Built for technical teams to extract structured specifications from large PDF collections while maintaining strict logical control and data integrity.
Problem
Extracting reliable data from large-scale technical documents often results in inconsistent formatting and high rates of AI hallucinations.
Unstructured Data: Large PDF collections are difficult to parse systematically without losing context or technical nuance.
Accuracy Risks: Standard LLM outputs often include hallucinations, which are unacceptable for technical specifications.
Lack of Logic Control: Traditional extraction methods lack the deterministic rules needed to ensure the final output follows strict engineering schemas.
Solution
The system utilizes an "LLM as extractor, rules as arbiter" architecture to combine generative power with deterministic verification.
Hybrid Extraction Pipeline: Uses LLMs for initial semantic parsing followed by a rigid rule-based engine for final data validation.
Schema Validation: Implements strict ontologies and schema checks to ensure every extracted artifact meets predefined technical standards.
Controlled Logic: Features priority thresholds and specific fail-behaviors to handle ambiguous data without human intervention.
Structured Indexing: Transforms raw text into a searchable, indexed knowledge base ready for API integration.
Tech Stack
Core Logic: LLMs for semantic extraction.
Validation: Custom deterministic rule engines and schema validators.
Integration: REST APIs for downstream system connectivity.
Results & Impact
Data Reliability: Transformed raw knowledge into verifiable, structured artifacts with minimal hallucination rates.
Operational Speed: Significantly accelerated the preparation of technical specifications and project documentation.
Risk Reduction: Lowered implementation risks by ensuring all data follows strict, pre-validated logic.
Scalability: Enabled the processing of massive document volumes that were previously impossible to manage manually.