NLP Development Services

NLP development is building systems that process and understand human language, classifying text into categories, extracting specific information from documents, detecting sentiment and intent, summarising long content, and translating between languages. Custom NLP development means training or fine-tuning models on your specific data and domain rather than using generic pre-trained models with limited customisation. Custom models significantly outperform generic ones on domain-specific vocabulary: medical terminology, legal language, technical product descriptions, or financial jargon all require domain adaptation to achieve production-grade accuracy.

Traditional NLP (fine-tuned BERT, RoBERTa, SpaCy) is faster, cheaper per inference, and more suitable for high-volume applications where latency and cost are constraints. These models are trained on labelled data and excel at structured classification and extraction tasks. LLM-based NLP (GPT-4o, Claude, Gemini) is more flexible, handles complex reasoning and nuance, and requires fewer labelled examples to achieve good performance. It is better for complex extraction, summarisation, and tasks where the output needs to explain reasoning. We choose the right approach based on your volume, latency requirements, accuracy targets, and cost constraints.

For fine-tuned classification models (BERT-based), 500-5,000 labelled examples per class typically delivers production-grade accuracy. For named entity recognition (extracting specific fields from documents), 200-2,000 annotated documents. LLM-based approaches via few-shot prompting require as few as 10-50 examples to demonstrate the pattern. The right approach depends on your existing labelled data volume, we assess this during scoping and recommend the most cost-effective path.

Document classification (routing support tickets, classifying legal documents, categorising financial transactions), named entity extraction (extracting parties, amounts, dates, and clauses from contracts; extracting diagnoses and medications from clinical notes), sentiment and intent detection (customer feedback analysis, support ticket urgency scoring, product review analysis), text summarisation (long document summaries for executives, clinical note summarisation, contract key term extraction), and language translation and normalisation (standardising product descriptions, translating multilingual customer feedback).

NLP models are deployed as REST APIs. Your existing application sends text input and receives structured output, a classification label, an extracted entity list, a sentiment score, or a generated summary. For batch processing, we build pipeline integrations that process document queues and write results to your database or data warehouse. Integration with CRM, support platforms, document management systems, and BI tools is standard. The model runs as a microservice and connects to your stack via API.

A focused NLP system for a single task (document classification or entity extraction) with model training, validation, and API deployment typically runs $20,000-$50,000. Multi-task NLP platforms with pipeline integration and multiple extraction models run $50,000-$120,000. LLM-based implementations using prompt engineering and RAG run lower ($15,000-$35,000) with higher monthly inference costs. We scope every project before pricing.