Snow Moon AI provides an infrastructure platform that converts ambiguous, real‑world data into deterministic large‑language‑model (LLM) workflows. By breaking input into atomic units, enforcing strict schema constraints, and routing through a state‑machine, the system delivers high‑volume entity extraction, knowledge‑graph reasoning, and automatic mapping of unstructured text to standard occupational taxonomies such as O NET.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations often need to extract structured information from large volumes of unstructured text, such as social media posts or documents, but current large‑language‑model (LLM) approaches produce inconsistent outputs and hallucinations, making the results unreliable for downstream tasks like taxonomy mapping or knowledge‑graph construction.
Solution
Snow Moon AI offers an infrastructure platform that transforms ambiguous textual inputs into deterministic LLM workflows through a three‑step protocol. First, raw text is broken into atomic units (action, artifact, target) to impose a clear structure. Second, strict schema constraints are applied, preventing hallucinations by validating each extracted entity against predefined taxonomies such as the U.S. O*NET/SOC classification. Third, the constrained outputs are routed through a state‑machine that orchestrates prompt chains and ensures repeatable processing. The platform includes high‑volume extraction pipelines, automated regression testing for prompt stability, and integration with graph databases (Neo4j) for precise knowledge‑graph reasoning. By combining these components, Snow Moon AI delivers reliable entity extraction, industry taxonomy mapping, and large‑scale knowledge‑graph construction for datasets like 100k+ tweets classified into thousands of use cases.
Target Audience
Primary customers are enterprises and product teams that need reliable large‑scale text extraction, taxonomy alignment, or knowledge‑graph construction for market research, demand analysis, or operational intelligence.
Features
- Atomic‑unit structuring of input text into Action, Artifact, and Target components
- Schema enforcement layer that validates extracted entities against standard taxonomies (e.g., O*NET/SOC) to eliminate hallucinations
- Workflow state machine that routes prompt results through deterministic state transitions
- High‑volume extraction pipeline capable of processing hundreds of thousands of records with automated regression testing
- Knowledge‑graph reasoning module that maps structured entities to Neo4j graph relationships
- Industry taxonomy mapping tool that automatically aligns unstructured text with occupational classification systems