Reason With Proof,
Not Probability
Neurosymbolic AI pairs LLMs with your business rules, run over all your data where it lives. The model interprets; the rules decide, so every answer is consistent, traceable to its rules and rows, and proven in production in banking and pharma.
Proven in production
LLMs Guess.
Rules Know.
In regulated, high-stakes work, a plausible answer that can't be traced is a liability. Neurosymbolic AI keeps the fluency and adds proof.
The Model Talks.
The Logic Decides.
Prometheux encodes your domain as an executable ontology: entities, relationships and rules. The LLM understands the question and writes the explanation. The reasoning engine derives the answer from your data.
Explainable by Construction
Reasoning provenance shows which rules fired on which records, so users and regulators can ask why and get a real answer.
Domain-Aware LLMs
Feed the model reasoning results, not raw tables. The LLM explains conclusions your rules have already proven.
Grounded Agents
Connect agents to your business logic via APIs or MCP, so every step of an agentic workflow runs on deterministic logic.
Better Training Data
Use rule-based reasoning to build task-specific, verified training corpora for fine-tuning vertical models.
Deploy AI in regulated industries with confidence. Give answers you can defend to auditors and boards. Turn proprietary rules into a competitive AI advantage.
Reasoning That
Scales With Your Data
Vadalog, the reasoning language behind Prometheux, came out of research with Oxford, TU Wien and Banca d'Italia. Vadalog Parallel runs it across clusters and GPUs, directly on the sources you already have.
Recursion Is Native
Supply chains, dependency networks and multi-hop paths are a few readable rules, not hundreds of lines of PySpark.
Distributed and GPU-Accelerated
AstraZeneca's biomedical reasoning ran 9× faster on NVIDIA GPUs with RAPIDS and Vadalog Parallel.
No Graph Database, No Migration
Reason over warehouses, lakes and databases where they are. No OWL/RDF stack, no copies to keep in sync.
Peer-Reviewed Foundations
Built on published work in VLDB, IJCAI and EDBT on logical reasoning and neurosymbolic AI.
Stop choosing between AI that scales and AI you can trust. Run expert-grade reasoning over all your data, where it lives.
From Question to
Proven Answer
Connect Your Sources
Point Prometheux at your warehouses, lakes and databases. Data stays where it is.
Encode the Domain
Define entities and rules once: readable, versioned and reusable.
Ask in Plain Language
An LLM or agent turns the question into a call to your rules via API or MCP.
Answer With Provenance
The engine derives the result; the LLM explains it, citing the rules and rows used.
Proven Where Mistakes
Are Expensive
Credit Risk & Compliance
Banca Sella explains every credit decision with rules over the client's full history, cutting a regulatory flow from 5 hours to under a minute.
Read the case study →Drug Repurposing
AstraZeneca reasons over a biomedical knowledge graph of compounds, genes and diseases, accelerated 9× on NVIDIA GPUs.
NVIDIA blog →Grounded Agentic Workflows
Give agents rules to call instead of tables to guess at. They return answers your business rules have already proven.
Power AI →Built on Peer-Reviewed Work
Vadalog and our neurosymbolic research come from collaborations with Oxford, TU Wien, Roma Tre, Politecnico di Milano and Banca d'Italia.
Browse the papers →“Prometheux technology powers highly scalable and explainable reasoning over some of the world's largest knowledge graphs.”
Accelerating Neurosymbolic AI with RAPIDS and Vadalog Parallel
“Prometheux enabled us to build a system that can access our databases, process all the client's historical variables, and provide an intelligent, understandable explanation autonomously.”
Massimiliano MalinHead of Credit Risk · Banca Sella
“Generative AI without ontologies is an engine without a road. Prometheux builds the road.”
Marco RomeiDeputy CIO · Banca Sella
Questions,
Answered
What is neurosymbolic AI?
It combines neural models (LLMs) with symbolic reasoning (logic rules over structured knowledge). The neural side handles language and ambiguity; the symbolic side guarantees that answers follow your rules and can be traced back to data.
How is this different from RAG?
RAG retrieves text for an LLM to summarise, so the model still decides the answer. With Prometheux the answer is derived by the reasoning engine from your ontology, and the LLM explains a result that is already proven, with full provenance.
Is it production-ready or still research?
Both. Vadalog comes from peer-reviewed research with Oxford, TU Wien and Banca d'Italia, and Prometheux runs in production: Banca Sella cut a regulatory credit flow from 5 hours to under a minute, and AstraZeneca's biomedical reasoning ran 9× faster on NVIDIA GPUs.
How is Prometheux different from other neurosymbolic platforms?
Many symbolic systems reason over a curated knowledge base, one case at a time. Prometheux reasons over your full data where it already lives, across sources, with native recursion, distributed and GPU-accelerated execution, and provenance on every result. It runs as SaaS, in your VPC or on-premises.
Do I need to move my data or run a graph database?
No. Prometheux reasons over your warehouses, lakes and databases in place. There is no graph database and no OWL/RDF stack to maintain.
How do agents use it?
Agents and applications connect to your business logic through APIs or MCP, so each step of an agentic workflow runs on deterministic, auditable logic.
Related Capabilities
Ground Your AI in
Your Own Logic
Bring a use case and your schema. We'll show you neurosymbolic reasoning over your own sources.
