3 case studies

Case studies

How we approach a sector's data problem: what makes it hard, the architecture that answers it, and what the business gets. Worked through in full, so you can judge the thinking rather than the summary.

Pharmaceutical · Data Engineering

Building a Unified Data Platform for a Pharmaceutical Company

ERP, CRM, manufacturing, inventory, distributor, pharmacy, hospital, e-commerce and finance data sat in disconnected systems. This is the platform that brought them into one governed foundation for reporting and analytics.

  • Cloud Data Lake
  • ETL / ELT
  • Spark / PySpark
  • Data Warehouse
  • Lakehouse
  • Master Data Management
  • Data Governance
  • BI & Reporting

How it flows

  1. Data sources
  2. Ingestion
  3. Cloud data lake
  4. Processing
  5. Warehouse / lakehouse
  6. Analytics & consumption

Challenge, architecture and outcomes

Enterprise operations · Agentic AI

AgentOS — An Enterprise Multi-Agent AI Workforce

A reference architecture for AI agents that complete work rather than describe it: a supervisor delegating to specialists, retrieval over approved company knowledge, typed tools onto real business systems — and a governance layer that decides whether any of it is allowed to run.

  • Agentic AI
  • Multi-agent
  • RAG
  • Tool Calling
  • Human-in-the-loop
  • Approval Policy
  • Audit Trail
  • Least Privilege

How it flows

  1. Experience layer
  2. Orchestration layer
  3. Specialist agents
  4. Governance layer
  5. Enterprise systems

Challenge, architecture and outcomes

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Send the problem rather than a spec. We will tell you what it takes, who would work on it, and whether we are the right people for it.

Vijeesh TP

Vijeesh TP

Founder