Enterprise data workflows rarely fail because of ambition. They fail because execution paths harden over time, manual steps compound, systems queue instead of parallelize, and decisions wait on infrastructure rather than insight.
This was the case for a large global financial services organization tasked with large-scale content tagging across decades of unstructured data.
The problem wasn’t scale.
It was how the work was executed.
The Challenge: A Three-Week Tagging Bottleneck
The organization relied on automated content tagging to support investment research and back-testing. The scope was massive:
20 years of historical data
SEC filings, news articles, and press releases
30,000+ tags
150,000+ phrases and linguistic variants
Tags used to drive back-testing and investment strategy analysis
Despite automation, the workflow was slow and fragile.
Baseline process:
Tagging ran continuously for 7 days
Required 130 Databricks servers
Followed by another week of back-testing
Then human intervention to refine outputs
Total time: ~3 weeks end-to-end
The system proved the concept, but it was too slow for production use. By the time insights surfaced, market opportunities had already passed.
Why Software-Only Execution Broke Down
Although tagging was automated, execution remained constrained by traditional software and CPU-bound infrastructure:
Sequential job execution
Limited parallelization across tags
Heavy compute loads from NLP, NER, and linguistic processing
Infrastructure overhead from scale-out CPU clusters
Long-running jobs that couldn’t adapt dynamically to workload intensity
In short: the workflow scaled linearly while the problem scaled exponentially.
The Shift: Hardware-Accelerated, Parallel Execution
The solution was not replacing inputs, models, or outputs, but rethinking where and how the work executed. The organization implemented a hardware-accelerated tagging architecture, designed to:
Run thousands of tags concurrently
Parallelize historical data processing
Reduce total compute hours without sacrificing accuracy
Maintain human-in-the-loop refinement where it mattered
The result was a fundamental change in execution, not logic.
The Results: Same Inputs, Radically Faster Outcomes
Before:
~3,120 compute hours
~3 weeks end-to-end runtime
High infrastructure cost
Limited iteration velocity
After:
~48 total compute hours
End-to-end runtime reduced from weeks to days
~65% faster time to results
~90% reduction in compute cost
Fully parallelized tagging and processing
Crucially, the tagging logic and outputs remained consistent. What changed was execution efficiency.
Business Impact: Insight Velocity Became a Competitive Advantage
By collapsing the tagging timeline, the organization unlocked new capabilities:
Faster back-testing cycles
More frequent feature regression analysis
Ability to test more hypotheses in tighter windows
Improved responsiveness to market signals
Lower infrastructure cost per analytical run
What was once a gating factor became an enabling layer.
Why This Matters for Enterprise AI Workflows
This case highlights a broader pattern in enterprise AI adoption: Most AI bottlenecks are not model problems. They are execution problems.
As enterprises apply AI to larger, more complex datasets, software-only pipelines increasingly fail to keep up. Hardware acceleration, paired with intelligent orchestration and selective human oversight, becomes the difference between experimentation and production.
The takeaway is simple:
Same data
Same logic
Same outputs
Radically different outcomes when execution friction is removed
The Bottom Line
This organization didn’t replace its data stack.
It removed the friction inside it.
And in doing so, transformed a three-week bottleneck into a workflow measured in hours, not weeks.
See it in motion.
Watch how Storm outperforms a traditional LLM, side by side, when speed, scale, and parallel execution actually matter.
