Capabilities
From ingestion to insight to intelligence
Data engineers, analytics engineers, scientists, and ML engineers under one delivery model.
Data platform engineering
Snowflake, Databricks, and cloud-native warehouse and lakehouse builds.
Pipelines & integration
Batch and streaming ingestion with dbt-modeled, tested transformations.
BI & analytics
Power BI, Tableau, and Looker semantic layers with governed metrics.
Machine learning
Forecasting, propensity, risk, and optimization models with MLOps pipelines.
Generative AI
RAG assistants, document intelligence, and evaluation harnesses with human review.
Data governance
Catalog, lineage, quality monitoring, PII classification, and access controls.
How we deliver
A delivery model your PMO can audit
Every engagement runs on the same transparent process, with named accountability from intake through steady state.
- 1
Use-case framing
Pick the decisions worth improving and define the measurable outcome up front.
- 2
Foundation
Ingest, model, and test the data products the use case depends on.
- 3
Build & evaluate
Ship dashboards, models, or AI assistants with quantitative evaluation gates.
- 4
Operate
Monitor quality, drift, and cost; retrain and iterate on a scheduled cadence.
Technology
Tools and platforms we staff and support
- Snowflake
- Databricks
- dbt
- Airflow
- Kafka
- Python
- SQL
- Power BI
- Tableau
- Azure OpenAI
- Amazon Bedrock
- LangChain
- MLflow
- Vector databases
Outcomes
What clients get
- One trusted source of truth instead of competing spreadsheets.
- AI pilots that graduate to production because evaluation was built in from day one.
- Documented lineage and access control that satisfies risk and compliance review.
- Analytics engineers who leave behind tested, readable models.
FAQ