[{"data":1,"prerenderedAt":206},["ShallowReactive",2],{"svc-detail-nlp":3,"svc-work-nlp":147,"svc-blog-nlp":194,"ill-services\u002Fnlp":205},{"slug":4,"short":5,"title":6,"tagline":7,"answer":8,"summary":9,"icon":10,"subServices":11,"useCases":64,"benefits":80,"process":86,"tools":102,"industries":113,"faq":120,"relatedWork":136,"relatedServices":139,"seo":144},"nlp","NLP Services","Natural Language Processing (NLP) services","Turn text and speech into structured, usable data.","NLP services turn unstructured text and speech (tickets, reviews, emails, calls, documents) into structured data you can route, analyse and act on. We build sentiment analysis, entity recognition, classification, intent detection, topic modelling, speech-to-text and OCR-plus-NLP pipelines using transformer models and LLMs, for teams that need insight and automation at a scale people cannot read.","Sentiment analysis, entity recognition, text and document classification, intent detection, topic modelling, speech-to-text and OCR+NLP pipelines built with transformers and LLMs, evaluated on your data and deployed as APIs or batch jobs.","layers",[12,16,20,24,28,32,36,40,44,48,52,56,60],{"slug":13,"name":14,"text":15},"sentiment-analysis","Sentiment analysis","Aspect-level sentiment over reviews, tickets, surveys and social data with dashboards and alerts.",{"slug":17,"name":18,"text":19},"named-entity-recognition","Named entity recognition","Extract people, organisations, products, amounts, dates and custom entities from documents and messages.",{"slug":21,"name":22,"text":23},"text-classification","Text classification","Route and label emails, tickets and content by category, urgency or risk with measured accuracy.",{"slug":25,"name":26,"text":27},"keyword-extraction","Keyword & phrase extraction","Surface key terms and phrases from large text collections for tagging, search and analysis.",{"slug":29,"name":30,"text":31},"topic-modelling","Topic modelling","Discover and track themes across feedback, calls and support data over time.",{"slug":33,"name":34,"text":35},"intent-detection","Intent detection","Understand what a customer wants from a message or utterance to route or automate the response.",{"slug":37,"name":38,"text":39},"spam-detection","Spam & abuse detection","Filter spam, fraud attempts and abusive content in forms, chats and marketplaces.",{"slug":41,"name":42,"text":43},"speech-to-text","Speech-to-text","Accurate transcription of calls, meetings and media with speaker labels, timestamps and domain vocabulary.",{"slug":45,"name":46,"text":47},"text-to-speech","Text-to-speech","Natural voices for IVR, accessibility, content and assistants, in multiple languages.",{"slug":49,"name":50,"text":51},"ocr-nlp-pipelines","OCR + NLP pipelines","Read scanned documents and images, then extract and classify the content into structured records.",{"slug":53,"name":54,"text":55},"language-detection","Language detection & routing","Detect language and dialect to route conversations and content to the right model or team.",{"slug":57,"name":58,"text":59},"semantic-analysis","Semantic analysis & similarity","Embeddings-based similarity, deduplication and clustering for content, tickets and records.",{"slug":61,"name":62,"text":63},"document-classification","Document classification","Sort incoming documents (invoices, contracts, IDs, claims) into types automatically for downstream processing.",[65,68,71,74,77],{"title":66,"text":67},"Voice-of-customer analytics","Mine reviews, surveys and tickets for themes and sentiment trends that drive product and service decisions.",{"title":69,"text":70},"Ticket routing and prioritisation","Classify incoming requests by intent and urgency so they reach the right queue immediately.",{"title":72,"text":73},"Call transcription and QA","Transcribe every call and score it for compliance, sentiment and outcomes.",{"title":75,"text":76},"Document intake","Read scans and PDFs, classify them and extract fields into your systems.",{"title":78,"text":79},"Content moderation","Detect spam, abuse and policy violations at scale with human review of edge cases.",[81,82,83,84,85],"Structured insight from unstructured text and speech","Faster routing and response with fewer errors","Trends spotted early across thousands of conversations","Models evaluated on your data, not generic benchmarks","Deployed as APIs or batch jobs that fit your stack",[87,90,93,96,99],{"step":88,"text":89},"Data and label review","Sample your text or audio, define categories and entities, and agree what good output looks like.",{"step":91,"text":92},"Model selection","Compare LLM prompting, fine-tuned transformers and classical methods on accuracy, cost and latency.",{"step":94,"text":95},"Pipeline build","Ingestion, pre-processing, inference and post-processing as an API or scheduled job with monitoring.",{"step":97,"text":98},"Evaluation and calibration","Held-out test sets, confusion analysis and confidence thresholds for human review.",{"step":100,"text":101},"Deploy and monitor","Production release with drift monitoring and a retraining or re-prompting plan.",[103,104,105,106,107,108,109,110,111,112],"Hugging Face Transformers","spaCy","OpenAI","Anthropic Claude","Whisper","Deepgram","scikit-learn","PyTorch","Python","Tesseract",[114,115,116,117,118,119],"retail-ecommerce","saas-technology","finance","healthcare","professional-services","logistics",[121,124,127,130,133],{"q":122,"a":123},"Do we still need classic NLP now that LLMs exist?","Often not for prototypes: an LLM prompt gets you a working classifier in an afternoon. At scale, though, a small fine-tuned model is cheaper, faster and more consistent for narrow tasks like routing or entity extraction. We usually start with an LLM to label data and validate the idea, then decide whether to distil.",{"q":125,"a":126},"How much labelled data do you need?","Less than most teams expect. With LLM-assisted labelling and transfer learning, a few hundred well-chosen examples per category is often enough for a first model, and we improve from production feedback. If labels do not exist, we help design a labelling process your team can sustain. Active learning, where the model picks the most informative examples to label next, keeps the effort focused where it changes results.",{"q":128,"a":129},"Can you handle multiple languages?","Yes. Multilingual transformer models and modern speech models cover most major languages, and we test accuracy per language on your data. Where a language is under-served we combine translation, language-specific models or targeted fine-tuning. Right-to-left scripts, transliterated text such as Roman Urdu, and code-switching are common in our clients’ data and are tested explicitly.",{"q":131,"a":132},"How accurate is speech-to-text on our calls?","Modern models perform very well on clear audio and degrade with noise, crosstalk and heavy accents. We measure word error rate on a sample of your recordings, add domain vocabulary and choose the model accordingly, so you get a real number rather than a vendor claim. For telephony audio, which is compressed and narrowband, we test the specific codec you use because it changes results noticeably.",{"q":134,"a":135},"How do you deliver the results?","As a REST API your systems call in real time, as batch jobs that write to your warehouse, or directly into dashboards. Most projects include a monitoring view so you can see volumes, categories and confidence over time. Predictions are stored with the model version that produced them, so results can be audited and compared when the model is updated.",[137,138],"customer-feedback-nlp","marketing-eda",[140,141,142,143],"voice-ai","ai-chatbots","data-analytics-predictive-ai","ai-automation",{"title":145,"description":146},"NLP Services: Sentiment, NER & Text Analytics | AIYatch","NLP services: sentiment analysis, entity recognition, text and document classification, intent detection, speech-to-text and OCR+NLP pipelines.",[148,162,175],{"path":149,"title":150,"description":151,"industry":152,"services":153,"results":154,"cover":161,"coverAlt":161},"\u002Fwork\u002Fstock-market-deep-learning","Deep-learning market analysis for a fintech","Time-series and sentiment models feeding a portfolio-monitoring platform for an investment-focused fintech.","Finance & Fintech",[142,4],[155,158],{"value":156,"label":157},"22%","improvement in model-guided returns vs. baseline strategy",{"value":159,"label":160},"Daily","automated retraining and reporting",null,{"path":163,"title":164,"description":165,"industry":166,"services":167,"results":168,"cover":161,"coverAlt":161},"\u002Fwork\u002Fcustomer-feedback-nlp","NLP for customer feedback analysis","Topic, sentiment and urgency models over reviews and tickets, feeding a weekly voice-of-customer report.","SaaS & Technology",[4,142],[169,172],{"value":170,"label":171},"50k+","reviews and tickets classified per month",{"value":173,"label":174},"Weekly","automated voice-of-customer report",{"path":176,"title":177,"description":178,"industry":179,"services":180,"results":182,"cover":192,"coverAlt":193},"\u002Fwork\u002Finvoice-document-processing-pipeline","AI invoice and document-processing pipeline for a logistics operator","An automated pipeline that reads supplier invoices, delivery notes and customs documents, validates them against orders and posts to the ERP with human review only on exceptions.","Logistics",[143,4,181],"ai-integration",[183,186,189],{"value":184,"label":185},"~80%","of invoices posted with no manual touch",{"value":187,"label":188},"3 days → same day","typical processing time",{"value":190,"label":191},"\u003C1%","field-level error rate after validation","\u002Fcovers\u002Fwork\u002Finvoice-document-processing-pipeline.svg","Abstract navy cover with a rising cyan course line",[195,200],{"path":196,"title":197,"cover":198,"coverAlt":199},"\u002Fblog\u002Fai-invoice-document-processing-playbook","AI invoice and document processing: a practical playbook","\u002Fcovers\u002Fblog\u002Fai-invoice-document-processing-playbook.svg","Line-art illustration of a paper invoice passing through a scanner frame and emerging as tidy data fields.",{"path":201,"title":202,"cover":203,"coverAlt":204},"\u002Fblog\u002Fnlp-for-business-insights","NLP for business: turning feedback, tickets and documents into 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