Bring AI into your platform, without the failure modes that sink most first deployments.
We help Industry 4.0 SaaS companies move from “we’re thinking about AI” to “we’re shipping AI features that work”, with infrastructure, governance, and ROI instrumentation built in from day one.
Where Pre-AI ManTech companies stall.
Most ManTech SaaS companies starting their AI journey hit the same wall in the same place. The pattern is almost identical, engagement after engagement.
The pilot looks promising.
A computer-vision model identifies defects on clean training data with high accuracy. Leadership greenlights the work. A small AI team is hired or assembled.
The infrastructure question gets deferred.
The pilot was built fast, in the cloud, for batch evaluation. Production needs edge inference, real-time decisioning, and integration with PLCs and MES, none of which were in scope.
The use case wasn’t strategically prioritized.
The pilot was chosen because it was technically tractable, not because it was the highest-ROI use case for your customer base. The more valuable use cases stay un-built.
There’s no ROI instrumentation.
The feature ships, but nobody can quantify what it’s actually delivering to customers. Leadership can’t make the case for the next investment.
Momentum stalls.
The AI initiative loses internal sponsorship. The infrastructure investment isn’t paid back. The next pilot starts from zero.
We exist to break that cascade. →
How we work.
A structured 60–90 day engagement that sequences strategy first, then infrastructure, then deployment, with measurement and governance baked into every stage.
AI Opportunity Mapping
We start by mapping where AI actually pays off for your platform: every candidate use case scored on customer ROI, technical tractability, and strategic compounding. You get a ranked list, not a wishlist.
Every candidate use case, scored before a line of model code.
We map and prioritize opportunities against customer-facing ROI, technical tractability, and strategic compounding, specific to your platform. You walk away knowing exactly what to build first, and why.
The panel that proves what the feature is actually delivering.
Measurement systems and runbooks quantify the value to your customers and to leadership, so the case for the next investment makes itself. Evidenced, not asserted.
What you walk away with.
Concrete artifacts, infrastructure, and a shipped feature, not slides.
A first production deployment, shipped, not piloted.
What a Pre-AI engagement looks like when it lands: a scoped feature live in production, instrumented from day one.
A Manufacturing-Tech SaaS team had a promising vision pilot but no production path: cloud-only inference, no MES/PLC integration, and no way to prove ROI to its own customers.
We scored the highest-ROI use case, built the edge-inference and integration foundations, and shipped the first production feature, instrumented to measure customer outcomes from day one.
Engineering under real-world constraints, before it was an AI problem.
Cryenx’s engineering legacy is in performance-critical, real-time systems for brands where production reliability wasn’t optional: Disney, Coca-Cola, Apple, LEGO, Warner Bros, SXSW. Real-time interactive systems, AR/XR experiences, integration with complex existing infrastructure.
That experience (engineering under real-world constraints, integration with legacy systems, observability discipline) is exactly the discipline ManTech AI requires. Most AI teams don’t have it. We do.
