Constraint Solver
Scheduling, resource allocation, portfolio optimization—solved declaratively. Define constraints like AllDifferent, Sum, or custom rules. Get optimal solutions without writing algorithms.
Neuro-Symbolic AI
The reasoning layer
your AI is missing
LLMs generate. ReasoningLayer proves. Explore how neuro-symbolic AI delivers the reliability enterprises demand.
Cost Reduction
Slash LLM costs dramatically
Most AI queries don't need an LLM. Route deterministic reasoning to the symbolic engine. Reserve LLMs for what they're good at: natural language.
Smart Context Engineering
Domain-aware prompt optimization
Stop stuffing prompts blindly. Your domain knowledge knows what's relevant. Automatically inject the right context—entity relationships, business rules, constraints—based on the user's intent. No more token waste, no more missed context.
- Intent-driven context selection
- Automatic entity & relationship injection
- Token-efficient prompt construction
- Dynamic context based on conversation state
Smart Tool Selection
Ontology-driven tool selection
Stop guessing which tool to call. Your ontology—types, relations, constraints, hierarchies—knows exactly which tools are relevant for each task. Not embeddings, not keyword matching: real structural understanding. Works with MCP, OpenAI function calling, or any tool protocol.
- Full ontology matching: types, relations, constraints
- Works with MCP, function calling, any protocol
- Auto-generate tool schemas from your domain model
- Precise routing = fewer wasted API calls
LLM-Free Reasoning
Zero tokens for pure logic
Most queries don't need an LLM at all. Constraint solving, rule evaluation, type checking, consistency validation—all run locally with pure symbolic inference. Only use LLM tokens when you actually need language understanding.
- Pure symbolic inference = zero API costs
- Runs offline on any device
- Deterministic results, infinitely cacheable
- LLM only for ingestion & natural language
Recommendation Engine
Based on YOUR data
Build powerful recommendation systems that understand your domain semantics. Unlike black-box ML models, every recommendation is explainable and traceable back to your ontology and business rules.
- Semantic matching beyond simple vectors
- Explainable recommendations with proof traces
- Real-time personalization with constraint satisfaction
- Combine user preferences with business rules
Reliability
Deterministic, not probabilistic
Same input always produces the same output. Constraint solver guarantees optimal solutions. Temporal reasoning handles causality. Zero hallucinations by design.
Knowledge-Grounded Agents
Agents that reason, not hallucinate
LLM agents are powerful but prone to hallucination. Ground your agents in a formal knowledge base—they can only assert facts that exist in your ontology, with full provenance and audit trails.
- Agents constrained to verified knowledge
- Every claim traceable to source data
- Automatic hallucination detection
- Graceful handling of knowledge gaps
Flexible Fuzzy Reasoning
Logical flexibility, zero black box
Real-world data is messy and incomplete. Our engine reasons gracefully with partial information, degrees of certainty, and fuzzy logic—delivering flexible decisions while keeping every step fully transparent and explainable.
- Reason over incomplete or uncertain data
- Fuzzy logic with explicit confidence scores
- Transparent decision paths—no black box
- Graceful degradation when information is missing
Type-Safe Inference
Catch errors at design time
Our rich type system with multiple inheritance catches entire classes of errors before they reach production. Type constraints are enforced at inference time—invalid states are literally impossible. No more runtime surprises from malformed data.
- Static type checking on knowledge base
- Multiple inheritance type hierarchies
- Compile-time constraint validation
- Invalid states are unrepresentable
Multi-Agent Orchestration
Driven by your ontology
Coordinate multiple AI agents using your domain ontology as the orchestration layer. Define agent roles, capabilities, and interaction protocols declaratively. The engine handles routing, conflict resolution, and consensus.
- Declarative agent roles and capabilities
- Ontology-based task routing and delegation
- Automatic conflict resolution between agents
- Shared knowledge base with access control
Trust & Compliance
Cryptographic proof of every decision
Merkle-signed validity certificates for regulators. Not just audit logs—mathematical proof that decisions followed your rules. EU AI Act Article 14 ready.
Explainable & Auditable RAG
Beyond vector similarity
Traditional RAG is a black box—you get chunks but no explanation of why. ReasoningLayer adds semantic justification to every retrieval, tracing back to ontology relationships and business rules.
- Semantic relevance scoring, not just cosine similarity
- Proof traces explaining why each chunk was selected
- Ontology-aware chunking and retrieval
- Audit-ready logs for regulatory compliance
Policy-Driven Guardrails
Enterprise rules, enforced by logic
Define your enterprise policies as formal constraints—what can be said, what data can be accessed, what actions are allowed. Your knowledge base enforces them at inference time. Not heuristics. Not filters. Provable policy compliance.
- Formal policy specification language
- Real-time constraint validation
- Role-based access control on knowledge
- Audit trail for every policy decision
Temporal Knowledge & Decaying Memory
Facts have expiration dates
Knowledge changes over time. "The CEO of X is Y" expires when leadership changes. ReasoningLayer handles temporal validity, version history, and automatic fact decay—so your AI never reasons on stale data.
- Time-bound facts with validity intervals
- Automatic expiration and decay policies
- Historical queries: "What was true on date X?"
- Conflict resolution when facts change
Hidden Insights & Weak Signals
Discover what others miss
Surface patterns and correlations that traditional analytics miss. Fuzzy logic and temporal reasoning help you detect early warning signals and emerging trends before they become obvious.
- Detect weak signals across disparate data sources
- Temporal pattern recognition for emerging trends
- Fuzzy matching for approximate relationships
- Cross-domain inference for unexpected connections
Why ReasoningLayer
Beyond traditional
neuro-symbolic systems
Most symbolic AI systems are rigid, hard to scale, and lack enterprise-grade security. ReasoningLayer changes everything.
Cryptographic Proofs
Every inference produces a Merkle-signed validity certificate. Mathematical proof of correctness for regulators. Not just logs—cryptographic guarantees that decisions are traceable and tamper-proof.
Domain-Ready Ontologies
Pre-built integrations with BioPortal (medical), MITRE ATT&CK (security), NVD (vulnerabilities), and regulatory frameworks. Start reasoning over industry-standard knowledge immediately.
Effect Prediction
Discover causal effects from time-series data. Bayesian merging of evidence across sources. Predict outcomes with confidence intervals and uncertainty quantification.
Temporal Reasoning
Native support for time-aware inference. Reason about events, durations, sequences, and causality. Track how facts evolve and expire over time.
Fuzzy Logic Engine
Non-rigid reasoning that handles uncertainty, partial matches, and degrees of truth. Real-world data is messy—your reasoning engine should handle that gracefully.
Residuation Engine
Automatic suspension when data is incomplete. Reasoning resumes seamlessly as new information arrives. No manual orchestration needed.
Enterprise Authorization
Google Zanzibar-style authorization built-in. Multi-tenant isolation, hierarchical RBAC, relationship-based access control. Production-ready for regulated industries.
Neural-Symbolic Bridge
Best of both worlds: LLMs handle natural language and ambiguity, symbolic engine ensures correctness and explainability. Each does what it does best.
Ready to stop hallucinating?
Join teams building the next generation of AI agents that reason.