Systems that work.
Workflows that last.
Data you can trust.
We design and build the analytical infrastructure, reproducible workflows, AI pipelines, and research computing systems that organizations need to do their work — and sustain it. From local deployments to HPC and cloud, matched to your needs and budget.
Good infrastructure is invisible when it works.
Most organizations don't need more data — they need better systems for collecting, managing, and using the data they already have. The difference between a one-time analysis and an ongoing capability is reproducible infrastructure.
Three Canyon builds technical systems that are designed for your team's actual capacity — not for consultants to maintain. Clean handoffs, clear documentation, and practical training are built into every engagement.
Our technical depth spans deep learning and AI systems, high-performance computing, cloud infrastructure, and big data environments. What sets us apart is computational thinking applied to your real constraints — we build what fits your needs, budget, and team, not what's most impressive on paper.
Scope
Understand your current systems, data landscape, team capacity, and what the infrastructure actually needs to do — before writing a line of code.
Architect
Design a system that fits your context — the right tools, data structures, and workflows for your scale, budget, and technical environment.
Build
Implement dashboards, pipelines, analytical systems, or research computing environments with clean, documented, reproducible code.
Hand off
Transfer ownership through documentation, training, and structured handoff — so your team can maintain, adapt, and extend the system independently.
From raw data to reliable systems.
Operational Dashboards
Design and build dashboards that surface the right information for the right audiences — program staff, leadership, funders, and boards — updated automatically and reliably.
Analytical Systems
Build end-to-end analytical infrastructure — from data ingestion and cleaning through analysis and visualization — designed for ongoing use, not one-time projects.
Reproducible Workflows
Implement scripted, version-controlled data workflows that eliminate manual steps, reduce errors, and make analyses auditable, shareable, and repeatable.
Research Computing
Support research teams with computing infrastructure, data management, statistical analysis environments, and the technical scaffolding that rigorous research requires.
Data Integration
Connect disparate data sources — program databases, survey platforms, administrative systems, and external APIs — into unified, analysis-ready datasets.
Implementation Support
Provide hands-on technical support during system rollout — troubleshooting, staff training, documentation, and the practical help that bridges design and daily use.
AI & ML Pipeline Development
Design and build end-to-end AI and machine learning pipelines — including deep learning systems, large language model deployments, and healthcare informatics applications — in Python and PyTorch.
High-Performance & Cloud Computing
Deploy and manage workloads across local infrastructure, national HPC resources (ACCESS, XSEDE), and cloud platforms including AWS — matched to your scale, timeline, and budget.
Built for your team, not ours.
Technical systems only create value if the people who need them can actually use and maintain them. We design with the end user in mind from the first conversation.
Reproducibility first
Every system we build is scripted, documented, and version-controlled. If a step can't be reproduced by someone else following the documentation, it isn't done.
Right-sized for your context
We don't build enterprise systems for small teams, or simple spreadsheets for organizations that need real infrastructure. Scale and complexity are matched to actual need.
Open and auditable
We favor open-source tools and transparent methods. You should be able to see exactly how your data is processed, and so should any auditor, funder, or successor.
Ownership by design
Documentation, training, and clean handoffs aren't afterthoughts — they're built into every engagement from the start, so your team truly owns what we build together.
Needs before tools
We start with your problem, not a preferred technology. The right solution might be a shell script, a deep learning model, or a spreadsheet — the answer comes from understanding your constraints first.
Tools selected for
fit, not familiarity.
We work across a wide range of tools and environments. Our recommendations are based on what fits your team, your data, and your budget — not on what we happen to know best.
Teams ready to build real infrastructure.
Research and evaluation teams that need reproducible, auditable analytical workflows
Nonprofits and agencies building their first operational dashboard or reporting system
Organizations with messy or siloed data that needs to be integrated and made usable
Grant-funded programs needing data infrastructure to meet reporting requirements
Teams inheriting systems with no documentation and needing to understand what they have
Organizations whose technical work has outgrown spreadsheets but hasn't reached enterprise scale
Research teams needing HPC access, AI pipeline development, or cloud-based big data infrastructure
Healthcare, public health, or clinical organizations building responsible AI systems for informatics or decision support
Have a technical challenge that needs solving?
We can help scope the problem, assess what infrastructure you actually need, and determine what kind of engagement would be most useful — starting with an honest conversation about where you are.
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