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Latent-Sense Technologies
Built for organizations that need to
how AI-enabled decisions are made.
Track, Prove and Trust
Solve AI's biggest LLM challenges in your stack
Enterprises face significant challenges in AI adoption, including regulatory compliance, and explainability. Traditional AI solutions often lack transparency, making it difficult to understand decision-making processes and ensure compliance. Latent-Sense Technologies solution delivers audit-ready pipelines that replace black-box roulette, skip retraining, and swaps RAG for governed reasoning.
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.
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.
Black-box LLMs vs Glass-box AI
Enterprise Concerns | Black-box LLM | LST glass-box |
|---|---|---|
Audit trail | None or post-hoc | Step-by step lineage + replay |
Policy enforcement | Prompting best-effort | Compliance driven reasoning inside the pipeline |
Re-training for drift | Frequent | Rare. Policy updates and memory |
Data Privacy | Re-train / fine-tune | User directed context-based |
RAG-like quality | Context injection only | Planned, validated reasoning over retrieved facts |
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.
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Externalized, structured reasoning over any model
LST shifts logic from model weights to an auditable reasoning stack with policy, validation, and memory.
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.
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.
Reasoning Agent Ecosystem
An Ecosystem of essential and customizable AI agents for reasoning, auditability and compliance.
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.
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.
rxMaps - Persistent Knowledge Graphs
Exportable, persistent reasoning maps shared across agents, sessions, and teams with provenance history.
RelsD - Saliency & Relationship Extraction
Builds relationship graphs from messy documents/data to give you an end-to-end picture.
ReDiD - Intent-Based PII Detection & De-Identification
Embedded privacy: de-identify PII and domain-specific sensitive concepts.
AiTD - Synthetic Text Detection to Safeguard Data Integrity
Content integrity: flag AI-generated text before it hits your system.
Benchmarking Toolkit
Custom reasoning benchmarking protocols measure logical coherence, contradiction detection, causal chaining, and inter-agent performance. This enforces auditable AI reasoning without requiring training or infrastructure development.
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.
HITL gates
Pause, review, sign-off at critical steps.
Deploy anywhere
API, SDK, MCP, AWS Marketplace or Private VPC.
Reasoning Validator
Drop in front of OpenAI, Claude, Gemini etc. or internal models
Ship governed AI without ripping out your stack
No retraining cycles
Handle drift and sensitive data in policy and memory.
Faster regulatory responses
Instant replay and evidence packs for audits
Lower incident risk
Pre-release validation blocks unsafe outputs
Resources
Use Case Gallery
Turn LLMs into trustworthy reasoning systems. It reduces failure risks, slashes costs, and unlocks 2—5x ROI for enterprises handling high-stakes or document-intensive work. It can be deployed as a stand alone agent, as part of a swarm of agents, or in an ecosystem of agents.