Five services, one data lifecycle.
From architecture to pipelines to the dashboards people actually open — we cover the full lifecycle, so the layers stay consistent with each other instead of becoming five disconnected vendors' work.
Talk to us about your data platformData Engineering & AI Enablement
We design and build the pipelines that move data from source systems into the models, tools, and applications that depend on it. That means ingestion, transformation, orchestration, and the quality and lineage controls that keep a data platform trustworthy as it grows.
Most organizations don’t have an AI problem — they have a data readiness problem. Before a model or a Gen AI application can produce something reliable, the data feeding it has to be clean, current, and accessible in the right shape. We treat that groundwork as the actual deliverable: well-engineered pipelines, sound data models, and the automation that keeps them running without manual intervention.
We work across the major cloud data-engineering toolchains on AWS and Google Cloud, and we build with the specific constraint in mind that AI enablement adds: data has to be structured and governed well enough for a model to use it directly, not just for a person to read a report from it.
Cloud Data Architecture
We design the architecture underneath a data platform: how data lands, where it’s stored, how environments are separated, who can access what, and how the platform grows without a redesign every time a new use case shows up.
That includes landing zones and ingestion layers, storage and compute tiering, access and governance controls, and the cost and performance trade-offs that come with each choice. We work primarily across AWS and Google Cloud, matching the architecture to the platforms and constraints already in place rather than starting from a blank slate.
The goal is a platform that holds up under real usage: new teams onboarding, new data sources arriving, and new AI and analytics workloads landing on infrastructure that was designed to take them.
Data Lakes & Data Warehouses
We build the storage layer that everything else in a data platform sits on: data lakes for raw and semi-structured data at scale, and data warehouses for the modeled, query-ready layer that analytics and reporting run against.
That means choosing and structuring storage tiers, defining ingestion and layering patterns (raw, cleaned, modeled), and designing the schemas and partitioning strategy that keep queries fast as volume grows. We build these on AWS and Google Cloud’s native lake and warehouse services, matched to the data volume, access patterns, and cost constraints of the organization.
Done well, a lake and a warehouse aren’t competing choices — they’re complementary layers of the same platform, each doing the job it’s suited for.
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, not what happened yesterday. This service covers the pipelines and infrastructure that make that possible.
We design streaming ingestion and processing (event pipelines, stream processing frameworks, message queues) alongside the large-scale batch processing that handles the volume most organizations still run day to day. The two aren’t separate disciplines in practice — most platforms need both, feeding the same downstream models, dashboards, and applications.
We build this on AWS and Google Cloud’s streaming and big-data services, sized to the actual volume and latency requirements of the use case — real-time processing is a real infrastructure cost, and we scope it to where the business impact justifies it rather than defaulting to it everywhere.
Business Intelligence & Reporting
A data platform only pays off once someone can actually use it to decide something. This service is the last mile: turning modeled, trusted data into dashboards and reports that people open, understand, and act on.
That covers the semantic layer (the business logic that turns raw tables into meaningful metrics), dashboard design, and self-serve reporting so teams aren’t waiting on a data team for every new question. We design for the metric to mean the same thing everywhere it’s used — the most common failure mode in BI is two dashboards quietly disagreeing with each other.
We build on top of the same cloud data platforms we architect and populate, so the reporting layer stays consistent with the data underneath it instead of becoming its own disconnected system.