Lera operates at the intersection of legacy banking systems and AI-powered decision automation. The stack reveals a dual architecture: deep T24/jBASE (core banking) expertise paired with modern Python, PySpark, and LLM infrastructure (OpenAI, LangChain, LangGraph). Projects span NL-to-SQL systems, RAG pipelines, and T24 modernization—pointing to a core mission of unlocking dark data in financial institutions and automating complex operational workflows. Hiring velocity is accelerating with balanced department growth, suggesting they're moving from services-led to product-led delivery.
Lera helps enterprises translate data and AI investments into measurable business outcomes, focusing on financial services and core banking modernization. The company operates from Hyderabad with global reach and deep domain expertise in legacy banking systems (T24, jBASE) combined with modern data and intelligence platforms. Their platform layer—combining data warehousing, business intelligence, and agentic AI—targets gaps in data governance, integration, and operationalization across finance, risk, and operations. With 20+ years of delivery experience in production banking systems, they position themselves as a bridge between legacy IT infrastructure and next-generation AI automation.
Lera uses T24 and jBASE for core banking, plus Python, PySpark, Spark, and Hadoop for data processing. AI layer includes OpenAI, LangChain, LangGraph, and vector databases (FAISS, Chroma). Cloud: Azure, AWS, GCP. Infrastructure: Docker, Kubernetes, PostgreSQL, MySQL, Oracle.
Projects include NL-to-SQL systems, RAG pipelines, T24 application improvements, the GenBI platform, FinSight 360, and scaling product practice. Focus on data modernization, integration, and governance challenges in banking.
Lera Technologies'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.