Question 1

What is a modern data platform and why does my business need one?

Accepted Answer

A modern data platform is the infrastructure layer that ingests, processes, governs, and serves data reliably across your organization. Without it, AI and machine learning projects fail because the data they depend on is inconsistent, siloed, or ungoverned. We build data platforms on proven open-source and cloud-native technologies — Apache Iceberg, dbt, Airflow, Spark — so your data is always accurate, auditable, and ready for both analytics and AI workloads.

Question 2

What is the difference between an AI Agent and a Generative AI system?

Accepted Answer

A Generative AI system (like a RAG application or an internal knowledge base) answers questions or generates content based on your data. An AI Agent goes further: it can reason, make decisions, use tools, and execute multi-step tasks autonomously. We build both, and often combine them into agentic platforms that operate inside real business workflows.

Question 3

How long does it take to deploy a data or AI solution to production?

Accepted Answer

A focused AI agent or data pipeline can reach production in 6–10 weeks. A full data platform or agentic system typically takes 3–6 months. We start every engagement with a discovery and architecture phase so that timelines are grounded in reality before a single line of code is written.

Question 4

What technologies do you use to build AI and data systems?

Accepted Answer

Our core stack is cloud-native and production-proven: AWS and Amazon Bedrock for cloud infrastructure and LLM access, Python and TypeScript for application logic, LangGraph for agentic orchestration, Apache Iceberg and dbt for data modeling, Airflow and Spark for pipelines, Kubernetes and Terraform for infrastructure, and OpenSearch for vector and full-text search.

Question 5

How do you ensure AI systems are production-ready and not just demos?

Accepted Answer

Production readiness is designed in from day one. Every system we build includes observability (logging, tracing, alerting), security controls (data access policies, model guardrails), and operational runbooks. We keep human review and escalation paths where decisions carry business risk, and systems are load-tested and monitored before they handle real traffic.

Question 6

What is Intelligent Automation and how is it different from traditional RPA?

Accepted Answer

Traditional RPA automates rigid, rule-based tasks by mimicking UI interactions — it breaks when the interface changes. Intelligent Automation replaces brittle scripts with AI-powered workflows that can understand context, handle exceptions, and adapt to variation. We integrate AI agents directly into business processes through APIs and event-driven architectures, making automation resilient and scalable.