Experience — Built by People Who Have Operated Real Systems at Scale

$6.5M+

Documented client spend

94+

Years of combined experience

10+

Industries at enterprise scale

Across all engagements, we provide full hands-on implementation—engineering, DevOps, cloud infrastructure, and production deployment. Not advisory-only roles. Every engagement carries direct accountability for delivery and outcomes.

Systems we've built and operated handle billions of API calls per day, support millions of customers, and move billions of dollars in annual commerce. We've been in rooms during major outages at Fortune 500 companies, calmly diagnosing and fixing production crises.

Selected Engagements

Proven delivery across industries

Capital One (via Acxiom)

$2.4M engagement

Cloud migration of credit-offer processing system, AI decision pipelines, large-scale data pipelines.

~$3M annual hosting cost reduction; ~400M credit files processed twice per month

Grip Places

$1.2M engagement — CTO & Co-Founder

Distributed AI / computer-vision IoT platform.

Deployed to 1,000+ edge locations; successful exit to Huvr, Inc.

Spectrum TV / Charter Communications

$765K engagement

Re-architected streaming metadata APIs; AWS migration; improved observability.

>1B API calls/day; error rates reduced ~3 points for 50M customers

State of Colorado — GCP

$605K engagement

Statewide GCP foundation and standards; multi-agency enablement.

99%+ security attack surface reduction; 90% → 99.9% uptime

LifeStance Health

$417K engagement

AI-driven call-center automation and routing solution.

$278K annual cost reduction

Laborjack

$410K engagement — Interim CTO

Full platform rebuild, cloud architecture, DevOps, team build-out.

ARR ~$100K → ~$1M in under 13 months; admin overhead reduced ~95%

Barrow BrainBook, Arizona

$131K engagement

Statewide athlete safety and concussion training platform.

Deployed to all middle and high school athletes; 50% reduction in concussion occurrence and severity statewide

TechStyle Fashion Group, Chrysler Motors

$575K combined

Fulfillment platform supporting $330M/year shipments; global vehicle diagnostics platform.

Release cycles reduced from weeks to minutes; millions of connected vehicles

Expertise

Capabilities proven in production

Large-Scale Systems

Billions of API calls, millions of users, global distribution patterns.

AI/ML Architecture

Decision pipelines, computer vision, NLP systems, model deployment.

Cloud & DevOps

AWS, GCP, Azure migrations; zero-downtime deployments; 99.9%+ uptime.

Security & Compliance

Regulated environments, HIPAA-aware design, audit-by-default architecture.

How We Work

The NOVABlue client experience

What to expect when you work with us.

How we manage risk

Alignment before architecture

Before we write a line of code, we interview your team extensively. How does the process work today? What problems come up in weekly meetings? If the only mistakes are typos, you're ready for automation. If you're constantly discovering new variations, we'll tell you that.

Technical risk evaluation

We evaluate security, performance, complexity, volume, and regulatory requirements. Complexity should be localized, not distributed. If your AI models are complex, that complexity shouldn't bleed into your login system.

How we communicate

Your contact points

Even on multi-million dollar projects, you'll typically interact with three people: a project manager for timelines, a technical account manager, and a designer if needed. Our technical account managers are deeply technical people who can also communicate with non-technical stakeholders. When you ask a technical question, you'll get an intelligent answer on the spot.

How much of your time we need

We think about it like a well-structured TV show: some arcs resolve in an episode, some span a season. Executives check in at high-level milestones. Team members engage intensely for specific components, then move on. Nobody's whole life gets consumed by the project.

How we ensure quality

Quality starts with architecture. Our team applies engineering principles most of which aren't written down anywhere—the kind where a senior engineer looks at a data structure and immediately spots an invalid state that should have been an enumeration. We add automated code quality scans, security scans, complexity limits, and automated tests. These practices compound over time.

Our culture around problems

We operate with a blameless culture. Nobody worries about being blamed for mistakes, so problems surface quickly and get solved fast. We've been in rooms where millions of customers were without service, working alongside Fortune 500 companies to diagnose major outages. Panic makes everything worse. We stay calm, stay creative, and systematically eliminate dead ends until we find the issue.

Our UX-first approach

Most technology companies start with their existing technology stack and try to fit your solution into that. We start with the user experience you want and engineer backwards. We're not locked into any particular technology—we use the right tool for the job.

But we go further: we start with your user experience of working with us. That's where UX truly starts, and it radiates outward to everything we build together.

The trade-offs we ask you to accept

We won't lie to get your business

Fast, cheap, good—pick two. We will not pretend these trade-offs don't exist. Other firms might tell you what you want to hear. We'll tell you what's true.

We won't compete on offshore pricing

We won't match offshore rates. But we will destroy offshore competitors on timelines, quality, and deliverability. They will not hit their deadlines. We will.

We will compete with US agencies

Against US firms, we typically cost 25–50% less—and we'll beat them on quality and timelines. We've worked alongside the big names on Fortune 500 projects. We've seen their top people. The brand name doesn't guarantee the quality of the work.

Why It Matters

Why experience matters in AI

AI demos are easy. Production systems are hard.

NOVABlue's value is knowing where systems fail outside of demos. We've seen the outages, the security incidents, the user adoption failures, and the cost overruns. That experience is baked into how we architect, how we communicate, and how we set expectations.

Built by people who have operated real systems at scale