Do we need clean data before you can start?

No. Assessing and cleaning the data is usually the first phase of the engagement, not a prerequisite for starting it.

What's the difference between data engineering and AI engineering here?

Data engineering builds the pipelines and infrastructure that make data usable. AI engineering builds on top of that: models, RAG systems, and automation that use the data once it's reliable.

Data & AI Engineering

Data you can trust. AI that reaches production.

Six disciplines from pipelines to LLM applications: built with the governance, monitoring, and handover of enterprise systems, because that's what they are.

Book a discovery session or browse the six disciplines

01 · Data

A platform you can build on.

Pipelines, warehousing, and analytics with quality and governance built in from day one.

Data Engineering & Pipelines

ETL/ELT development, real-time streaming with Kafka and Spark, orchestration with Airflow.

Data Modernization & Warehousing

Cloud warehouse design on Snowflake and Databricks, data lakes, and legacy migration.

BI Dashboards & Reporting

Power BI and Tableau dashboards, self-service analytics, and embedded reporting.

02 · Applied AI

AI that survives contact with production.

LLM systems built to the same bar as the rest of your stack: measured, governed, maintained.

LLM Application Development

Azure OpenAI and Claude integration, prompt engineering, and responsible AI implementation.

RAG Systems & Vector Search

Vector database design, semantic search, and knowledge-base integration.

AI Workflow & Document Automation

Intelligent document processing, extraction, classification, and process orchestration.

Our approach

Value early, built for the long term.

Discover & assess

Your data landscape, the high-value opportunities, and success metrics that matter.

Design & architect

Scalable solutions using modern data patterns that fit your organization.

Build & iterate

Working solutions shipped early, refined on real feedback and usage.

Deploy & operate

Monitoring, documentation, and knowledge transfer: your team runs it.

When to work with us

If any of these sound familiar, we should talk.

And if none do, we'll tell you that too. Start with a 45-minute discovery session with the principal architect.

You're consolidating siloed data sources into a platform with self-service analytics

You're putting LLMs to work on knowledge tasks and customer experience

You need real-time dashboards that give leadership visibility into the business

You're migrating legacy data infrastructure to cloud-native for scale and cost

You want a RAG system so teams can query internal knowledge in plain language

You're automating document workflows to eliminate manual data entry

Common questions

Answers before you ask.

Bring us the data problem, not the AI wishlist.

A discovery session with the principal architect: your data landscape, the highest-value opportunity, and whether AI belongs in the answer at all. Not sure yet? The free 90-minute architecture review, four slots a month, is the place to start.

Book a discovery session Learn about the architecture review