.png){kind=logo}
Latent-Sense Technologies
AI Reasoning Infrastructure & Ecosystem
Latent-Sense Technologies provides AI reasoning infrastructure and a reasoning ecosystem, integrating orchestration, reasoning benchmarking, and semantic knowledge infrastructure into enterprise workflows.
​
Our reasoning infrastructure converts unstructured enterprise knowledge (e.g. documents, policies, reports, contracts, research etc.) into reasoning-ready knowledge substrate that AI systems can query, trace, and audit enabling machines to operate over structured meaning rather than raw text retrieval or statistical embeddings.
rxMaps
Upstream Semantic Infrastructure
A semantic knowledge infrastructure is the foundation that prepares, structures, and connects meaning across all unstructured and structured enterprise data; enabling AI systems to reason with consistent, and traceable understanding.
​
rxMaps semantic infrastructure provide:
-
a normalized semantic space,
-
with explicit semantic relationships,
-
constraint-aware knowledge, and provenance‑preserved structure.​
ReX
Evidence-first Reasoning
Detect contradictions, build causal chains, enforce policies, and transform any LLM into a structured reasoner with auditable traces.
-
Cognitive Auditor & Claim Verification to validate or critique outputs across agents.
-
Guards against hallucinations and surfaces assumptions for auditability and trust.
​
​
​
rxOrchestrator
Glass-box Coordination
Coordinates multi‑agent reasoning with regulator‑ready logs of requests, branches, tool calls, and outputs.
-
Deterministic & reproducible: same input produces the same validated output.
-
Lifecycle governance (create, execute, delete) prevents shadow IT and silent failures.
​
​
​
rxMaps
Upstream Semantic Knowledge Infrastructure
Beyond standard storage and compute, rxMaps provides the essential semantic normalization layer for advanced AI. Unlike probabilistic RAG or vector search, 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.
Cloud Agnostic
Deployed on AWS
.png)
rxMaps within the AI Stack
rxMaps by Latent-Sense Technologies is an "upstream" semantic infrastructure that transforms raw data into a structured, reasoning-ready fabric to enable true Cognitive AI anywhere within an AI stack.
Raw Data
Raw corpora,documents, unstructured and semi-structured data sources.
Can reside in S3 | Azure Blog Storage | Data Lakes| Enterprise Stores | Source Systems
LST Reasoning Infrastructure - rxMaps
Reasoning Fabric Formation
rxMaps as the semantic fabric substrate enables:
​
-
semantic fabric assembly and mapping
-
semantic persistence
-
relationship integrity preservation
-
reasoning-ready structure for downstream model use
​
Reasoning Representation Engineering
rxMaps as enterprise structured semantic memory provides:
​​
-
semantic operations for querying, exploring, extracting
-
semantic structure and relationships storage
-
semantic structure artefacts
-
knowledge artefacts
Data Engineering
The data engineering layer primarily prepares, transforms, and organizes data so that downstream systems (ML models, analytics dashboards, and AI agents) can operate on it.
​
This is where enterprises transform raw organizational data into structured assets that can be used for analytics, machine learning, and AI applications.​​
​
​
rxMaps become engineered semantic artifacts residing in GraphDBs, RAGs and Datalake artifacts with:
-
Context gain: document meaning is retained during Extract-Transform-Load (ETL)
-
Unified Knowledge: Data is unified across datasets through semantic structure
-
Causal understanding: Systems detect relationships and semantic structures
-
Reasoning layer: Infers implications across records.
Can integrate with:
Databricks
Snowflake
Palantir Foundry
Microsoft Fabric
and more.
rxMaps become semantic artifacts
Model Infrastructure (Pre-Training)
Pre-training, representation learning, model optimization, training orchestration etc.
rxMaps enable:
-
training-data semantic preparation
-
tripletization with intact latent semantics
-
reasoning-ready representation before training
Model Infrastructure (Inference Layer)
Runtime Inference
Runtime inference infrastructure serving:
​
-
context execution,
-
memory access,
-
tool use,
-
generation-time orchestration
Reasoning Harness
rxMaps and ReX function as a reasoning harness for post-training / inference-time cognition and control, through:
​
-
Evidence-first decision validation
-
Agent reasoning verification
-
Semantic structuring for reasoning traces
-
Traceability across prompts, evidence, memory and decisions
Can integrate with LLMs from:
Open AI
Anthropic
LLaMA
Gemini
Cohere
and more.
rxMaps act as an enabler
rxMaps with ReX act as a reasoning harness.
Application Layer
Enterprise AI products, copilots, AI SaaS, agentic applications
Example applications: Microsoft Copilot | Salesforce Einstein | ServiceNow AI | Sierra.
rxMaps with ReX provide structured evidence-first reasoning
ReX
Evidence-first reasoning
ReX provides structured reasoning that enhances AI application layers and multi-agent systems. ReX delivers risk mitigation, operational efficiency, cost savings, and strategic differentiation across enterprise and regulated environments.
Neuro-symbolic
Evidence-first
Auditable trace
Enhance LLM Reasoning
-
LLMs Foundational Capabilities Elevated (Contradiction Resolution, Causal Tracing)
-
Reasoning for auditability and trust in high-stakes domains
-
Knowledge scaffolding
Enhance Agent Swarms
-
Middleware Reasoning Layer providing logical consistency, structured reasoning, and introspective capabilities
-
Cognitive Auditor Agent to validate or critique outputs of other agents.
-
Feedback Loop in RAG Systems to flag contradictions between retrieved knowledge and LLM generations.
-
Claim Verification & Justification Agent
Mitigate Risks
-
Reduces hallucinations and factual inconsistencies.
-
Ensures internal and cross-agent logical coherence.
-
Enhances transparency for audit and compliance.
-
Surfaces assumptions and implicit logic in outputs.
-
Enables safer deployment in clinical, legal, and financial contexts.
ReX saves time and costs
up to 85%
Document review time
up to 80%
Expert validation cycles
up to 50%
Token usage
Use cases in high-stakes domains
Domain | ReX + LLM | Improvement |
|---|---|---|
Aviation | Incident analysis, procedural logic checking | Robust causal tracing |
Legal AI | Contract review, clause alignment, argument validation | Better contradiction and entailment |
Healthcare | Medical inference, treatment justification | Safer, traceable diagnostics |
Finance | Risk analysis, compliance logic | Transparent regulatory reasoning |
rxOrchestrator
Glass-Box Reasoning.
Deterministic Outputs.
Audit-Ready by Design.
rxOrchestrator streamlines and enhances AI workflows across complex organizational ecosystems.
Glass-box AI
Deterministic outputs
Lifecycle governance
Ingest & Structure
Persist & Share
Bring your own agent
Deterministic, Audit‑Ready Orchestration for Enterprise AI
The rxOrchestrator coordinates multi‑agent reasoning, logs every step (requests, branches, tool calls, outputs), enforces lifecycle governance, and delivers deterministic, reproducible workflows with regulator‑ready audit trails.
Enterprise Integration
API‑first, modality‑agnostic; plug‑and‑play with AWS, SDKs, MCP. No re‑engineering.
Auditability & Compliance
Logs requests, branches, tool calls, and outputs making regulator‑ready audit trails.
Native rxMaps Integration
Persistent, collaborative, auditable reasoning maps across agents.
Lifecycle & Governance
Explicit control (create, execute, delete) prevents shadow IT and risks.
Reasoning Structure
Structured pipelines with rxMaps as backbone for continuity, coherence, explainability.
Reproducibility & Reliability
Deterministic workflows: same inputs create the same validated output.