We architect the data platforms, knowledge systems, and AI-ready foundations that let enterprises move faster, reason deeper, and ship products that learn. End to end, from ingestion to intelligence.
Every AI initiative, from LLMs to predictive models to real-time intelligence, needs clean, governed, semantically consistent data. Without that foundation, AI projects stall, hallucinate, or fail outright. We build the infrastructure that makes AI trustworthy.
AI is the top floor of a building. You cannot skip the foundation and expect it to stand. Under every working AI deployment is a platform with quality data, clear lineage, enforced governance, and a semantic layer that gives models the context to reason correctly.
A structured, repeatable delivery model built for enterprise complexity, from first conversation to a production platform.
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We audit your existing data landscape: sources, systems, quality, and gaps. A current-state assessment that grounds every decision that follows.
We translate business objectives into data requirements. Use cases, KPIs, SLAs, and success criteria defined and signed off before architecture begins.
An end-to-end blueprint covering ingestion, storage, processing, governance, and consumption. Platform-agnostic design first, then stack selection.
Cloud environment setup, IaC provisioning, security baseline, networking, and CI/CD pipelines. The operational backbone everything else runs on.
Ingestion pipelines, ELT and ETL, streaming, and batch processing built to a medallion architecture. Reliable, observable, and built to scale.
Semantic and dimensional modeling that turns raw data into consistent, business-ready entities. The layer that makes analytics and AI trustworthy.
Lineage, cataloging, access control, quality SLAs, and GDPR compliance wired into the platform, not bolted on after the fact.
Entity relationships, semantic modeling, and graph-powered reasoning that give AI systems the context they need to generate reliable outputs.
Self-serve dashboards, semantic models, and KPI frameworks your teams act on, built on a foundation they can trust.
Feature stores, model training, MLOps pipelines, and production monitoring. AI that runs reliably because the data underneath it already does.
We work across the leading data platforms and cloud ecosystems, so we meet you where your stack already is.
Published case studies from production data work. Different industries, one job: get the data governed, consistent, and ready, so analytics and AI can run on top of it.
A telecom provider unified Oracle, MySQL, and SQL Server into one Amazon Redshift warehouse on SnapLogic ETL pipelines. One view of customers, products, and assets, and a 40% productivity gain once teams stopped compiling reports by hand.
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A global manufacturer unified a fragmented, post-merger data estate into a governed, AI-ready Snowflake foundation, integrating M&A data five times faster.
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A global marketing leader replaced manual, error-prone API work with automated master data management on Semarchy. Custom Java plugins and a tuned matching strategy produced clean golden client records across a 71,000-employee enterprise.
Read the case study →Read the full library of IWConnect data and integration case studies.
Browse all case studies →From discovery to production, we scope, architect, and deliver.
A data platform is the full stack that ingests, stores, governs, models, and serves data: the lakehouse or warehouse, pipelines, governance, and a semantic layer. The warehouse is one part of that platform, the place structured data is stored and queried.
It depends on scope, but a first production-ready slice usually lands in weeks, not months, because we start from a current-state assessment and a platform-agnostic architecture before any build.
No. We design around the platforms you already run, from Databricks and Snowflake to Microsoft Fabric, Azure, and AWS, and add only what is missing.
AI-ready data is clean, governed, and well-modeled, with clear lineage and a semantic layer. We build that foundation first, then layer analytics, ML, and AI on top so models have trustworthy context.
Lineage, access control, cataloging, and quality checks are wired into the platform from the start, with quality SLAs. We can hand it over to your team or keep managing it, your choice.
It depends on your workloads, your existing cloud, and your team. We run a platform-agnostic architecture first, then recommend the stack that fits your data, budget, and people, not our preference.
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