Custom LLM & NLP for R&D
Generic LLMs can't understand your domain. We fine-tune and deploy custom NLP models on your proprietary data, unlocking insights from millions of specialized documents.
Is This Your Challenge?
Your organization has accumulated decades of specialized knowledge in technical documents, research papers, patents, and internal reports. But this knowledge is locked away, inaccessible at the scale and speed your R&D teams need.
Common Challenges:
- Information Overload: Thousands of papers published monthly, impossible to manually review all relevant research
- Generic LLMs Fail: ChatGPT doesn't understand your specialized terminology, abbreviations, or domain-specific relationships
- Proprietary Knowledge: Your most valuable data can't be sent to public APIs due to confidentiality
- Accuracy Matters: Medical, pharmaceutical, or legal domains require high precision—hallucinations are unacceptable
Our Solution: Domain-Specific Language Models
INM Consulting fine-tunes and deploys custom NLP models on your proprietary data, creating AI systems that truly understand your domain and can extract insights at scale.
Data Preparation & Annotation
We work with your domain experts to prepare and annotate training data, identifying key entities, relationships, and domain-specific patterns that generic models miss.
Model Fine-tuning
We fine-tune state-of-the-art transformer models (BERT, GPT, BioBERT) on your data, teaching them your terminology, abbreviations, and domain-specific knowledge. We can also train custom models from scratch if needed.
Deployment & Integration
We deploy models as secure, scalable APIs behind your firewall, integrated with your document management systems and research workflows. Your data never leaves your infrastructure.
NLP Capabilities
🎯 Named Entity Recognition
Automatically identify and extract entities specific to your domain: drugs, proteins, diseases, compounds, processes, equipment.
🔗 Relationship Extraction
Discover and map relationships between entities: drug-disease associations, protein interactions, causal relationships.
📊 Knowledge Graph Construction
Build structured knowledge graphs from unstructured text, enabling complex queries and reasoning across your entire document corpus.
🔍 Semantic Search
Search by meaning, not keywords. Find conceptually similar documents even when they use different terminology.
📝 Automated Summarization
Generate accurate, domain-aware summaries of technical documents, research papers, and reports.
🚨 Real-time Monitoring
Continuously monitor new publications and internal documents, alerting teams to relevant findings.
Real-World Applications
💊 Drug Discovery
Challenge: 10K+ papers published monthly, impossible to review manually
Solution: Custom NLP pipeline extracts drug-disease relationships, identifies potential targets, builds knowledge graphs
Impact: 70% reduction in review time, 95% accuracy
⚖️ Patent Analysis
Challenge: Analyzing competitor patents and prior art
Solution: Custom models trained on patent language, identifying similar technologies and freedom-to-operate risks
Impact: 60% faster patent analysis
📖 Literature Screening
Challenge: Systematic reviews require reading thousands of papers
Solution: AI-powered screening and categorization with domain-specific relevance scoring
Impact: 80% time savings, consistent quality
Technical Architecture
Foundation Models
- BioBERT, SciBERT
- GPT-4, Claude
- Domain-specific BERT variants
- Custom transformer architectures
NLP Frameworks
- spaCy, Hugging Face
- LangChain for LLM apps
- Custom training pipelines
- Active learning systems
Infrastructure
- GPU clusters for training
- Vector databases (Pinecone, Weaviate)
- Neo4j for knowledge graphs
- Private cloud deployment
Featured Project: Pharmaceutical R&D
Industry: Pharmaceuticals
Challenge: Analyzing 500K patient genetic records (terabytes) and extracting associations from 20 million biomedical articles.
Solution: We built an end-to-end ML workflow for genetic data analysis with dimensionality reduction, clinical integration, and interactive dashboards in Spotfire. Developed NLP models for text classification on the 20M article corpus. Deployed web apps using Streamlit and REST APIs using FastAPI.
Results: Terabytes of genetic data processed, 500K patients analyzed, 20M articles mined, interactive dashboards and APIs delivered.
Read Full Case Study →Ready to Unlock Your Technical Knowledge?
Let's discuss how custom NLP can accelerate your R&D and give you a competitive edge.