Stragentech's approach is grounded in fraud prevention systems built at Amazon — processing millions of daily decisions with minimal human review — and in modernizing 40+ legacy systems at Boeing before any AI work could begin. The sequence is deliberate: derisk the platform, unclog the decision bottlenecks, then scale autonomous action. That's the expertise Stragentech brings to industrial operations for $5M–$150M SMBs.
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The Challenge
Equipment alerts, maintenance flags, and operational exceptions pile up faster than humans can process them — costing uptime and morale.
300+ daily alerts, 80% false positives. Teams have learned to ignore the noise — which means missing the signal that matters.
You find out about equipment failures when the equipment fails. Every unplanned outage costs multiples of what prevention would have.
The tools exist. The budget was approved. But without a leader who's built agentic systems, pilots never reach production.
Who We Work With
Best suited for companies where the core value proposition depends on operational reliability, transaction integrity, or connected systems at scale.
$25M–$150M manufacturers and managed services providers with connected equipment dealing with alert fatigue and first-generation IoT hitting its limits.
$10M–$50M ARR SaaS companies carrying legacy technical debt, losing engineering velocity to maintenance, and needing technology leadership to navigate AI transformation.
Private equity firms requiring pre-acquisition technical due diligence or post-acquisition technology leadership. The practice understands both the technology and the EBITDA lens — with direct experience inside PE-backed companies.
Results
Drawn from prior engagements at Amazon, Boeing, and Dedicated Computing — the same patterns applied to your scale.
Re-architected legacy operations systems onto AWS with ML-based agentic automation — autonomous investigation, decisioning, and corrective action at scale across millions of daily signals.
Owned and led delivery of AWS IoT Analytics — low-latency, highly available managed services continuously monitoring equipment and triggering automated responses.
Led architecture reviews and modernization strategy for 40+ legacy on-premise applications, plus agentic AI automation in complex aviation data processing workflows.
Defined IoT managed services strategy, built the software org from scratch, and drove $7M EMEA expansion for a PE-backed hardware company.
How It Works
A structured process designed to minimize your time investment while maximizing what gets learned and delivered quickly.
30 minutes to understand your context, primary pain points, and whether there's a fit worth exploring.
Operational Intelligence Assessment across 5 domains. Findings presented with a prioritized roadmap and quick wins.
Focused retainer executing the highest-value roadmap items with monthly progress reviews with leadership.
Ongoing fractional CTO partnership or transition to internal ownership — with documentation, training, and handoff support.
About Stragentech
Stragentech is a specialized fractional CTO practice building autonomous operational intelligence systems for industrial manufacturers, managed services providers, and B2B SaaS companies. The practice draws on 25+ years of production experience at Boeing, AWS IoT, and Dedicated Computing — applied to the scale and urgency of growing businesses.
Work spans two core domains: industrial IoT intelligence and AI-led modernization for operations-heavy businesses. Every engagement is structured around measurable outcomes — uptime gained, cost eliminated, and manual processes retired.
Insights
Practical frameworks for CEOs and operators navigating AI transformation without a full-time technology leader.

How SMB manufacturers deploy Agentic AI + Industrial IoT to eliminate unplanned downtime and automate remediation — without an enterprise budget. 10-slide framework with ROI benchmarks.

Legacy code isn't just a developer headache — it's slowing your operations, increasing your incident rate, and blocking every AI initiative you try to launch. Here's how to measure it.

A visual framework showing the operational gap between companies still running reactive manual processes and those deploying agentic AI — and what it costs to stay on the wrong side of that line.
Six structured questions — grounded in MIT Sloan research — that separate leaders who extract real enterprise value from AI from those running expensive experiments that go nowhere.
Lessons from taking 40+ legacy applications and a 30% compliance posture to 95% in 18 months.
Where AI adoption in aviation stands today, why traditional certification frameworks are struggling to keep pace with ML systems, and what engineering leaders can do to close the gap.
Get In Touch
Every engagement begins with a free 30-minute discovery call. No pitch, no pressure — just a conversation to see if there's a fit worth exploring.