NLP & Text
Processing

We audit your raw text formats, identifying formatting issues, encoding errors, and noise. We define cleaning rules and normalization targets for your pipeline.

Our team designs custom regular expressions and tokenizers to parse structured values from unstructured strings. We handle boundary cases, acronyms, and formatting errors.

We build preprocessing pipelines that handle lowercasing, accent normalization, and stop-word filtering. This step ensures that minor spelling differences do not impact search quality.

We implement advanced linguistic steps like lemmatization and part-of-speech tagging using libraries like spaCy. This adds grammatical structure and reduces words to their root forms.

We compile the text processing code into optimized worker nodes, enabling distributed execution across large database clusters. We benchmark throughput and optimize processing speeds.

We test the parsing pipeline against a validation corpus to measure formatting accuracy and recall. We refine extraction rules to handle edge cases before deployment.