Data, SQL & Analytics
Your data, structured to be trusted and put to work, so your software runs fast and your decisions run on real numbers.
Data is either an asset or a liability, and the difference is almost entirely in how it's structured. Badly designed data makes software slow, reporting unreliable, and every question harder to answer than it should be. Well-designed data makes the whole system fast, trustworthy, and ready to build on. We do the unglamorous, high-leverage work of getting your data right.
That starts with deep SQL and database design expertise, the foundation underneath most software. We design data models, write efficient queries, build pipelines that move data reliably, and create the analytics layer that turns all of it into something you can actually act on. When the goal is AI, we build the data foundation that makes it possible, because AI is only as good as the data underneath it.
Your data is yours, structured, documented, and running on infrastructure you own. What we build makes it a durable asset rather than a growing liability.
What's Included
Database Design
Data models and schemas designed for speed, integrity, and growth, the foundation that determines whether your software is fast or slow.
SQL & Query Optimization
Deep SQL expertise to write efficient queries and fix the slow ones, often the difference between an app that lags and one that flies.
Data Pipelines
Reliable pipelines that move, clean, and transform data between systems, so the right data is in the right place, current and correct.
Analytics & Reporting
Turning raw data into dashboards and reports your team can actually use to make decisions, based on numbers you can trust.
Data Integrity & Governance
Making sure your data is accurate, consistent, and trustworthy, because decisions and AI built on bad data are worse than none.
AI-Ready Data
Structuring and preparing your data so it can power AI, from clean pipelines to vector databases for retrieval.
How We Approach It
01
Understand the data and the questions.
We start with what data you have and what you need to do with it, run the software, answer questions, power AI, because the purpose shapes the design.
02
Design the model right.
We design data models and schemas for integrity and performance from the start. Getting this right early prevents most of the slowness and mess that shows up later.
03
Clean and structure what exists.
Real data is messy. We clean, validate, and structure existing data so it's trustworthy, because everything built on top inherits its quality.
04
Build reliable pipelines.
Where data needs to move and transform, we build pipelines that do it dependably, with validation and error handling so bad data doesn't flow downstream.
05
Make it usable.
We build the query, analytics, or reporting layer that turns structured data into answers people can actually use, or the foundation that lets AI use it.
06
Optimize and maintain.
As data grows, we keep queries fast and the system healthy, so performance holds up instead of degrading over time.
Where This Applies
A sense of what this looks like in practice. If your data challenge isn't listed, it's very likely something we can help with.
A database designed properly from the start for a new product
Slow queries and databases diagnosed and made fast
Data pipelines that move and clean data between systems
Dashboards and reporting your team can trust and act on
Messy, inconsistent data cleaned and structured
A single source of truth built from data scattered across systems
Data prepared and structured to power AI and retrieval
Vector databases set up for AI search and RAG
Tech We Use
SQL, PostgreSQL, MySQL, SQL Server, MongoDB, Redis, Elasticsearch, Prisma, and Python for data work, plus pgvector and vector databases like Pinecone and Qdrant when the goal is AI-ready data.
Related Work
FAQ
Is your data working for you?
Tell us about your data and what you need from it. We'll help you turn it into an asset you can trust and build on.