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Corti Tech Stack

Enterprise AI models for healthcare via API

Software Development Brooklyn, New York 51–200 employees Founded 2016 Privately Held
corti.ai LinkedIn

Corti builds inference-heavy healthcare AI, evident from a production-focused stack (PyTorch, TensorFlow, NVIDIA Triton, vLLM, Kafka) paired with infrastructure challenges around low-latency serving and 100k concurrent stream scaling. The org is engineering-led with active adoption of GitOps and Model Context Protocol, signaling a shift toward operational maturity and AI agent tooling. Pain points cluster around production reliability (model drift, latency) and multi-tenant infrastructure — common for APIs moving from pilot to enterprise workloads.

Tech Stack 35 technologies

Core StackGo Kubernetes Docker AWS Python MLflow Kubeflow ArgoCD FastAPI PyTorch Apache Kafka Java GraphQL CockroachDB Elasticsearch Azure DevOps Jenkins Kafka gRPC Drata TensorFlow Loki Grafana Azure GCP DVC vLLM NVIDIA Triton Inference Server WebSockets WebRTC+2 more
AdoptingGitOps Replit Model Context Protocol

What Corti Is Building

◆Challenges

  • Clinicians overwhelmed
  • Medical knowledge outpacing human capacity
  • Lack of access to quality healthcare
  • Scaling infrastructure for high concurrency
  • Maintaining multi-tenant cluster reliability
  • Model reliability in production
  • Model performance issues
  • Partner ecosystem growth
  • Scaling partner integrations
  • Low-latency inference

▲Active Projects

  • Automation tools for developer experience
  • Speech recognition and text generation backend
  • Low-latency ai integration pipeline
  • Infrastructure scaling for 100k concurrent streams
  • Ci/cd pipeline development
  • Ml model deployment automation
  • Model governance implementation
  • Agent store growth
  • Multi-tenant kubernetes setup
  • Certified solutions partner network

Hiring Activity

Accelerating15 roles · 9 in 30d

Department

Engineering
8
Product
3
Marketing
2
Sales
1
Security
1

Seniority

Senior
9
Mid
3
Lead
2
Staff
1
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About Corti

Corti provides healthcare AI models through a cloud API, targeting clinicians and healthcare systems. The platform delivers speech recognition, text generation, and decision-support capabilities built on PyTorch and TensorFlow, deployed across AWS, Azure, and GCP with Kubernetes orchestration. Engineering priorities center on low-latency inference pipelines, multi-tenant reliability, and scaling to handle concurrent workloads — reflected in active work on Kafka-based streaming, NVIDIA Triton optimization, and Kubernetes cluster management. The company operates a partner ecosystem and certified solutions program.

HeadquartersBrooklyn, New York
Company Size51–200 employees
Founded2016
Hiring MarketsUnited States, Denmark, United Kingdom

Frequently Asked Questions

What tech stack does Corti use?

Core stack: PyTorch, TensorFlow, Kubernetes, Docker, Apache Kafka, NVIDIA Triton Inference Server, FastAPI. Cloud: AWS, Azure, GCP. ML ops: MLflow, Kubeflow, DVC, ArgoCD. Monitoring: Loki, Grafana. Infrastructure: CockroachDB, Elasticsearch.

What is Corti working on?

Low-latency AI inference pipelines, infrastructure scaling for 100k concurrent streams, multi-tenant Kubernetes setup, ML model deployment automation, model governance, and agent store growth.

How this profile is built

Corti's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.