Introducing IndustrySync: The Easy Button for Enterprise AI Production
There’s a conversation happening in boardrooms and trading floors and research labs right now, and it goes something like this:
“We’ve invested in the GPUs. We’ve hired the talent. We’ve picked the models. Why aren’t we in production yet?”
The answer is almost never the model. It’s everything underneath it.
The Problem Nobody Budgets For
When organizations plan AI initiatives, they think about compute, talent, and data. What they consistently underestimate is the infrastructure layer that connects all three.
Building that layer from scratch takes 18 to 24 months. It requires 20 to 30 engineers. It costs $15 to $25 million before a single model runs in production. And while it’s being built, your most expensive and scarce people (quant engineers, computational biologists, AI researchers) spend 40 to 60 percent of their time on plumbing instead of the work you actually hired them to do.
That’s the AI infrastructure gap. And it’s costing organizations far more than they realize.
What It Costs in Financial Services
In financial services, stale infrastructure doesn’t just slow you down. It costs you money in ways that show up directly on the P&L.
Most risk teams still run overnight batch processes. By the time markets open, the numbers are already hours old. On a normal day, that’s an inconvenience. On a day like Liberation Day (April 3, 2025, when volatility swung dramatically intraday) it’s the difference between acting and watching.
Our backtest on a $10 billion equity portfolio showed that real-time risk calculation, running every five minutes instead of once overnight, produces roughly 1% better portfolio outcome per shock event. That’s profit captured when markets move in your favor, and losses avoided when they don’t. It’s also the difference between a clean regulatory audit and the kind of missed calculation that cost one major bank $500 million in penalties.
The infrastructure problem compounds on the cost side, too. Running these workloads in the cloud costs five times more than it should be. Teams spend millions per year in AWS on workloads that could run far more efficiently on-premises, and the engineers managing that infrastructure are the same people who should be building better models.
What It Costs in Life Sciences
In drug discovery, the math is different but the problem is the same.
The highest cost in drug discovery isn’t failure. It’s delay. Every month lost in the discovery pipeline is a month of revenue delayed, a month off your patent clock, a month your competitor might be using better.
Virtual screening runs that take days limit how many candidates your team can evaluate per quarter. GPU clusters sitting at 50 to 70 percent utilization mean you’re paying for compute that isn’t working. Computational scientists, some of the most expensive and specialized people in your organization, spend half their time configuring infrastructure instead of running experiments.
When the infrastructure works the way it should, the numbers change dramatically. Screening runs that took days complete in hours. Billions of candidates evaluated per run. GPU utilization climbs significantly from day one, and your scientists are back doing what you hired them for.
Introducing IndustrySync: The Easy Button
DDN IndustrySync Pipelines are pre-validated, production-ready AI workflows built on NVIDIA AI Data Platform reference design. There are two pipelines today, one for Financial Services and one for Life Sciences, and they share one philosophy: stop building infrastructure. Start delivering outcomes.
Every component is pre-integrated and tested. The data layer is ready on day one. The NVIDIA NIM stack is already configured. The pipelines are designed to work with your data, your models, and your existing workflows. You’re not adopting a rigid system and reshaping your organization around it. You’re plugging a validated architecture into what you already have. Production in days, not months.
That’s not a marketing claim. It’s a deployment model validated at production scale.
Even Easier: HyperPOD and Cloud
For organizations that want to go further, IndustrySync runs on DDN Enterprise AI HyperPOD, a rack-level, pre-integrated AI platform built with Supermicro, accelerated by NVIDIA, and powered by DDN software. HyperPOD removes the infrastructure layer entirely. You receive a validated, production-ready system, deploy IndustrySync on top, and you’re running real workloads before most organizations have finished their architecture review.
Already in the cloud? IndustrySync runs there too. You get something equally valuable: a validated, production-ready pipeline running on your existing cloud investment, without the months of build time. The pipeline adapts to where you are, not the other way around.
The Numbers, With the Assumptions Shown
We’ve been deliberate about how we present the business case for IndustrySync, because numbers without context are just noise.
For Financial Services, based on a 100-GPU cluster and a three-year horizon:
- Cloud infrastructure spend drops from $41 million to $8.7 million, an 80 percent reduction
- Engineering talent previously consumed by infrastructure (estimated at $3 million per year, based on 20 engineers at $300K fully loaded with 50 percent of their time on infrastructure) returns to productive work
- Real-time risk delivers approximately 1% better portfolio outcome per market shock event
For Life Sciences, on equivalent infrastructure:
- The same 80 percent cloud cost reduction applies
- 100x faster virtual screening means more candidates evaluated per program
- Months removed from discovery timelines, and industry estimates put the value of one month of earlier launch for a blockbuster program at $30 to $100 million
These are stated assumptions, not black-box projections. We show our work because we expect you to pressure-test it against your own environment.
The Question Worth Asking
If you could skip the 18 months of infrastructure build and go straight to production, what would your team do with that time?
Your quant engineers would build better models. Your computational biologists would run more experiments. Your risk officers would make better decisions with better information. Your discovery programs would move faster.
That’s what the AI infrastructure gap is actually costing you. Not just money, but time, talent, and competitive position.
IndustrySync closes the gap. The infrastructure is solved. The pipeline is validated. The easy button is ready.
Ready to deploy in your environment? DDN IndustrySync is available through a 90-day Early Adopter Program: full deployment, your data, your workloads, production-ready outcomes.
Visit IndustrySync or speak with your DDN representative.
All financial figures based on stated assumptions. DDN internal estimates, not independently audited. Industry estimates for life sciences time-to-market impact based on published pharma benchmarks.