B2B research intelligence platform powered by AI and verified expert networks
NewtonX operates a B2B research platform combining expert networks, survey automation, and AI-native tooling to accelerate market intelligence workflows. The stack is modern and data-forward (React + Python + Django on GCP/Kubernetes, with Kafka, BigQuery, and dbt for data pipelines), but the project list reveals internal friction: they're simultaneously building semantic search across proprietary corpora, fusing structured and unstructured data, and optimizing expert acquisition—classic signals of a platform scaling faster than its data infrastructure. Hiring is accelerating across engineering and sales, with notable investment in senior and staff-level roles.
NewtonX is a B2B research intelligence platform founded in 2017 and based in New York. The company serves mid-market to enterprise research teams at companies across 140+ industries, offering AI-accelerated expert surveys, market research, qualitative and quantitative research, and custom recruiting. The platform combines verified expert networks with automation and research expertise to support faster, defensible decision-making. Active projects center on scaling the core matching engine (connecting experts to clients, automating expert opinion capture), building partner channels (agency program expansion), and strengthening the data foundation (unified pipelines, semantic search, event instrumentation).
Frontend: React, TypeScript, Redux, Tailwind CSS. Backend: Python, Django, Node.js. Data: PostgreSQL, BigQuery, dbt, Kafka, Pub/Sub. Infrastructure: GCP, Kubernetes, Docker, Terraform. Analytics: Mixpanel, Looker.
Core priorities include expert-to-client matching, AI-automated opinion capture, agency partnership expansion, scalable process automation, semantic search across proprietary research corpora, and unified data pipeline design for event tracking and experiments.
NewtonX'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.