Why standard parsing fails agents
What flat text looks like vs. what agents need
Standard text extraction strips away the context that makes documents meaningful. GroundX rebuilds it — preserving the structure, relationships, and visual semantics that agents rely on to answer accurately.
Table comprehension
Standard parsing flattens tables to rows of text — agents cannot tell which column a value belongs to or how cells relate.
GroundX reconstructs cell relationships, column headers, and spanning cells as explained semantic objects.
Figures and charts
Images and charts are invisible to text-based parsers — critical information in diagrams or graphs is silently dropped.
GroundX's vision models interpret figures and generate natural-language descriptions agents can reason over.
Arbitrary chunking
Fixed-size chunking splits concepts mid-sentence — retrieved passages lack context and produce incomplete or hallucinated answers.
GroundX chunks at natural conceptual boundaries, keeping each retrieved passage self-contained and grounded.
Cross-page context
Standard parsers treat each page independently — totals referencing prior rows, or conclusions summarizing earlier sections, lose their meaning.
GroundX maintains context across page boundaries and grounds every chunk in a full-document summary.
Search precision
Vector similarity alone retrieves semantically adjacent text — not necessarily the passage that answers the question.
GroundX combines vector and semantic search with proprietary re-ranking, consistently outperforming pure vector retrieval.
Hallucination reduction
90% of LLM hallucinations in RAG pipelines are data problems — incomplete or decontextualized input causes the model to fill gaps incorrectly.
ParseX's deep contextualization ensures agents receive complete, grounded passages — not fragments that invite fabrication.
