Cloud and data transformation partner for BFSI, AgriTech, and eCommerce
CoffeeBeans is a Bangalore-based consulting firm built around cloud, data engineering, and AI/ML modernization for mid-market enterprises. The tech stack spans all three major clouds (AWS, GCP, Azure) plus a deep data infrastructure layer (Kafka, Spark, Flink, Airflow, dbt, Databricks, Snowflake, BigQuery), with recent project velocity concentrated on RAG pipelines, GenAI copilots, and LLM fine-tuning. Hiring is heavily data-skewed (10 of 17 open roles) and senior-weighted, signaling either aggressive scale of existing delivery or a shift toward higher-margin AI/data consulting engagements.
CoffeeBeans Consulting delivers end-to-end cloud and data modernization services to mid-market organizations across BFSI, AgriTech, eCommerce, and supply-chain verticals. Founded in 2017, the firm operates from Bangalore with 201–500 employees and works across product development, application modernization, DevOps, infrastructure, and emerging AI/ML capabilities. Current project focus spans real-time analytics, fraud detection, personalization, and generative AI integration; internal pain points center on data fragmentation, legacy system decryption, and operational efficiency—typical of a services firm scaling delivery volume and client handoff complexity.
CoffeeBeans deploys AWS, GCP, and Azure; orchestrates with Kafka, Pub/Sub, and Kinesis; transforms data via Spark, Flink, Airflow, and dbt; and builds ML/GenAI with MLflow, Kubeflow, LangChain, scikit-learn, XGBoost, and OpenAI/Cohere/Mistral APIs.
Active projects include RAG pipelines, GenAI-powered copilots, LLM fine-tuning for domain-specific tasks, end-to-end data platform architecture, real-time analytics, fraud detection, and Kubernetes platform operations.
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CoffeeBeans'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.