Latent Data
Introducing Alpha Lake — the geometric intelligence layer
Alpha Lake, from Latent Data, sits over the relationship networks inside enterprise data, AI workflows, and agentic systems — revealing structure, behavior, and risk that flat tools cannot see.
// synthetic SBM / Barabási–Albert graph at production scale
Enterprise data is no longer just rows, dashboards, and reports. It includes transactions, documents, model outputs, embeddings, logs, agent traces, workflows, and decisions — all connected, all changing.
Traditional tools store and query that data, but miss the deeper structure that determines what it actually means.
Language models read text. They do not measure structure. They are made for linear, sequential data.
Latent Data uses geometric deep learning — the math of relationships, manifolds, and flow — to analyze data the way it is actually organized, not the way it reads.
Latent Data sits above your existing stack and builds a living, geometric digital twin of how entities, behaviors, and workflows actually relate — then keeps it fresh as new data arrives.
Build a geometric digital twin of the relationships, behaviors, and workflows inside your data.
Track drift, anomalies, agent behavior, and structural change as the system evolves.
Surface emerging risks, opportunities, and likely future states before they reach the dashboard.
Send signals into dashboards, alerts, APIs, copilots, and decision systems.
Six surfaces where the structural layer changes the work. Each one is a place flat tools have always struggled.
Reveal coordinated fraud and money-laundering structure across accounts, devices, and transactions — the patterns that single-row scoring can't see.
Map address relationships, transaction trajectories, and entity behavior across Bitcoin and Ethereum. Validated on the public Elliptic Bitcoin fraud graph.
Track agent behavior, tool usage, workflow drift, repeated failures, and token-cost patterns — as a graph of decisions, not a wall of logs.
Detect when models, data streams, users, agents, or whole subsystems move into unusual regions of behavior — before metrics break.
Connect users, devices, accounts, and sessions into a stable identity graph; surface synthetic identities and coordinated abuse.
Convert complex data behavior into risk scores, opportunity signals, recommendations, and next-best actions — with structural context attached.
Latent Data is built on geometric deep learning — the mathematics of relationships, manifolds, graphs, and flow. Where language models read text and tabular tools score rows, geometric methods analyze data based on its structure: how things connect, how behavior moves through a system, and how that structure changes over time.
Finds the shape of complex, high-dimensional data and the low-dimensional structure that actually governs its behavior.
Models relationships between entities — users, documents, transactions, agents, decisions — as first-class objects.
Monitors how data, models, and workflows evolve, and where they begin to drift away from expected regimes.
Creates compact, AI-ready data layers that downstream models, agents, and dashboards can reuse.
Combines all of the above into a living model of your data system — one that can be queried, monitored, and refreshed as reality changes.
Reported at >95% confidence from internal benchmark runs. Headline scale numbers are on synthetic networks at production scale; real-world validation uses the public OGBn-products graph and the Elliptic Bitcoin fraud graph.
Latent Data is built on more than a decade of published research in geometric deep learning, manifold learning, and graph signal processing — the same foundation behind sister companies Latent Alpha and Latent Bio.
The methods Latent Data builds on — manifold learning, graph signal processing, diffusion geometry, optimal transport on graphs — come out of more than a decade of peer-reviewed work. A selection of the foundational papers is below.
PHATE — a foundational method for revealing the underlying shape of high-dimensional datasets, from cellular populations to relational systems.
Read paperA multiscale extension that exposes structure across resolutions simultaneously — foundational for the digital-twin approach behind the platform.
Read paper// foundational research from the Krishnaswamy Lab at Yale · same lineage as Latent Alpha and Latent Bio.
For demos, partnership, press or careers — reach out directly. We respond to qualified outreach within one business week.
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