Noetic Systems is the foundational semantic mapping layer for enterprise LLMs — turning fragmented datasets into connected, queryable intelligence.
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Trusted by Innovative Teams
Fragmented pipelines, undefined metrics, and missing relationships make it impossible for LLMs to reason correctly — no matter how good the model.
Critical data assets are siloed across warehouses, lakes, and SaaS systems with no unified semantic view.
Teams debate what a metric actually means, leading to inconsistent reports and eroded stakeholder trust.
Field names and data types drift across pipelines, making joins unreliable and documentation obsolete.
The connections between datasets go unmapped, leaving LLMs and analysts without the context they need.
Metrics are built before the underlying semantics are understood, creating technical debt from day one.
Data teams spend weeks manually cataloging what already exists before answering any business question.
The Solution
A living semantic map of every metric, dataset, and relationship across your enterprise — purpose-built for LLM reasoning.
Core Engine
Automatically ingests, semantically annotates, and links every data asset across your warehouse — creating a queryable, LLM-ready semantic layer that understands context, granularity, and lineage.
How It Works
Four agentic steps. Zero manual cataloging. Full semantic coverage.
Connects to your data warehouse, lake, or API layer. Auto-profiles every table, field, and schema — capturing type, range, and cardinality.
Applies domain-aware semantic labels to every field. Understands that "revenue_usd" and "rev_local" are related but not equivalent.
Builds the Metrics Topology Graph — linking datasets, defining valid comparison paths, and flagging granularity mismatches.
Sits between your LLM and your data. Validates every query against the semantic graph and returns accurate, attribution-ready answers.
Use Cases
Illustrative examples of the semantic datasets Noetic Systems can generate from your existing data infrastructure.
Representative examples for illustration.
Schema
customer_idlifecycle_stagechurn_risk_scorecohort_monthRecommended Metrics
↗ Links to Revenue Intelligence via customer_id
Schema
account_idarrexpansion_flagbilling_periodRecommended Metrics
↗ Joins Product Engagement on account_id
Schema
user_idfeature_idsession_depthevent_tsRecommended Metrics
↗ Feeds Customer Retention engagement signals
Schema
workflow_idstagesla_hoursowner_teamRecommended Metrics
↗ Correlates with support ticket volume
Schema
segment_idsegment_labelrevenue_bandindustrygeo_regionRecommended Metrics
↗ Links to Customer Retention and Revenue Intelligence via segment_id
AI Insights
Every metric, relationship, and data lineage node — continuously updated and queryable by your LLMs.
Engineering complexity
34low
Data quality score
87%
Entities mapped
142
Metrics recommended
28
Semantic topology preview
142 entities mapped · 28 metrics suggested
Early Access
Join data and AI leaders building LLM deployments they can actually trust. Limited early access seats available.