Sensing, reasoning, optimization, execution, verification, and adaptation in one shared state — from cloud to edge.
Programmable by AI agents, deterministic by design, auditable by default.
Today's stack splits every stage across separate tools and separate state. AI agents can orchestrate them, but every handoff is where context is lost and verification fails. Holo runs the full cycle in one shared state — so agents orchestrate and verify in a single call.
Holo Language composes the full decision cycle in one program — no glue code, no handoffs.
Learn more ↗Holo reduces the operational complexity around AI decisions: fewer moving parts, lower compute overhead, and outputs your systems can verify.
Replace fragmented search, rules, optimization, and verification layers.
Deterministic outputs with exact constraints and audit-ready results.
Run through the cloud API, on-premises, or close to edge infrastructure.
Compact CPU-first execution without mandatory GPU clusters.
Compose complete decision workflows using Holo Language.
Tested against scikit-learn, PySCF, BM25, TF-IDF, SymPy, PyMatching, SQLite, and ClickHouse. Every boundary is part of the product story.
Explore all benchmarks ↗Results are workload-specific. Full methodology, baselines, limitations, and negative results are documented in the API reference.
Holo can verify an LLM agent, operate as an enterprise decision engine, or run close to physical systems where cloud dependence is not an option.
Let LLMs handle language. Holo handles persistent state, strict logic, optimization, and verification before action.
Run complete decision workflows for allocation, routing, compliance, forecasting, and operational control.
Process signals and make deterministic local decisions near devices, sensors, robots, and industrial systems.
Holo is not a collection of models connected through orchestration. Search, reasoning, optimization, memory, and verification operate on one native geometric structure.
From financial infrastructure and AI agents to robots, industrial systems, and scientific computing.
Coordinate autonomous agents with verifiable, constraint-safe decisions.
Make high-stakes financial decisions explainable, constrained, and auditable.
Optimize, simulate, and verify complex physical and operational processes.
Execute cryptographic and transaction logic with verifiable correctness.
Run perception-to-action logic in one deterministic edge runtime.
Discover structured laws, model dynamic systems, and verify predictions.
Begin in the cloud, then move on-premises or closer to devices when security, latency, or operational requirements demand it.
Start in minutes. Integrate from any language or existing AI stack.
FASTEST WAY TO STARTGive AI agents direct access to deterministic Holo capabilities.
NATIVE AGENT ACCESSDeploy inside private infrastructure for regulated and sensitive workflows.
DATA STAYS WITH YOURun compact workloads closer to devices, sensors, and physical systems.
LOW-LATENCY EXECUTIONA focused entry point today, followed by a credible path toward the deterministic runtime for AI systems acting in the physical world.
High-value workflows where fragmentation, latency, constraints, and auditability create immediate pain.
Continuous systems that sense, decide, act, verify, and adapt with less manual orchestration.
Compact deterministic infrastructure close to robots, devices, sensors, and industrial systems.
Show us how your system senses, decides, and acts. We’ll show you where Holo can unify, verify, and run it.
The mathematical foundation: why Holo Engine's hyperbolic geometry is equivalent to an EBM, with proofs.
A practical workshop. Real CRM and churn data. Every endpoint verified — Learn, Reason, Discover, Optimize. +20pp over baseline.
Head-to-head benchmark results on classification tasks against gradient-boosted trees.
Can Holo Engine handle geometric verification, 3D construction, and elliptic curve arithmetic? 53 tests say yes.
Explore the hyperbolic geometry behind Holo Engine in real time. Drag, zoom, and watch the tree respond.