NLP Development Services

Frequently Asked Questions

• Q:

  What NLP solutions does Azumo build?

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  Azumo builds production natural language processing systems for document classification, entity extraction, sentiment analysis, text summarization, language translation, search relevance, chatbots, and voice interfaces. We work across the full NLP stack: rule-based systems for structured extraction, classical ML models like BERT and RoBERTa for classification, and large language models from OpenAI, Anthropic Claude, LLaMA, and Mistral for generative tasks. Real-world projects include an AI-powered supplier search tool for Meta that parses unstructured vendor data using NLP, a standalone NLP model for gaming, and document processing systems for financial services clients. We deploy on AWS, Azure, and Google Cloud. SOC 2 certified with nearshore engineering teams across Latin America working in US time zones.

• Q:

  What is the difference between NLP, NLU, and NLG?

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  NLP (natural language processing) is the broad field of teaching machines to work with human language. NLU (natural language understanding) is the subset focused on comprehension: parsing intent, extracting entities, resolving ambiguity, and classifying meaning from text. NLG (natural language generation) is the subset focused on producing human-readable text from structured data or prompts. A customer support system uses all three: NLU to understand the question, NLP pipelines to retrieve relevant information and match intent to knowledge base entries, and NLG to compose a natural-sounding response. Azumo builds systems that combine NLU and NLG. For example, our RAG-based document Q&A systems use NLU to parse queries, semantic search to retrieve context, and LLM-powered NLG to synthesize coherent, cited answers.

• Q:

  What NLP models and frameworks does Azumo work with?

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  Azumo works with transformer-based models including BERT, RoBERTa, DeBERTa, and DistilBERT for classification and extraction tasks. For generative NLP, we use OpenAI GPT-4o, Anthropic Claude, LLaMA 3, and Mistral. We build with Hugging Face Transformers, spaCy for entity recognition and dependency parsing, and NLTK for text preprocessing. For production deployments, we use ONNX Runtime and TensorRT for inference optimization. Cloud services include AWS Comprehend, Azure Cognitive Services, and Google Cloud Natural Language API for teams that prefer managed NLP. We also fine-tune open-source models on your domain data when off-the-shelf accuracy is insufficient. Valkyrie, our AI infrastructure platform, provides unified model access across providers.

• Q:

  How does Azumo approach custom NLP model development?

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  Every NLP project starts with data assessment: evaluating your text corpus for volume, quality, language distribution, and label availability. We then select the right approach based on your accuracy requirements and budget. For classification tasks, we typically fine-tune BERT-family models on your labeled data, which achieves high accuracy with relatively small training sets (500-2000 examples per class). For extraction tasks like named entity recognition, we combine pre-trained models with domain-specific training data. For generative tasks, we use prompt engineering, few-shot learning, or LLM fine-tuning depending on complexity. We build evaluation pipelines that measure precision, recall, and F1 on held-out test sets from your actual data. All NLP models go through bias testing and edge case evaluation before production deployment.

• Q:

  What industries benefit most from NLP development?

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  NLP delivers strong ROI in financial services, healthcare, legal, e-commerce, and media. Financial firms use NLP for earnings call analysis, SEC filing extraction, fraud detection in transaction narratives, and automated compliance reporting. Healthcare organizations automate clinical note processing, medical coding (ICD-10), and literature review while maintaining HIPAA compliance. Legal teams accelerate contract analysis, clause extraction, and regulatory research across thousands of documents. E-commerce companies use NLP for product categorization, review sentiment analysis, and search relevance improvement. Media companies automate content tagging, summarization, and multilingual translation. Azumo has built NLP systems across all these verticals, including Meta's supplier search tool and document processing systems for financial services.

• Q:

  How long does it take to build a custom NLP solution?

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  A proof-of-concept NLP model on your data can be delivered in 1-3 weeks depending on data readiness. Production-ready NLP systems with enterprise integrations typically take 2-6 months. Key timeline factors are data preparation (cleaning, labeling, and augmentation take the most time), model selection and fine-tuning, integration complexity, and accuracy requirements. Simple classification tasks with clean labeled data can reach production in 6-8 weeks. Complex extraction or multi-language systems take longer. Azumo accelerates delivery with pre-built text processing pipelines, established evaluation frameworks, and active learning workflows that minimize the labeling effort required. Our nearshore teams work in US time zones with sprint-based delivery and daily standups.

• Q:

  How does Azumo handle multilingual NLP?

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  Azumo builds multilingual NLP systems using cross-lingual transformer models like XLM-RoBERTa and mBERT that support 100+ languages in a single model. For translation tasks, we integrate Google Cloud Translation, DeepL API, and open-source models like NLLB (No Language Left Behind). For domain-specific multilingual accuracy, we fine-tune models on your parallel text data across target languages. Our approach handles code-switching (text that mixes languages), dialectal variation, and non-Latin scripts including Chinese, Japanese, Korean, Arabic, and Hindi. We evaluate multilingual models per-language to identify accuracy gaps and apply targeted fine-tuning where needed. Our team in Latin America provides native Spanish and Portuguese QA capabilities alongside English.

• Q:

  What security measures does Azumo implement for NLP projects?

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  Azumo is SOC 2 certified and implements end-to-end encryption, role-based access controls, PII detection and redaction, and comprehensive audit logging for all NLP systems. Text data often contains sensitive information: names, account numbers, medical records, and legal details. We build PII detection pipelines that identify and mask sensitive entities before they reach model training or inference. For regulated industries, we implement HIPAA-compliant text processing, GDPR data minimization, and audit trails that log every document processed. We deploy NLP models within your private cloud or on-premises infrastructure when data cannot leave your environment. All models undergo bias evaluation to ensure equitable performance across demographic groups and language varieties.