Full-stack data engineering and applied AI for small businesses and research teams, from the database at the bottom to the agents on top.
Most AI fails in production for a boring reason.
An agent is a reasoning layer pointed at your systems, and it inherits every flaw beneath it. Point one at a schema that lies and it repeats the lie with confidence; point one at three databases that disagree and it averages them. That gap between a demo that dazzles and a deployment that stalls is almost never the model. It is the foundation the model was standing on. So we build the four layers below in order, base to apex, and pour each one only when the one under it will hold.
This is where correctness is won or lost, because everything above reads from here. A schema modeled to how the work actually happens, instead of to a SaaS template's guess at it, is the difference between a query that answers the question and one that answers a slightly different question nobody asked. We build the CRMs, the accounting systems, and the operational tools so the source of truth is true.
Scattered inputs become one clean, queryable store. That means ingestion that survives the source changing shape, transformation you can trace when a number looks wrong, and storage that holds up under load, on Postgres, Kubernetes, Terraform, and AWS. It is the layer nobody sees until it breaks, so it is the layer we spend the most care making unbreakable.
Here the clean foundation starts paying for itself. A Model Context Protocol server lets a model read your database and call your tools through one typed interface, so you retire the brittle pile of glue scripts that usually sits in this gap. The model reaches your systems the same way a careful engineer would, and you can see every call it makes.
Only now does the agent make sense, resting on three layers that already hold. We run inference and bespoke agents on hardware you own, so your data never leaves the building and the system does not go dark when someone else's API changes its terms overnight. The apex is the smallest piece of the build and the part everyone asks for first, which is precisely why it comes last.
The same four layers have gone in for independent businesses across Dallas and beyond, in whatever order the existing systems allow, though the dependency between them never changes. Some start at the database and climb; some already have the data and want the agent that was never possible without the plumbing underneath.
A CRM that three teams fill in three different ways. Numbers that don't reconcile between the spreadsheet and the dashboard. An AI pilot that dazzled in the demo and quietly stalled in production. Under each of those is the same missing layer, and it wants plumbing underneath, built once and built right, before it wants another tool on top.
It starts with your real data, not a discovery deck: what's in the spreadsheets, what the CRM half-captures, where two systems each insist on a different number. That audit sets the schema, and the schema gets drawn to the work as it actually happens.
From there the build climbs the stack in order, base to apex, and never skips a rung you don't yet have the foundation for. Code ships as it's written, so you see the system take shape instead of waiting for one delivery at the end. The person who does the work, and why it stays that way, is on the about page.
Tell us what the data is, where it lives, and what you wish it could do.