Natural language processing
Werkon builds language-processing systems that preserve the original text, identify language and boundaries deliberately, apply one bounded task, attach labels or matches to evidence, and route uncertain cases for review.
Language-task contract
The NLP boundary connects source text and its language context to one structured task, representative annotation, evaluation, downstream use, uncertainty, review, and correction. It also preserves the raw input needed to investigate every result.
Language path
Annotation disagreement often reveals that the business concept, source context, or label boundary is unclear. Resolving that ambiguity before model work produces stronger requirements and a more honest evaluation.
Specify the character, token, span, sentence, message, document, pair, or collection being processed, its language context, the exact output record, downstream use, unknown state, and human authority.
Collect lawful representative text across languages and sources, preserve raw records, write label and span guidance, measure disagreement, adjudicate difficult cases, and create protected splits without leakage.
Measure the current process, deterministic parsing, dictionaries, search or embeddings, available task models, and structured generation under the same language and segment evaluation.
Return source identity and spans with the structured result, validate schemas and permissions, expose uncertainty, route unknowns and conflicts, record edits and overrides, and protect downstream state changes.
Stage volume, monitor language, label, source, length, ambiguity, and workflow measures, review new terms and errors, update annotation and evaluation before models, and retain rollback and manual handling.
Processing method
One language workflow may combine several methods. Exact structure should remain deterministic, learned interpretation should be evaluated, and every transition should preserve the source record.
Use Unicode-aware normalization, segmentation, regular expressions, dictionaries, grammars, or ordinary code when the required boundary or value can be specified and tested exactly.
Evidence: Encoding and normalization policy, language scope, boundary rules, pattern owner, positive and negative cases, span tests, false-match review, and change history.
Use a classifier, tagger, extractor, or ranker when representative annotations express a stable concept and the model clears rules and current practice by relevant language and segment.
Evidence: Annotation agreement, class balance, clean splits, baseline comparison, per-label and per-language results, calibration, threshold, errors, and retraining trigger.
Use embeddings or another semantic representation for retrieval, similarity, grouping, or candidate ranking when relevance can be judged against real queries and hard negatives.
Evidence: Query and corpus set, relevance judgments, hard negatives, language coverage, recall and ranking measures, threshold behavior, source freshness, and retrieval inspection.
Use a generative model for bounded extraction or transformation only when output is schema-validated, tied to source spans, evaluated against difficult cases, and rejected when evidence is missing.
Evidence: Model and prompt version, schema, source-support test, field-level results, unsupported-value rate, invalid-output path, review load, cost, latency, and fallback.
Language controls
Normalization, segmentation, translation, truncation, and extraction can change meaning or break the path back to a source. The structured result must retain enough evidence for a person or system to verify it.
Engagement fit
Source basis
Unicode Consortium
Unicode Standard Annex #29: Unicode Text SegmentationThe current Unicode 17.0 annex defines default grapheme, word, and sentence boundary guidance, conformance, normalization considerations, tailoring, and tests while noting that language conventions can require different boundaries.
RFC Editor
RFC 5646: Tags for Identifying LanguagesThis part of BCP 47 defines the structure and semantics of language tags, including language, script, region, variants, extensions, private use, registry authority, and stability rules.
NIST AI Resource Center
AI RMF CoreCurrent voluntary guidance calls for documented test sets, deployment-context evaluation, relevant segment measures, domain and user input, limitations, monitoring, feedback, and appeal. AI RMF 1.0 is currently under revision.
A Systems Audit is the usual starting point. If the opportunity is already clear, we can move directly into a focused build.
Show Us the WorkflowStart with the free automation readiness checklistOBSERVEQUANTIFYDECIDEBUILD
Continue with the work
Stay with the operating question. The technology can wait until the work is clear.
Connect language-model expertise to grounded retrieval, extraction, and evaluation needs.
Open path02Compare NLP with generative AI, custom systems, model development, integration, agents, automation, vision, deployment, and support.
Open path03Use source-grounded generation when the output is a summary, answer, reviewed draft, or deliberate synthetic artifact rather than a structured language record.
Open path04Create the dataset lineage, baseline comparison, protected evaluation, reproducible artifact, and release evidence for a task-specific language model.
Open path05Carry source records, identities, structured language output, review, failure handling, and reconciliation across the wider product boundary.
Open path