NLP Development Services

Natural language processing development is the engineering of systems that read, interpret, classify, extract from, search, and generate human language. In enterprise applications, NLP processes text at scale: classifying thousands of tickets, extracting entities from thousands of contracts, analysing sentiment across millions of reviews, and searching documents by meaning. The core capabilities include text classification, entity extraction, sentiment analysis, semantic search, summarisation, and intent recognition - each requiring domain-specific training to work accurately on your real-world text.

Start with API services (Google Cloud NLP, AWS Comprehend, Azure Text Analytics) when your text is standard English, your entities are common types (names, dates, locations), and your classification categories are general. Move to custom when: your domain vocabulary is specialised (medical, legal, financial), your entity types are domain-specific (product codes, clinical terms, legal citations), your text is messy (abbreviations, jargon, mixed languages), or API-level accuracy doesn't meet your business requirements. Custom NLP closes the accuracy gap that matters.

For text classification: 200-500 examples per category minimum, 1,000+ ideal. For entity extraction (NER): 500-2,000 annotated documents minimum. For sentiment analysis: 1,000+ labelled examples. Quality matters more than quantity - 500 well-annotated examples with consistent labels outperform 5,000 with inconsistent annotation. If you don't have annotated data, we help create annotation guidelines and manage the labelling process - which typically consumes 40-60% of the project timeline.

Yes - using multilingual transformer models (mBERT, XLM-RoBERTa) and language-specific models where accuracy demands it. Cross-lingual entity extraction, sentiment analysis, and classification work across 50+ languages. The engineering challenge is language-specific: sentiment expression, entity formats, and text structure differ across languages, and a model trained on English sentiment misinterprets sarcasm, formality, and politeness patterns in other languages.

On well-defined entity types with sufficient training data: 90-97% F1 score on your domain text. On rare or ambiguous entity types: lower, requiring more training data or active learning. General-purpose NER (Google, AWS) achieves 85-95% on standard entities (person, location, organisation) but drops significantly on domain-specific entities (gene names, legal citations, product codes). Custom NER closes that gap - the accuracy improvement is the entire value proposition.

Those pages are PRODUCTS built on NLP. This page covers the BUILDING BLOCKS: text classification, entity extraction, sentiment analysis, semantic search, and summarisation. A chatbot uses intent classification and entity extraction (NLP capabilities). A copilot uses semantic search and summarisation (NLP capabilities). RAG uses semantic search and embedding (NLP capabilities). If you need a finished product (chatbot, copilot, search), start with that page. If you need the text intelligence capability itself - customised for your domain - start here.

A focused capability (classification or extraction) on well-defined text: 8-12 weeks. A full NLP pipeline with multiple capabilities across document types: 12-20 weeks. Enterprise multilingual systems: 4-8 months. Data annotation consumes 40-60% of the timeline - the phase most teams underestimate. We share a realistic timeline after the text analysis, because annotation volume depends on your text complexity and entity types.

Yes - with domain-specific training. Customer emails with typos, support tickets with abbreviations, social media with slang, and medical records with non-standard shorthand all require training data that represents those patterns. A model trained on formal English fails on informal text. A model trained on YOUR informal text handles it. The engineering is in the annotation guidelines and the training data curation - teaching the model what "messy" looks like in your specific domain.

PII detection and redaction built into the processing pipeline. Names, addresses, social security numbers, medical record numbers, and financial identifiers detected and anonymised before text reaches the model for training or inference. HIPAA-compliant architecture for medical text. GDPR-compliant processing for EU data. The privacy layer is designed into the pipeline architecture, not applied as a filter afterward.

Phase 1 (API-level NLP for classification, routing, basic extraction): 2-3x ROI within 6 months on $20K-$50K investment. Custom NLP for domain-specific extraction and classification: ROI varies by text volume and processing cost saved. Zurich Insurance: 40,000 hours saved annually. Insurance industry: 50-70% processing time reduction. Email routing: 60% fewer misrouted messages, 40% faster response. The ROI scales with text volume - the more text you process, the larger the return on NLP investment.