Applied AI & Smart Automation that works in
production, not just demos
Practical AI built around real business cases – Agentic AI, LLMs, NLP, machine learning, and intelligent automation deployed within your existing platforms. No AI for its own sake. No endless proofs-of-concept. Just measurable outcomes, governed and embedded into daily operations
Most enterprises don’t lack data - they lack the infrastructure to use it
After years of platform investments, the reality for most mid-to-large enterprises is a fragmented, expensive, and underperforming data landscape. Tools accumulate. Pipelines break. Teams firefight. ROI disappears into complexity. This is the problem Ignitho was built to solve — not by adding more technology, but by applying Frugal Innovation: making what you already own work at its full potential
Pilots that never reach production
AI models validated in sandboxes that never get deployed into live systems. The business never sees the value. Engineering gets blamed. The project gets quietly shelved
Black-box models nobody trusts
Data trapped across cloud APIs, legacy files, and SaaS platforms — fragmented ecosystems that make unified reporting impossible without manual intervention.
Data quality & decision risk
Frequent inconsistencies and errors in source data that cascade into unreliable dashboards, delayed board reporting, and flawed strategic decisions.
Cloud cost overruns
Poorly optimized queries, uncompressed data formats, and over-provisioned infrastructure that inflate cloud bills while delivering no additional insight.
Full data platform lifecycle
Our data engineering practice covers the full data platform lifecycle — from raw ingestion through to business-ready intelligence layers. Every engagement is anchored to your existing technology investments, not a new vendor stack
Real-Time Data Streaming & Pipeline Engineering
Build event-driven, low-latency data pipelines that move, transform, and validate data at speed. We architect streaming infrastructure on Kafka, Kinesis, and Azure Event Hubs — enabling real-time decisioning, fraud detection, and operational intelligence without overhauling your existing landscape
Modern Data Warehouse & Lakehouse Design
Architect scalable, cost-efficient data warehouses and lakehouses on Snowflake, Databricks, and cloud-native platforms. We migrate legacy systems, implement medallion architectures, and establish data contracts that make your warehouse a reliable source of truth
ETL/ELT Pipeline Optimization & Reliability Engineering
Rescue, stabilize, and optimize broken or inefficient data pipelines. We audit ETL processes, rewrite transformations, remove manual interventions, and implement monitoring
Cloud Migration & Platform Modernization
Execute low-risk migrations from legacy on-prem infrastructure to cloud-native platforms. We run parallel workloads, validate parity, and cut over only when confidence is achieved. We specialize in Oracle, SQL Server, and Teradata migrations to Snowflake and Databricks on AWS or Azure — often achieving 30–50% reduction in cloud consumption bills
Data Quality, Governance & Observability
Implement data quality frameworks, automated testing, and observability tooling. We build Great Expectations suites, Monte Carlo integrations, and dbt testing layers
Data Platform Architecture & Consulting
Independent advisory for CDOs, CIOs, and Heads of Data Engineering to evaluate architecture, make stack decisions, and build a pragmatic roadmap