Natural language processing turns unstructured text -- emails, support tickets, contracts, clinical notes, user reviews -- into structured data your systems can act on. We build NLP systems that classify, extract, summarise, and interpret text at scale. Not generic sentiment scores. Models trained on your domain vocabulary that understand what your customers, documents, and users are actually saying.
Recent outcomes
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Text-based interviews converted to automated phone calls
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2,000+ shipments yr 1RaftLabs builds custom natural language processing systems for classification, entity extraction, sentiment analysis, and text summarisation on domain-specific data. We use traditional ML (fine-tuned BERT, RoBERTa) for high-volume, latency-sensitive applications and LLM-based approaches (GPT-4o, Claude) for complex extraction and summarisation tasks. Every system is trained on your domain vocabulary and connects to your existing data pipeline or operational applications. A focused NLP system typically runs $20,000–$50,000.
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Most businesses are swimming in unstructured text: support tickets, customer emails, contracts, product reviews, clinical notes, compliance documents. Structured data in databases gets analysed. Unstructured text sits in folders and inboxes.
NLP systems turn that text into structured signals -- classifications, scores, extracted entities, summaries -- that your dashboards, CRMs, and operations systems can act on.
Capabilities
Automatic categorisation of incoming documents, emails, and tickets into defined categories -- routing them to the right queue, workflow, or system without human triage. BERT and RoBERTa fine-tuned on your labelled document corpus outperform generic classifiers significantly on domain-specific vocabulary: a general-purpose classifier often fails on insurance policy language, clinical terminology, or legal agreement types where in-domain training data is the difference. For high-volume, latency-sensitive pipelines (10,000+ documents per day), we fine-tune distilled models (DistilBERT, MiniLM) that run 3-6x faster with less than 3% accuracy loss versus full BERT. For lower-volume use cases requiring minimal labelled data, LLM-based few-shot classification via GPT-4o or Claude achieves production-grade accuracy from 20-50 examples. Multi-label classification handles documents that belong to multiple categories simultaneously (a contract that is both a service agreement and a data processing addendum). Accuracy benchmarks validated on held-out data from your document set before deployment, with the confusion matrix shown for each class -- not just headline accuracy.
Structured data extraction from unstructured text: parties, dates, amounts, and governing law clauses from contracts; diagnoses, medications, and dosages from clinical notes; company names, transaction amounts, and counterparties from financial documents; dimensions, materials, and certifications from supplier product sheets. spaCy and Hugging Face token classification models fine-tuned on your annotated documents handle high-volume, latency-sensitive extraction; LLM-based extraction (GPT-4o structured outputs, Claude tool use) handles complex, variable-format documents where rigid entity schemas don't capture the variation. Custom entity types are trained on your domain vocabulary using annotation tools (Label Studio, Prodigy) -- a medical billing system has different entity requirements than a contract management platform. Nested entity handling extracts entities within entities (a medication with its dose, route, and frequency as sub-attributes). Output delivered as structured JSON to your database, ERP, or document management system, replacing the manual data entry step that currently sits between document receipt and system record creation.
Customer sentiment scoring on reviews, support conversations, and feedback -- at the aspect level, not just the document level. Aspect-based sentiment analysis (ABSA) identifies which specific product attributes or service dimensions are driving positive or negative sentiment: a hotel review can score food, service, and cleanliness separately rather than collapsing to a single score that obscures what's actually broken. Intent classification for support routing and chatbot NLU distinguishes billing question from technical issue from cancellation risk from feature request -- enabling workflow routing that reduces escalation rates by directing tickets to the right specialist queue on first contact. Urgency detection models trained on your resolved ticket history learn which text signals correlate with high escalation or high customer impact, surfacing critical issues before SLA clocks expire. NPS driver analysis identifies which topics co-occur most strongly with promoter (9-10) vs. detractor (0-6) scores -- giving product and customer success teams specific roadmap inputs rather than a net score that doesn't explain why. Delivered as per-record scores and labels with confidence values, not aggregated summary statistics that obscure the distribution.
Automated summarisation of long documents at the speed and volume that manual reading cannot match: clinical note summarisation surfaces relevant patient history before a clinical encounter so physicians spend review time on judgment rather than document archaeology; contract key term extraction generates a structured term sheet (parties, obligations, payment terms, termination clauses, renewal dates) from 50-page agreements in seconds; research paper summarisation delivers method, findings, and limitations in 200 words for literature review pipelines. Extractive summarisation (selecting the most important existing sentences) works well for news and regulatory documents where verbatim accuracy matters; abstractive summarisation using fine-tuned T5, BART, or LLM-based approaches (GPT-4o, Claude) generates new prose that synthesises across document sections -- more fluent but requires validation for high-stakes use cases. Summary length and focus are configurable per reader type: an executive summary of an earnings call differs from a compliance officer's summary of the same transcript. For medical and legal use cases, confidence scoring and source sentence attribution allow the human reviewer to verify the summary against the original document rather than trusting it blindly.
NLP models that handle multiple languages from your customer base or document sources -- without maintaining separate models per language or losing accuracy on non-English text. Multilingual transformer models (XLM-RoBERTa, mBERT) support 100+ languages in a single model, enabling classification and extraction pipelines to process mixed-language document sets without a language detection routing layer adding latency and complexity. For languages where the multilingual model underperforms (typically lower-resource languages or highly domain-specific vocabulary), language-specific fine-tuning on your target language data closes the accuracy gap. Translation-based pipelines -- translate to English, apply a high-accuracy English model, translate outputs back -- work well when speed of deployment matters more than per-language accuracy. Language detection and routing (using fastText's language identification, which covers 176 languages at under 1ms per document) directs incoming support tickets, reviews, and documents to the correct processing pipeline. Particularly relevant for global e-commerce companies processing product reviews in 15+ languages, international financial services firms with multilingual compliance documents, and multinational enterprises running unified customer feedback analytics across regions.
Clause extraction and risk flagging in contracts, agreements, and regulatory documents -- the category where generic NLP models fail fastest because legal language is domain-specific, jurisdiction-dependent, and precision-critical. Legal NER models trained on annotated contract corpora extract parties, effective dates, payment obligations, limitation of liability caps, indemnification scope, governing law, and notice requirements as structured fields rather than free text. Risk clause detection identifies non-standard language in incoming contracts -- a limitation of liability clause with an unusual carve-out, a data processing agreement missing required GDPR provisions, an auto-renewal clause with atypically short notice periods -- flagging the specific clause text for attorney review rather than requiring full-document manual review. Policy document comparison detects substantive changes between document versions, distinguishing edits that affect legal obligations from formatting and language cleanup. Every extraction is returned with a confidence score and source text reference so compliance officers and attorneys can verify the output and retain the professional judgment that automated extraction cannot replace. Audit logs record every document processed, every field extracted, and every confidence score assigned -- the evidentiary trail required for compliance workflows.
Document types, current volume, what you need to extract or classify, and where the output needs to go. We'll give you a fixed-cost proposal.
Machine Learning Development -- ML systems beyond NLP including structured data models
Intelligent Document Processing -- full document workflow automation combining OCR and NLP
AI Document Intelligence -- AI-native document workflows
Generative AI Integration -- LLM integration for text generation and summarisation
Custom AI Development -- AI-native products from scratch
Frequently asked questions
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.
Work with us
We scope NLP Development Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.