
Content Intelligence
INGEST
Documents
Media files
Records
Logs
Storm
Content Intelligence
OUTPUT
Structured fields
Entity tags
Classifications
Signals
Before
Mixed formats, no structure
contract_draft_v3_final.pdf
mixed
DOC
meeting_notes_Q4.docx
text
LOG
server_2024-01-15.log
untagged
MP4
training_video_001.mp4
media
CSV
client_data_export.csv
tabular
After
Normalized fields and tags
entity: company
type: contract
date: 2024-01
status: active
score: 0.94
Consistent schema • Automation-ready
High-volume tagging
Process millions of assets quickly
without sequential model calls.
Consistent structure
Apply the same schema across
datasets so downstream systems
behave predictably.
Entity and signal
extraction
Identify entities, topics, metadata,
and semantic signals in a
repeatable way.
Automation-ready
outputs
Deliver clean, structured data that
agents, workflows, and analytics
can use immediately.
LLM-based tagging
Sequential calls
Variable outputs
Latency increases with volume
Higher cost at scale
Storm
Parallel execution
Deterministic structure
Predictable performance
Lower overhead at high volume
FINANCIAL SERVICES
65%
Global financial services firm reduced a three-week content tagging bottleneck to hours by parallelizing execution across millions of documents.
View Case Study
Data and analytics teams
Prepare content for retrieval, reporting, and analysis without manual cleanup.
AI and ML teams
Feed models and agents structured inputs that improve reliability.
Enterprise operations
Automate classification and compliance workflows at scale.
What changes when content is structured
Storm
Content Intelligence
CortexOne
Execution Layer
Workflows
Agents
Marketplace
Storm is not a standalone tool. It is part of a unified execution platform.

Storm
