Where we help
Data, Analytics & ML
We build one governed copy of your data, so the dashboards stop disagreeing.
One source of truth
Put Your Data to Work
Your data can be a real competitive advantage. We build the analytics and machine learning behind it, whether that means data lake architecture or advanced AI models, and help you get maximum value from the information you already hold.
Our Data & Analytics Services
- One governed platform on BigQuery
- Streaming pipelines for data that cannot wait overnight
- Quality checks that run on every load
- Dashboards with lineage you can trace to the source
- ML models taken past the notebook into production
- Monitoring that catches model drift before the results do
Why it matters
Most companies collect far more data than they use. It sits in separate systems, nobody fully trusts the numbers, and the report that would answer a question takes days to produce. The problem is rarely a shortage of data.
One place for your data
We bring your sources together into a platform on BigQuery, so a question has one answer instead of four that disagree.
Numbers people trust
We build in data quality checks and clear lineage, so when a dashboard reports a figure, you can trace exactly where it came from.
Models that ship
We take machine learning past the notebook and into production on Vertex AI and Databricks, with monitoring so you know when a model starts to drift instead of hearing about it from the results.
Analytics Solutions
Data Lake & Warehouse
Storage and processing platforms that scale, for both structured and unstructured data.
Real-time Analytics
Stream processing and live dashboards for insights as things happen.
Machine Learning
Custom ML models for prediction, classification, and recommendation systems.
Business Intelligence
Interactive dashboards and self-service analytics so people can decide from the data.
Our Data Approach
Good analytics rests on a foundation built in order, not all at once. We work through a set of stages that run from raw data to models people actually use.
Data Assessment
We review your current data and find the opportunities.
Architecture Design
We design a data platform that scales.
Implementation
We build and deploy the data pipelines and analytics solutions.
Optimization
We keep monitoring and tuning performance.
Every phase ends with an exit. If the work is not worth doing, we say so in writing and you walk away with the findings.
Reference architecture
Sources
- Apps & SaaS
- Databases
- Events
Ingestion
- Datastream
- Pub/Sub
Platform
- BigQuery
- Dataform
Serve
- Looker
- Vertex AI
Proof
Consumer Electronics
Product intelligence from a connected appliance fleet
A leading global consumer appliances company
A global appliance brand made a fleet generating 70,000 telemetry records a minute queryable in plain English, and put live pricing intelligence across 160 retailers and 1,150+ SKUs into production.
Consumer Electronics · IoT · Google Cloud
Read the study
Logistics
Illustrative exampleA governed logistics lakehouse on Databricks and Google Cloud
A logistics operator
Unified freight, telematics and warehouse data into a governed lakehouse on Databricks and Google Cloud, with Unity Catalog over Delta Lake and BigQuery federation so analysts query trusted data without moving it.
Logistics · Databricks · Google Cloud
Read the study
Frequently asked
Is this just dashboards, or does it include machine learning?
It covers the full range, from data pipelines and warehousing through business intelligence and into machine learning. Many clients start by getting reporting they trust and add predictive work later. We meet you wherever your data maturity is now.
Where do we begin?
We start by looking at what data you have, where it lives, and what decisions you are trying to support. That tells us whether the first job is plumbing, cleanup, or modeling. Chasing machine learning before the data is reliable is a common and expensive mistake, so we check the foundation first.
How do you keep sensitive data governed?
We set access controls, masking, and audit logging into the platform from the start. BigQuery and Databricks both support fine-grained and column-level access, and we align them to frameworks like GDPR or HIPAA where they apply. Governance is part of the build, not a later add-on.
Do we need a data lake, or can we use what we already have?
Often you can use what you have. If your data already sits in a warehouse, we may just need to connect and model it. A lake or lakehouse earns its place when you hold large volumes of raw or unstructured data. We design to your actual volume and needs rather than the biggest possible architecture.
Let us talk about data, analytics & ML.
A senior engineer replies, usually within one business day.