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Solve AI's challenges in your stack
Track
Persistent mappings and memory ensure consistent entities, relationships, and context across workflows.
Prove
Benchmarking, lineage, and replay produce evidence for audits and sign‑off.
Trust
Neuro‑symbolic agents, validators, and privacy controls enforce policy building explainable outputs and trustworthiness.
Reasoning Infrastructure
Latent-Sense Technologies provides AI reasoning infrastructure and a reasoning ecosystem, integrating orchestration, reasoning benchmarking, and semantic knowledge infrastructure into enterprise workflows.
rxMaps: Semantic Knowledge Infrastructure
rxMaps operates upstream to transform raw data into a deterministic, long-term semantic substrate. It establishes the cross-document framework and auditable logic required for true, defensible Cognitive AI.
ReX: Evidence-First Reasoning
Detects contradictions, builds causal chains, and enforces policy; transforms any LLM into a structured reasoner (multi-hop, neuro-symbolic inference). Outputs come with supporting evidence and an auditable trace.
rxOrchestrator - Agent Agnostic Coordinator
Routes decisions, actions, escalations, and human-in-the-loop; logs every step into a unified auditable evidence trail. Specialist agents (contract checkers, validators, compliance enforcers) coordinate under shared context and policy constraints.
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Reasoning Microservices
An ecosystem of essential and customizable AI agents for reasoning, auditability and compliance.
RelsD
Saliency & Relationship Extraction
Semantic relationships extraction and saliency analysis.
ReDiD
Intent-Based Detection & De-Identification
Embedded privacy: de-identify PII and domain-specific sensitive concepts.
AiTD
Synthetic Text Detection
Content integrity: flag AI-generated text before it hits your system.
Benchmarking Toolkit
Custom benchmarking protocols to measure resaoning across agents.
The Latent-Sense edge
Latent-Sense stands out by offering the only enterprise-ready, modular, agentic structured reasoning orchestration platform that combines: a neuro-symbolic multi-agent architecture, persistent reasoning mappings, built-in data privacy/synthetic text handling, reasoning benchmarks, and rapid cloud-native integration and deployment.
Neuro-Symbolic Reasoning
Integrates neuro-symbolic reasoning for persistent, explainable automation.
Glass-Box AI
First of its kind Glass-box AI, improves on state-of-the-art LLM systems by delivering
transparent, auditable reasoning and privacy-first automation.
Outperforms LLMs
Outperforms black-box LLMs on 30+ semantic competencies tests.
Externalized, structured reasoning over any model
Avoid retraining
Handle drift, new taxonomies, and remove sensitive data with policy updates and semantic knowledge graphs (rxMaps), not weight changes.
Retrieval-augmented reasoning
Orchestrated steps turn retrieved facts into policy‑checked conclusions.
Glass-box default
Every step is recorded, replayable, explainable; add HITL where it matters.
Moving from black-box to
explainable, cost effective, regulator-ready reasoning
Opaque outputs
No defensible decisions under audit.
Retraining drag
Cost, delay, and governance risk.
RAG gaps
Retrieval ≠ reasoning; context can still hallucinate.
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How it works
LST turns any AI model into a transparent, policy-compliant reasoning engine powered by modular agents, persistent memory, and built-in privacy, with zero retraining required.
1
Connect
Plug-in your LLM and data sources.
2
Compose
Build policy-aware pipelines with reasoning agents and memory.
3
Prove
Replay lineage, capture HITL sign-off, and ship governed outputs.
Enterprise Controls
LST bypasses infrastructure bottlenecks and lengthy sales cycles with rapid, API, SDK, AWS Marketplace, and MCP-driven deployments making LST a leader in the "buy-over-build" market. The platform is designed for quick enterprise onboarding with plug-and-play control over pipelines.