AI you can verify.
Knowledge you can trust.
AI you can verify. Knowledge you can trust.
Trusted AI for enterprise knowledge, neurosymbolically validated,
peer-reviewed, sovereign by design.
We are accelerating the advent of General AI through cutting-edge neuro-symbolic research.
The Pain
OUR SOLUTION
Published
Research
Published Research
Open-sourced
Open Sourced
Independently Verifiable
Independently Verifiable
the Four Pillars
Built different. On purpose.
Trusted & Verifiable
Every answer traceable back to its source
Outputs are validated against semantic and type conditions before they leave the stack. No invented citations. No silent failures.
Deep Knowledge Retrieval
Find, synthesize, and ground answers in your own data
Vector retrieval combined with symbolic constraints and knowledge graph traversal. Built for production scale on enterprise document volumes.
Deep Knowledge Retrieval
Find, synthesize, and ground answers in your own data
Vector retrieval combined with symbolic constraints and knowledge graph traversal. Built for production scale on enterprise document volumes.
Neurosymbolic
by Design
Neurosymbolic by Design
LLM + knowledge graph + symbolic validation, no guessing layer.
The probabilistic strength of language models, controlled by symbolic logic. Reasoning you can inspect, not just observe.
Neurosymbolic by Design
LLM + knowledge graph + symbolic validation, no guessing layer.
The probabilistic strength of language models, controlled by symbolic logic. Reasoning you can inspect, not just observe.
Sovereign &
On-Premise
Your data never leaves your infrastructure
Deploy on your hardware, behind your firewall, under your control. Including the model weights. No vendor lock-in. No third-party API dependency.
Sovereign & On-Premise
Your data never leaves your infrastructure
Deploy on your hardware, behind your firewall, under your control. Including the model weights. No vendor lock-in. No third-party API dependency.
How it works
From question to verified answer
Step 1
Step 2
Step 3
Step 4
Retrieve
Symbolic and vector retrieval against your data — across documents, databases, and structured knowledge.
Reason
Language model synthesis under contract constraints — semantic and type requirements enforced at every step.
Validate
Symbolic verification of the output against your domain rules — before it reaches the user.
Trace
Full provenance: every claim mapped back to the source passage, the retrieval path, the validation result.
Retrieve
Symbolic and vector retrieval against your data — across documents, databases, and structured knowledge.
Reason
Language model synthesis under contract constraints — semantic and type requirements enforced at every step.
Validate
Symbolic verification of the output against your domain rules — before it reaches the user.
Trace
Full provenance: every claim mapped back to the source passage, the retrieval path, the validation result.
Use cases
Built for regulated enterprises
Legal & Notaries
Document binding, contract analysis, and verifiable case research
Legal & Notaries
Document binding, contract analysis, and verifiable case research
Finance & Controlling
Reporting, regulatory filings, and audit-grade document synthesis.
Finance & Controlling
Reporting, regulatory filings, and audit-grade document synthesis.
Public Sector
Administrative processes with sovereignty and traceability requirements.
Public Sector
Administrative processes with sovereignty and traceability requirements.
Digital Transformation
AI strategy and trustworthy knowledge infrastructure for the enterprise.
Digital Transformation
AI strategy and trustworthy knowledge infrastructure for the enterprise.
Consulting
Deep research and synthesis for advisory work — grounded in client data.
Consulting
Deep research and synthesis for advisory work — grounded in client data.
Substance
Engineered for environments
that can't afford to guess
No logos. No promises. Just architecture and research you can verify yourself.
ON-PREMISE
LLM
Symbolic
Validation
Knowledge
Graph
ON-PREMISE
LLM
Symbolic
Validation
Knowledge
Graph
No data leaves your infrastructure
Every answer source-traceable
EU AI Act Annex IV-aligned
Research
Our research is public. Read it yourself.
From foundational theory to working code, every layer of our stack is published or open-sourced.
Trustworthy Agent Design
A practical whitepaper on designing trustworthy LLM agents with contract-based controls that validate inputs, outputs, and semantic requirements before agents act.
HyDRA - Knowledge Graph Construction
A whitepaper on HyDRA, a hybrid-driven reasoning architecture that uses collaborative agents, competency questions, and verifiable contracts to automate reliable knowledge graph construction.
SymbolicAI Framework (Open Source)
A developer-focused whitepaper on SymbolicAI, the open-source neurosymbolic framework for composing LLMs with Python-native symbolic abstractions, semantic primitives, and contract validation.
Large Language Models Can Self-Improve at Web Agent Tasks
An exploration of how large language models can improve their performance on complex web-agent tasks where training data is scarce and environments require multi-step actions.
SymbolicAI: A Framework for Logic-Based Generative Systems
A modular framework for combining generative models with logic-based concept learning, solver integration, and controlled flow management.
Addressing Parameter Choice in Unsupervised Domain Adaptation
A study of parameter selection for unsupervised domain adaptation, where labeled source data must transfer to a target domain without labels.
Retrieval-Augmented Decision Transformer: External Memory for In-Context RL
A reinforcement-learning study on using retrieval and external memory to strengthen in-context adaptation from a small set of examples.
Trustworthy Agent Design
A practical whitepaper on designing trustworthy LLM agents with contract-based controls that validate inputs, outputs, and semantic requirements before agents act.
HyDRA - Knowledge Graph Construction
A whitepaper on HyDRA, a hybrid-driven reasoning architecture that uses collaborative agents, competency questions, and verifiable contracts to automate reliable knowledge graph construction.
SymbolicAI Framework (Open Source)
A developer-focused whitepaper on SymbolicAI, the open-source neurosymbolic framework for composing LLMs with Python-native symbolic abstractions, semantic primitives, and contract validation.
Large Language Models Can Self-Improve at Web Agent Tasks
An exploration of how large language models can improve their performance on complex web-agent tasks where training data is scarce and environments require multi-step actions.
SymbolicAI: A Framework for Logic-Based Generative Systems
A modular framework for combining generative models with logic-based concept learning, solver integration, and controlled flow management.
Addressing Parameter Choice in Unsupervised Domain Adaptation
A study of parameter selection for unsupervised domain adaptation, where labeled source data must transfer to a target domain without labels.
Retrieval-Augmented Decision Transformer: External Memory for In-Context RL
A reinforcement-learning study on using retrieval and external memory to strengthen in-context adaptation from a small set of examples.
Full publication list available on request.
Ready for AI you can defend?
Let's talk about your knowledge stack, what you're trying to build,what's blocking you, and whether our approach fits.
Ready for AI you can defend?
Let's talk about your knowledge stack, what you're trying to build,what's blocking you, and whether our approach fits.