Help With Your Data First. Then AI, Where It Fits.
We help growing businesses organize, connect, and trust their data, and then find sensible ways to use it. Most of the work is practical and unglamorous. That's usually where the value is.
Data Architecture
Modern cloud data platforms designed around how the business actually works.
- Lakehouse and warehouse design on Azure or AWS
- Databricks and Snowflake platforms sized for where you are now
- Clear layers from raw data to trusted business tables
Data Integration
Connect information from systems such as POS, ERP, CRM, e-commerce platforms, APIs, databases, and files.
- Reliable pipelines that run on a schedule, not by hand
- Bringing spreadsheets and exports into one place
- Monitoring so you know when something breaks
Data Governance
Improve data quality, ownership, metadata, lineage, and trust.
- Agreed definitions for the numbers that matter
- Data quality checks and clear ownership
- Access rules that match who should see what
Analytics
Create a more consistent foundation for reporting and business insights.
- One source for the metrics every team uses
- Reports that refresh themselves
- Dashboards people actually open
AI Readiness
Prepare data and processes for practical AI use cases.
- Finding the use cases that are worth trying
- Getting documents and data into a usable shape
- Deciding what needs to be true before AI will help
AI Applications & Agents
Explore assistants, agents, and other AI applications built around useful business information.
- Assistants that answer questions from your own documents
- Asking questions of your data in plain English
- Small agents that take repetitive steps off someone's plate
From scattered data to numbers you can trust
A common pattern we use, in plain language. Raw data lands first, then gets cleaned, then becomes the trusted business view that reports and AI can rely on.
Technology is important, but it comes after understanding the problem.
These are the tools we know well. We pick from them based on what you already use and what the problem actually needs.
Cloud
- Azure
- AWS
Data
- Databricks
- Snowflake
- Delta Lake
- SQL
- Python
- PySpark
Integration
- APIs
- ETL / ELT
- Data pipelines
- Orchestration
AI
- LLMs
- RAG
- AI agents
- Natural-language analytics
Application
- Python
- React
- Next.js
Growing businesses, in most industries
Data problems look similar across industries: scattered systems, manual reports, numbers that don't match. Our focus areas are retail, e-commerce, professional services, and real estate, but we're happy to talk with any growing business.
Want to talk through a data or AI problem?
Bring it as it is. We'll help you work out a sensible place to start.