Data Engineering
We build batch and streaming pipelines that move data reliably from source systems into governed storage, with quality checks and monitoring built into each stage.
Data & Artificial Intelligence
From raw data to decisions leaders can defend
We build the pipelines, platforms, and reporting layers that make organizational data usable — then apply analytics and AI where they produce measurable value.
Delivery Flow
How data becomes a mission or business decision
Each stage is engineered, documented, and monitored. We do not skip governance to accelerate reporting.
- 01
Data Sources
Applications, sensors, legacy systems, files, and third-party feeds.
- 02
Integration
Ingestion via Glue, Kinesis, MSK/Kafka, and API-based collection.
- 03
Engineering
Transformation, validation, and modeling with Spark, Python, and SQL.
- 04
Storage
Governed lakes and warehouses on S3, RDS, Aurora, and PostgreSQL.
- 05
Analytics
Metrics layers and dashboards in Power BI and Tableau.
- 06
AI
Targeted models, evaluation, and decision-support prototypes.
- 07
Mission Decisions
Reporting leaders and operators can act on with confidence.
Practice Areas
What we deliver across the data lifecycle
Data Platforms
We design lake and warehouse architectures on AWS, defining storage layers, partitioning, access control, and retention so platforms remain manageable as volume grows.
Data Analytics
We model data for analysis with SQL and Spark, establishing consistent definitions so different teams reach the same numbers from the same source.
Business Intelligence
We build Power BI and Tableau reporting around defined metrics and refresh schedules, replacing manual spreadsheet cycles with maintained dashboards.
Data Science
We support exploratory analysis and model prototyping with Python, focusing on data readiness, evaluation, and whether a model is the right answer at all.
Artificial Intelligence
We scope AI work as pilots with clear success criteria — document processing, decision support, and workflow assistance — reviewed for accuracy and responsible use.
Our Approach
Data readiness before model ambition
Most analytics and AI programs stall because of pipeline, quality, and access problems, not algorithms. We address the foundation first and then extend into advanced use cases.
- Define the decision the data must support before designing the pipeline.
- Instrument pipelines so failures are detected before reports go stale.
- Apply least-privilege access and encryption to data at rest and in transit.
- Document models, metrics, and lineage so results can be independently verified.
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