model.parthenocissus.ai/knowledge-graph
Knowledge Graph
entity linkage / second-order exposure
Parthenocissus / data infrastructure for complex information.
The connection layerbetween fragmenteddata and structuredintelligence.
Fragmented filings, feeds, documents, and events become one living graph. Not a chatbot, not a single model: the durable layer underneath decision intelligence.
Data InfrastructureEntity ResolutionKnowledge GraphsStatistical ModelsDecision Systems
340 links
model.parthenocissus.ai/linear-regression
Linear Regression
coordinate system / OLS signal fit
model.parthenocissus.ai/correlation-matrix
Correlation Matrix
PCA-ready revenue source covariance
1.00
0.62
0.31
0.48
0.71
0.62
1.00
0.44
0.22
0.58
0.31
0.44
1.00
0.19
0.35
0.48
0.22
0.19
1.00
0.40
0.71
0.58
0.35
0.40
1.00
PC157%
PC221%
PC311%
Built for information-dense industries
Financial IntelligenceInvestment ResearchEnterprise AnalyticsRisk ManagementScientific DiscoveryStrategic Decision Systems
See it in motion
What the platform looks like, in use.
A product walkthrough for the connection layer: search an entity, expand its graph, inspect statistical relationships, then receive a proactive signal.
app.parthenocissus.ai
Concept walkthroughsecond-order exposure to Vantage Semiconductor
Vantage Semiconductor · company
10-Q · Vantage · filing
Semiconductor Cycle Paper · research
Concept preview
Hover the graph.
A mockup of the financial-intelligence graph: companies, filings, events, research, and market signals resolved into one structure.
parthenocissus.ai · concept preview, illustrative data
MockupCompany
Filing
Event
Research
Market Signal
Trellis Platform
One system. Every layer of your data.
Trellis is the intelligence infrastructure layer for source health, semantic structure, model behavior, and decisions teams can trace.
Live workspace
Operational data intelligence
A unified command surface for source health, model behavior, and decision output.
3.8B rows processed
99.4% pipeline reliability
12 departments connected
Connected Sources
0
Active Pipelines
0
Knowledge Entities
0
Model Confidence
0%
Data Freshness
0m
Decisions Generated
0
Recent activity
CRM source synchronized
Revenue model retrained
Supplier risk anomaly detected
Customer segment updated
Forecast confidence increased
Architecture
From raw data to operational intelligence.
Every stage keeps lineage, confidence, and business meaning attached to the records it transforms.
Selected layer
Data Sources
Bring fragmented operational data into one governed intake layer.
Input
Databases, APIs, spreadsheets, documents, CRM, ERP, cloud storage
Output
Versioned source events and normalized raw records
Technology
Connectors, OCR, CDC, API orchestration, file parsers
Business value
Reduces manual collection and exposes hidden data dependencies.
Knowledge graph
Your company already has a knowledge graph. It is just disconnected.
Drag nodes, filter entity families, and expand relationships to see how operational context forms around the enterprise.
Model studio
Rotatable 3D mathematical models.
Drag each live model to inspect coordinates, transitions, surfaces, uncertainty, and residuals from different angles.
Live parameter controls
feature vectorsleast squaresresidual analysis
Drag to rotate / scroll to zoom
Capabilities
A connected system for data, models, and decisions.
Connect
Connect databases, APIs, files, applications, and external datasets.
Live lineage, confidence, and workflow context included.
Structure
Clean, standardize, map, and organize fragmented information.
Live lineage, confidence, and workflow context included.
Understand
Discover entities, relationships, patterns, and business context.
Live lineage, confidence, and workflow context included.
Model
Apply statistics, machine learning, forecasting, and optimization.
Live lineage, confidence, and workflow context included.
Explain
Use transparent metrics, causal analysis, and traceable model outputs.
Live lineage, confidence, and workflow context included.
Act
Deliver recommendations, alerts, dashboards, and automated workflows.
Live lineage, confidence, and workflow context included.
Use cases
Decision workflows for complex operating environments.
Illustrative examples show how the same intelligence layer adapts to different business systems.
Financial Services
From scattered signals to governed recommendations.
Problem
Risk, customer, and transaction data often live in separate systems with isolated controls.
Trellis workflow
Connect account and transaction data
Build customer-risk entities
Run anomaly and churn models
Generate reviewed actions
Model video
live simulationThe video shows state transitions, graph signals, and model confidence updating as new records enter the workflow.
Illustrative Example
42% faster analysis
31% fewer manual workflows
18% improvement in forecast accuracy
65% faster anomaly detection
Product demo
Revenue PCA analysis workspace.
Trellis projects every revenue stream into principal components, isolates volatility clusters, then turns the model output into an auditable retention action.
Revenue Retention WorkflowPCA model$17.0M analyzed ARR7 revenue sources5 volatility drivers
Revenue at risk
$1.84M
isolated by PCA cluster
Variance explained
75%
PC1 48% / PC2 27%
Protected ARR
$419K
projected after action
Review reduction
65%
fewer false positives
PCA coordinate model
Revenue sources by scale, usage change, and volatility.
Before PCA
312 review accounts
Broad churn rules flag too many accounts.
After PCA
1,282 targeted accounts
Volatility cluster separates real risk from noise.
Action policy
$419K protected ARR
Retention offers are constrained by governance rules.
Philosophy
Data should not live in separate systems.
Most organizations do not lack data. They lack connection, structure, and context.
Trellis is designed around a simple belief: data becomes valuable when relationships are visible, models are explainable, and insights can become actions.
Company vision
Building the intelligence layer for modern organizations.
1
Today
Connect fragmented data and automate analysis.
2
Next
Create adaptive systems that understand business context.
3
Future
Build organizational intelligence that continuously learns, explains, and improves decisions.
Build a system that grows with your data.
Start with one workflow, one dataset, or one decision problem.