See what others miss.
Detect weak signals, fraud, faults, and emerging risk across millions of connected events—before they become expensive.
Foundation intelligence for graphs & time
Intelligence for
connected
worlds
in motion.
Tenvia builds foundation models that understand relationships and time—so organisations can explain the present, forecast what comes next, and act with confidence.
01 Graph intelligence02 Temporal reasoning
Live relationship model
System online06 nodes · 05 edges
Emerging signal+18.4%
⌁Relationships reveal the signal.
Built on research recognised by
IEEEACMARCAAAI · IJCAI
67K+ research citations
What we make possible
From connected
data to
consequential
action.
Most AI sees rows. Tenvia understands relationships—between people, assets, events, and time.
↗
Reason across time.
Use foundation models that understand, explain, forecast, and complete complex time series across domains—not a separate model for every task.
Ground every answer.
Combine knowledge graphs with LLMs so every conclusion can be traced through valid evidence and reasoning paths.
Knowledge-grounded LLM reasoning
Reason with evidence.
Not just probability.
Tenvia combines knowledge graphs with language models to make complex reasoning grounded, traceable, and interpretable. The result is intelligence that can show not only what it concludes, but why.
01 · RoGICLR 2024
Reasoning on Graphs
A planning–retrieval–reasoning framework that grounds LLM answers in valid knowledge-graph paths for faithful, interpretable reasoning.
Explore the research ↗02 · GCRICML 2025
Graph-constrained Reasoning
Constrains LLM decoding with knowledge-graph structure so generated reasoning paths remain grounded and verifiable.
Explore the research ↗03 · Graph foundation modelNeurIPS 2025
GFM-RAG
A graph foundation model for retrieval-augmented generation that learns reusable knowledge-graph retrieval across domains.
Explore the research ↗A major research frontier
One foundation.
Many timelines.
Time series are everywhere—from markets and machines to climate and health. Our TimeOmni research moves beyond narrow forecasting tools toward foundation models that can understand, reason, and generate across temporal tasks.
Temporal reasoningUNDERSTAND → GENERATE
MODEL 01 Open model
TimeOmni-1
A generalized, unified model for time-series reasoning—trained once to work across diverse temporal tasks while preserving broad reasoning ability.
View on Hugging Face ↗MODEL 02 Multimodal
TimeOmni-VL
A unified multimodal model for time-series understanding and generation, spanning analysis, anomaly detection, forecasting, and imputation.
View on Hugging Face ↗Where connection matters
One intelligence layer.
High-consequence domains.
Energy & infrastructure
Forecast demand, detect faults, and protect complex asset networks.
Health & biosignals
Understand temporal patterns across monitoring and physiological data.
Finance & markets
Reason over changing signals, anomalies, and connected risk.
Supply & industry
Anticipate disruption, maintenance needs, and operational change.
Built from first principles
Research depth.
Real-world velocity.
Tenvia is inspired by globally recognised work in graph machine learning, time-series foundation models, anomaly detection, and trustworthy AI led by Professor Shirui Pan.
We translate frontier methods into reliable systems that fit the realities of enterprise data.
Meet the research foundation ↗Early access