Transform your organization—phase by phase.
Data engineering, GIS, AI, and fractional technical leadership for governments, cities, schools, and founders. Not a strategy deck, not a pilot that stalls: a working system, built on governed data and handed to a team that can run it.
Problems we solve
Every engagement starts with a problem someone has to solve. These are the five we are brought most often.
“Our data is everywhere, and nobody trusts the numbers.”
State & local government
The problemRecords sit across legacy systems, departments, and spreadsheets, so every report starts with a week of manual reconciliation.
How we solve itOne governed data platform on AWS GovCloud or Azure Government, with dashboards leadership can defend in a budget hearing.
The full approach for state & local government“Which streets, pipes, and assets need us first?”
Cities
The problemRepair budgets follow complaints and age tables rather than risk, so crews fix what is loudest, not what fails next.
How we solve itGIS asset models that combine inspection history, sensor data, and 311 requests into a ranked, mappable work plan.
The full approach for cities“We need senior technical leadership, not a full-time CTO yet.”
Startup founders
The problemArchitecture, security, and first-hire decisions made in year one are the most expensive ones to undo in year three.
How we solve itA fractional CTO who sets the architecture, runs the roadmap, and hires the team that eventually replaces them.
The full approach for startup founders“How do we use AI without putting student data at risk?”
Educational institutions
The problemFaculty and staff are already using AI tools, often with no policy, no data agreement, and no way to tell what is working.
How we solve itAn AI use policy, FERPA-aware data governance, and small measured pilots in advising, admissions, and operations.
The full approach for educational institutions“Is this model good enough to put in front of the public?”
AI evaluation
The problemVendors demo well. Failures show up after procurement, in the cases the demo never covered.
How we solve itIndependent evaluation against test sets built from your own cases (accuracy, bias, and failure modes) and a clear go or no-go before you sign.
The full approach for ai evaluation
Something else on your desk? See every solution.
A solution is only as good as the data under it. Here is what sits under ours.
From raw records to a decision you can defend
Four layers, built in order on one foundation: data governance. Each layer is useful on its own, and governance is what makes the next one trustworthy.
Data engineering
Permits, work orders, meter and SCADA telemetry, public records, reconciled into one governed platform on AWS GovCloud or Azure Government, and kept current as new data lands.
GIS & spatial analysis
Every record located: assets, parcels, service areas, and the network between them, so an answer can be read on a map instead of a spreadsheet.
AI & machine learning
Forecasting, detection, and language models fitted to your data, each shipping with the evidence behind its output and a person at the decision.
Dashboards & maps
Views built for the moment a recommendation is questioned: what drove it, how confident it is, and which records it came from.
Data governance, under every layer
The reason an answer can be defended. It is designed in the first phase, not audited in the last, and it is what stays useful long after a particular model is retired.
- Ownership & lineageEvery dataset has a named owner, a quality check, and a traceable path back to its source system.
- Access & classificationRole-based access, sensitive fields classified and masked, and location data handled as the personal data it often is.
- Model documentationEach model ships with its training data, evaluation results, known limits, and the person accountable for it.
- Audit trailWho saw what, which data drove a recommendation, and what was decided, kept for records requests and auditors.
- FISMA & FedRAMP
- FERPA
- State privacy law
- NIST AI RMF
- Section 508
Getting it right in a notebook is the easy part. Getting it running inside an agency is the work.
In phases, with a gate at each one
Nothing moves to the next phase on optimism. Each gate is agreed before the phase begins, so scaling rests on results.
Assess
Data and infrastructure inventory, stakeholder interviews, and the question worth answering first.
Gate: Success metrics agreedDesign
Architecture across data, GIS, and AI, with the security and compliance perimeter drawn up front.
Gate: Perimeter signed offPilot
A limited rollout measured against thresholds set before it started, not after.
Gate: Evidence threshold metDeploy
Phased rollout with change management and training alongside your staff.
Gate: Your team running itOptimize
Monitoring and tuning as data, load, and policy change.
Gate: Ongoing
Where it runs
- Your cloudAWS GovCloud, Azure Government, or Google Cloud, inside your tenancy and under your keys.
- On premisesFor data that cannot leave the building, on infrastructure you already run.
- At the edgeModels on drones, vehicles, or fixed cameras where latency or bandwidth rules out the cloud.
What stays when we leave
Handover is part of the plan from the first phase, not a last-week scramble: documentation, runbooks, and training alongside your staff, so the system keeps running after we step back.
Read the full delivery methodTell us which decision you need to get right first.
Start with one problem: which assets fail next, whether a model is fit to use, or who your first engineering hire should be. We will tell you plainly whether it is worth a pilot.
Platforms we build on
