Knowledge base maintenance
- Complex healthcare landscapes
- Multiple stakeholders
- Bridging gap between automated system and clients
- Issue resolution
- Improving quality of care
- Building automated fp&a processes
- Scaling high-growth saas
▲Active Projects
- Shared backend platform
- Ai enablement layer
- Observability and quality story
- Internal knowledge base automation
- Ai-powered customer onboarding automation
- Ai-driven knowledge capture system
- Land and expand strategy
- Implementing care intelligence platform
- Ai-native platform development
- Ai-driven feature integration
Hiring Activity
15 roles · 15 in 30d
Department
Seniority
Company intelligence
