Machines that read your documents the way your experts do.

Language that seems simple to humans is structurally complex at scale.

Natural language processing for text classification, entity extraction, sentiment analysis, document intelligence, and semantic search. Built on 18 years of processing millions of documents across clinical, legal, financial, and operational domains.

10M+documents processed

12domain-specific NLP systems shipped

5regulated industries served

Talk to the AI team →See related work

Domain-specific NLP

General models fail on specific language.

Pre-trained models perform well on internet text. Your business runs on clinical terminology, legal boilerplate, financial jargon, or proprietary nomenclature. The gap between general NLP and domain NLP is where accuracy lives — and where most projects fail.

23%

avg. accuracy gain from domain-specific training data

AurvikAI project benchmarks

Domain annotation pipelines

Custom labelling workflows with domain expert reviewers and inter-annotator agreement measurement.

Terminology models

Fine-tuned on your specific vocabulary, abbreviations, and naming conventions — not general internet text.

Evaluation with domain experts

Human-validated evaluation sets built with your team, measuring real business impact alongside F1 scores.

Confidence scoring

Every output includes confidence scores with defined thresholds for human review routing.

NLP capabilities we deliver

From document classification to semantic search — production systems for text at scale.

Text classification

Support ticket routing, content categorisation, intent detection, and sentiment analysis. Multi-label taxonomies handling hundreds of categories with configurable confidence thresholds.

ClassificationMulti-labelRouting

96%

classification accuracy

How we build NLP systems

A process designed for language ambiguity and domain complexity.

Classification, extraction, summarisation, and search have different architectures and different failure modes.

Problem specificationStart

Defining the exact NLP task including edge cases, ambiguous inputs, and acceptable error profiles.

Data requirementsData

Mapping the training data needed — volume, diversity, and annotation quality — against what exists.

Success metricsEvaluation

Business-relevant metrics defined before modelling begins — not just F1 score.

Domain experts annotating text data in a custom labelling interface for NLP model training

Training data quality

Domain-specific training data is the moat.

General pre-trained models are available to everyone. The competitive advantage comes from training data that reflects your specific language, your specific edge cases, and your specific quality standards. We build annotation pipelines designed for consistency, speed, and domain accuracy.

We build annotation pipelines, evaluation frameworks, and model selection processes that reflect your actual language — not general internet text.

95%+

inter-annotator agreement

50K+

annotations per project

Discuss your NLP challenge →

Common questions about NLP development

From model selection to production reliability.

Depends on the task. Classification with a clear taxonomy and sufficient training data is often better served by a fine-tuned smaller model — faster, cheaper, more predictable. Open-ended generation, summarisation, and question answering benefit from LLMs. Many production systems use both — LLMs for understanding, smaller models for classification and routing.