Sold by: Latent-Sense Technologies Inc
Deployed on AWS
We create and provide cognitive AI infrastructure that makes AI decisions auditable, evidence-first, explainable, defensible, and safe as autonomy scales across mission-critical workflows. You can use our API-first solutions to make your unstructured data reasoning-ready, and deployable into your workflows, and AI stacks. No RAG. No re-platforming. No ETL required.
Overview
Enterprise AI systems often struggle with three fundamental problems:
- Unstructured knowledge is difficult for AI to reason over
- AI outputs cannot be fully explained or audited
- RAG pipelines lack structured semantic grounding
rxMaps solves these challenges by converting enterprise knowledge into a persistent semantic structure that AI systems can reason over. Using rxMaps, organizations can transform large document repositories into structured knowledge maps that preserve relationships between concepts, evidence, and reasoning paths.
These maps become a machine-readable reasoning layer that enables AI systems to: (a) navigate complex knowledge domains, (b) compare evidence across documents, (c) identify contradictions or gaps in reasoning and (d) generate outputs that are fully traceable to their source material. This enables teams to unlock value from their unstructured data while maintaining auditability, reliability, and governance.
What rxMaps Does?
rxMaps transforms enterprise documents and datasets into structured semantic reasoning maps. Each rxMap represents knowledge as: entities, relationships, propositions, evidence chains and reasoning signals. This structure allows AI systems to navigate meaning rather than just text, enabling deeper reasoning across complex information. The result is a persistent semantic knowledge layer that sits between enterprise data and AI applications.
Why rxMaps vs RAG vs Knowledge Graphs?
rxMaps combines the scalability of RAG with the semantic rigor of knowledge graphs, without the additional burdens of: manual modeling, data extraction pipelines, data ingestion pipelines, data indexation and transformation pipeline. All these capabilities come, for free, as part of creating rxMaps. This allows organizations to deploy AI systems that can reason over structured and unstructured enterprise knowledge while maintaining auditability and explainability of the AI reasoning.
Architecture Fit with AWS Stack
rxMaps is designed to integrate seamlessly with modern cloud data and AI architectures, including AWS data storage, engineering, AI, and analytics ecosystems. rxMaps acts as a semantic infrastructure layer that can sit within or between enterprise data storage layer, data engineering layer, AI systems layer. This architecture enables organizations to enhance existing pipelines rather than replacing them.
Highlights
- Enable traceable, Evidence First and auditable AI reasoning: Every inference generated from our rxMaps and ReX agent can be linked back to its supporting evidence, making AI outputs explainable and defensible in regulated or high risk environments.
- Create persistent AI memory for enterprise knowledge: rxMaps transform unstructured data, into durable semantic memory that enterprise AI systems can query and reason over repeatedly, enabling consistent and reliable outputs across applications.
- Improve AI reliability, reduce hallucinations and prevents semantic collapse: With our Glass-Box and Cognitive AI infrastructure components that operate on structured knowledge maps rather than only on raw text, they can enable reasoning over verified semantic relationships, significantly improving accuracy and reducing hallucination risks.
Details
Delivery method
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
Credits | Purchased credits for issuing cog runs | $0.000097 |
Dimensions summary
This listing uses a single usage-based pricing dimension: purchased credits. You buy credits upfront and spend them to issue cog runs, which trigger the reasoning services. Pricing scales with how many runs you execute, so cost tracks your actual usage rather than a fixed subscription. There are no separate tiers or instance sizes to choose. You draw down from your credit balance as you request runs. This structure lets you match spending to workload volume, buying more credits as your reasoning activity grows.
Top-of-mind questions for buyers
A cog run is a single request to one of the reasoning services, such as relationship discovery, PII redaction, relevance redaction, or map creation. Each run you issue draws down credits from your purchased balance. You spend credits per run, so cost tracks the number of runs you execute.
The service offers two run types. Small runs cap corpus size at 1 megabyte and use a streamlined two-step request-and-poll process. Large runs have no corpus size limit and require uploading files to storage first. Processing larger corpora through large runs can consume more of your credit balance.
You buy credits upfront and spend them as you issue cog runs. Once your balance is depleted, you purchase more credits to continue issuing runs. There are no separate tiers or subscription fees; you simply add credits as your reasoning activity grows.
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