Five services, one data lifecycle.
Every platform we build is AI-ready, every pipeline is a candidate for automation, and every metric has one trusted definition.
Talk to us about your data platformData Engineering & AI Enablement
We build the pipelines that move data from source systems to the people, models, and applications that depend on it: ingestion, transformation, orchestration, and delivery, with the quality and lineage controls that keep a platform trustworthy as it grows.
AI workloads add a second job. Models don’t read reports - they need data in a specific shape. We turn structured and unstructured data (tables, documents, logs, text) into embeddings, features, and AI-ready datasets that power GenAI, retrieval-augmented generation (RAG), and machine learning applications.
Most organizations don’t have an AI problem - they have a data readiness problem. We treat that groundwork as the deliverable, and we increasingly build it with agentic data engineering: treating every pipeline as a candidate for automation, so the platform keeps running without manual intervention.
Cloud Data & AI Architecture
We design cloud architectures around each organization’s business needs, not around a preferred vendor. That starts with selecting the right platforms and technologies for the analytics and AI workloads the business actually runs, and matching them to what is already in place.
Every architecture we deliver is secure, scalable, and cost-optimized, with governance built in rather than added later: how data lands, where it’s stored, how environments are separated, who can access what, and what each storage and compute choice costs as usage grows.
We work across the leading cloud platforms, principally AWS and Google Cloud. The goal is a platform that holds up under real usage - new teams onboarding, new data sources arriving, and new AI workloads landing on infrastructure designed to take them.
Data Lakes, Lakehouses & Data Warehouses
We build the storage layer everything else in a data platform sits on: modern data lakes, lakehouses, and data warehouses that unify structured and unstructured data in one governed environment.
That means choosing the right pattern for the workload, defining ingestion and layering (raw, cleaned, modeled), and designing the schemas and partitioning that keep queries fast as volume grows. Where they fit, we build on open table formats, so data stays portable across engines instead of locked into one.
Done well, a lake, a lakehouse, and a warehouse aren’t competing choices. They’re layers of the same platform, each doing the job it suits - and together ready for both analytics and AI workloads.
Big Data & Real-Time Analytics
Some decisions can wait for a nightly batch job. Others - fraud checks, operational alerts, live dashboards, in-product personalization - need data that reflects what just happened. We process high-volume, high-velocity data streams in real time, powering operational dashboards, real-time decisions, and time-sensitive business logic.
We design streaming ingestion and processing alongside the large-scale batch processing most organizations still run day to day. In practice the two aren’t separate disciplines - most platforms need both, feeding the same downstream models, dashboards, and applications.
Real-time processing is a real infrastructure cost. We size it to the actual volume and latency the use case requires, and scope it to where the business impact justifies it rather than defaulting to it everywhere.
BI, Semantic & Serving Layers and AI Products
The most common failure in BI is two dashboards quietly disagreeing with each other. We build governed semantic and serving layers that define every metric once and serve it consistently to dashboards, AI agents, and applications - so a number means the same thing wherever it shows up.
On top of that layer we build the products people use: dashboards and self-serve reporting, conversational analytics that let teams ask their data questions in natural language, AI-generated insights, and AI-powered data products.
Because AI agents read from the same governed definitions as dashboards, they answer with the same numbers the business already trusts - instead of inventing their own.