Cognitive Intelligence
Technical
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Neural Assets
NLP Systems.
Advanced NLP solutions including LLM fine-tuning, entity extraction, and multilingual processing for enterprise use cases.
TransformersLLMBERTRAG
System Ready
Technical
Capabilities.
Large Language Model fine-tuning with LoRA and QLoRA
Retrieval-Augmented Generation (RAG) pipeline implementation
Named Entity Recognition and relationship extraction
Multilingual text processing across 50+ languages
Sentiment analysis and document classification systems
Conversational AI with context management and intent classification
Semantic Understanding
Language is complex and full of nuance — our NLP solutions leverage the latest transformer architectures for true contextual understanding.
- Custom RAG (Retrieval-Augmented Generation) systems
- Multilingual support and cross-lingual embeddings
- Domain-specific language model fine-tuning and adaptation
Core Focus
Transformers, LLM, BERT — delivering measurable impact through deep technical expertise.
Engagement Model
From discrete consulting engagements to full turnkey delivery, we adapt to your program's specific needs and timeline.
Deep Tech FAQ
Do you fine-tune large language models?+
Yes. We fine-tune LLMs using LoRA/QLoRA for domain adaptation, with custom RAG pipelines that connect models to your proprietary knowledge bases and APIs.
What languages do you support for NLP?+
We build multilingual NLP systems supporting 50+ languages, with cross-lingual embeddings that maintain accuracy across language boundaries without separate models per language.
ChipTalk.AI Advantage
ChipTalk's NLP practice combines deep expertise in transformer architectures with practical RAG pipeline engineering. We fine-tune and deploy LLMs for domain-specific applications—technical support, document intelligence, code generation—with rigorous hallucination testing and output safety controls.
Related Engagements
01
Technical Documentation QA Bot
Fine-tuned a Mistral 7B model with LoRA on 50K pages of engineering documentation, deploying a RAG-based QA assistant that reduced engineer search time by 60%.
02
Multilingual Customer Support Classifier
Developed a multilingual intent classification and sentiment analysis pipeline supporting 25 languages for a global telecom equipment manufacturer, achieving 92% intent accuracy across all locales.
We measure NLP quality beyond accuracy—our evaluation frameworks include faithfulness scoring (for RAG), toxicity screening, and adversarial testing to ensure your LLM-powered features are trustworthy enough for production.
Relevant Tags:TransformersLLMBERTRAG