QuadSci.ai Tech Stack

AI platform predicting customer churn and revenue expansion from usage data

Software Development New York 11–50 employees Privately Held

QuadSci builds predictive AI for B2B revenue intelligence, processing billions of telemetry events to forecast churn and expansion up to 12 months ahead. The stack reveals a production-focused organization: full ML pipeline infrastructure (DSPy, FastAPI, Kafka, Kubernetes) paired with native CRM integrations (Salesforce, HubSpot) and emerging generative AI layers (RAG, prompting systems). Active hiring skews senior and principal engineers, with projects centered on scaling AI feature delivery on Vertex AI and deploying customer-facing agents—suggesting they're moving from predictive modeling into prescriptive, agentic workflows.

Tech Stack 41 technologies

Core StackJavaScript TypeScript React Vue Angular Node.js Python Java C# FastAPI Flask Salesforce HubSpot ServiceNow Zendesk Kafka PostgreSQL MySQL MongoDB Nginx Docker Kubernetes GraphQL HTML5 CSS Django REST Framework AWS SQS Pub/Sub Apache DSPy+11 more

AdoptingDSPy

What QuadSci.ai Is Building

◆Challenges

Internal processes distract from customers
Lack of ai-driven performance flywheel
Complex data source integration
Performance scalability concerns
Predicting arr
Improving gtm performance
Accurate arr predictability
Customer churn prediction

▲Active Projects

Rag pipelines
Retrieval systems
Prompting pipelines
Production-grade pipelines and apis
Customer integrations
Platform core capabilities
Quadsci ais (engage, economic forecasting, & growth)
Ai feature roadmaps (incl. genai applications)
Generative ai application interfacing with telemetry data
Deploying ai products at scale on vertex ai

Hiring Activity

Accelerating10 roles · 5 in 30d

Department

Engineering

Sales

Data

Seniority

Senior

Mid

Principal

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About QuadSci.ai

QuadSci develops AI products for GTM and product teams at B2B SaaS companies. The platform ingests product telemetry and engagement data to surface behavioral patterns, then applies machine learning to predict customer churn, expansion, and contraction. Two main products—Cohorts AI for usage-pattern discovery and Growth AI for trend forecasting—feed into Q-Chat, a family of AI agents that translate predictions into recommended actions. The company operates at 11–50 employees, headquartered in New York, with engineering presence in the United States and Mexico.

HeadquartersNew York

Company Size11–50 employees

Hiring MarketsMexico, United States

Frequently Asked Questions

What tech stack does QuadSci use?

Frontend: React, Vue, Angular, TypeScript, GraphQL. Backend: Python (FastAPI, Flask, Django REST Framework), Node.js, Java, C#. Data: Kafka, PostgreSQL, MySQL, MongoDB. Infrastructure: Docker, Kubernetes, AWS SQS, Pub/Sub, Nginx. Adopting DSPy for AI pipelines.

What is QuadSci working on?

Core projects include RAG and retrieval systems, generative AI applications interfacing with telemetry, production-grade ML pipelines and APIs, customer integrations, and deployment of AI products at scale on Vertex AI. Also developing AI feature roadmaps and agentic interfaces (Q-Chat) for GTM teams.