AdTech analytics platform with AI-powered insights and dashboards
Ven Analytics builds analytics solutions for adtech companies using Power BI, Python, and a modern data stack (Spark, Hadoop, cloud platforms). The stack reveals a shift toward generative AI—LangChain, RAG, vector databases (Pinecone, Weaviate), and LLM integrations (OpenAI, Anthropic, Llama) are production-level, not experimental. Hiring is tilted heavily toward engineering (4 of 6 open roles) with mid-level dominance, paired with internal pain around recruitment bottlenecks—a classic signal of rapid scaling outpacing hiring infrastructure.
Ven Analytics is a 11–50 person analytics consultancy founded in 2019, based in Mumbai. The company helps mid-market and enterprise organizations transition from intuition-driven decisions to data-driven strategy through custom dashboards, analytics implementations, and machine-learning-powered insights. Their technical footprint spans Power BI for reporting, Python and Go for backend services, Angular/React for frontends, and cloud infrastructure (AWS, GCP, Azure). Recent project work includes policy implementation and compliance audit support, suggesting exposure to regulated verticals. The team operates India-based with active growth in engineering capacity.
Power BI for dashboarding, Power Query and DAX for data modeling, Python for custom analytics, and Apache Spark + Hadoop for distributed data processing. They also integrate cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
Yes. The stack includes OpenAI, Anthropic, and Llama, alongside RAG frameworks (LangChain, LangGraph, LlamaIndex) and vector databases (Pinecone, Weaviate, FAISS), indicating production use of AI-augmented analytics and insights generation.
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Ven Analytics Pvt. Ltd.'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.