We build NLP systems that extract meaning from text data through entity recognition, classification, relationship extraction, and semantic analysis at production scale.
Natural language processing has been transformed by large language models. Tasks that previously required months of custom model training with thousands of labeled examples can now be accomplished through well-designed LLM prompts, often with better accuracy. Named entity recognition, text classification, relationship extraction, question answering, and summarization are all dramatically more accessible with modern AI.
However, LLMs are not the answer to every NLP problem. High-volume classification tasks may need dedicated models for cost efficiency. Low-latency applications may require smaller, specialized models. Highly domain-specific tasks may benefit from fine-tuned models trained on your data. The art of modern NLP is knowing when to use LLMs, when to use dedicated models, and how to combine both effectively.
Arthiq builds NLP solutions that select the right approach for each component of your system. We combine LLM capabilities with traditional NLP techniques, pre-trained transformers, and custom models to deliver systems that are accurate, efficient, and cost-effective for your specific requirements.
Extracting structured information from text is one of the most valuable NLP capabilities. Arthiq builds entity recognition and information extraction systems that identify people, organizations, locations, dates, monetary values, product names, and domain-specific entities within text data. These systems power applications from contract analysis to news monitoring to medical record processing.
We go beyond simple entity recognition to extract relationships between entities. Our systems can identify that a specific person is the CEO of a particular company, that a contract term applies to a specific service, or that a medical condition is treated with a particular medication. This relationship extraction creates structured knowledge from unstructured text that feeds into knowledge graphs, databases, and automated workflows.
For domain-specific entities that are not well-covered by general models, we fine-tune recognition models on your labeled data or use few-shot LLM approaches that learn from a small number of examples. Our active learning workflows prioritize the most valuable labeling tasks to build accurate models efficiently.
Classification is the workhorse of NLP, routing information to the right place and triggering the right actions. Arthiq builds classification systems that categorize support tickets by topic and urgency, sort documents by type and department, tag content by theme and audience, detect spam and abuse, and identify customer intent from messages.
Our classification systems support multi-label classification where a single text can belong to multiple categories, hierarchical classification with parent-child category relationships, and confidence-based routing where uncertain classifications are directed to human review. We calibrate classifier confidence scores so that a 90 percent confidence score genuinely means 90 percent accuracy, enabling reliable automation decisions based on confidence thresholds.
For applications with many categories that evolve over time, we implement classification architectures that can add new categories without retraining the entire model. LLM-based classifiers achieve this naturally through prompt updates, while we implement similar flexibility for dedicated models through modular training approaches.
NLP powers search systems that understand meaning rather than just matching keywords. Arthiq builds semantic search engines that find relevant documents, passages, and answers based on the intent behind a query. A search for "how to cancel my subscription" finds the relevant help article even if it uses different terminology like "end your plan" or "terminate service."
Our question answering systems go beyond retrieval to generate direct answers from your content. Users ask questions in natural language and receive precise answers with source citations. For internal knowledge bases, this means employees can find information in seconds rather than searching through dozens of documents.
We implement these capabilities using vector embeddings for semantic matching, re-ranking models for precision, and LLM-based answer generation for natural responses. The architecture is designed for real-time queries with sub-second response times, even across large document collections.
NLP is a broad field, and the right approach depends entirely on your specific use case, data, and requirements. Arthiq brings the expertise to evaluate options, select the best approach, and deliver production-quality NLP systems that solve real business problems.
Our team works across the full NLP stack from data preparation through model development, API deployment, and monitoring. We deliver in iterative phases with measurable quality improvements at each milestone.
Contact us at founders@arthiq.co to discuss how NLP can extract value from your text data and automate language-dependent processes.
Our team will design and deliver NLP systems that extract meaning, classify content, and answer questions from your text data with production-grade accuracy.