Language Modeling
& LLM Solutions
Custom large language models that understand your domain, speak your language, and deliver answers your business can actually act on.
LLMs that know
your business
Generic LLMs are trained on the internet. Your business isn't on the internet. That's the gap YIME closes — through fine-tuning, RAG, and custom NLP systems that understand your domain, your documents, and your workflows.
We build production LLM systems that don't hallucinate on your most important queries, don't leak sensitive data, and don't require a PhD to operate. Just results.
What a domain-tuned
LLM
looks like
A generic LLM gives generic answers. A YIME-tuned LLM answers like your best domain expert — citing the right policies, using the right terminology, and knowing when to say it doesn't know.
- Zero hallucinations on grounded RAG queries
- Domain-specific terminology out of the box
- Cites source documents with page references
- Refuses gracefully when context is insufficient
Retrieval-Augmented
Generation:
grounding LLMs in your data
RAG eliminates hallucination by connecting the LLM to your documents at query time — not just at training time. The model only answers from what it can find.
RAG, fine-tuning, or
prompt
engineering?
Choosing the wrong LLM approach wastes months. YIME makes the right call upfront — matching your data, latency, and accuracy requirements to the best architecture.
| Requirement | RAG | Fine-tuning | Prompt Eng. | YIME Picks |
|---|---|---|---|---|
| Dynamic / frequently updated data | Best | Weak | Weak | RAG |
| Specific domain tone & style | OK | Best | Good | Fine-tune |
| Low hallucination on proprietary docs | Best | Good | Weak | RAG |
| Fast iteration, no training data | Good | Slow | Best | Prompt Eng. |
| Specialized task (classification, NER) | OK | Best | Weak | Fine-tune |
| Large document corpus Q&A | Best | Weak | Weak | RAG |
| Privacy / on-premise requirement | Good | Best | OK | Fine-tune + Self-host |
Everything we build
with language AI
RAG & Document Intelligence
Semantic search and Q&A over millions of internal documents — contracts, policies, manuals — with cited responses and sub-second retrieval.
LLM Fine-tuning
Domain-specific fine-tuning using LoRA, QLoRA, and SFT — on LLaMA, Mistral, and open-source models — for privacy-first, on-premise deployment.
Conversational AI & Chatbots
Context-aware multi-turn chatbots with intent classification, slot filling, and graceful handoff to human agents — deployed on web, mobile, or telephony.
Content Generation Pipelines
Automated copywriting, product descriptions, email campaigns, and long-form content generation with brand voice preservation and human review workflows.
Contract & Legal NLP
Clause extraction, risk flagging, obligation mapping, and executive summarization from legal documents — cutting review time by 60–70%.
Multilingual NLP
Cross-lingual understanding, translation-aware retrieval, and multilingual generation — serving global audiences from a single model deployment.
The tools we use to
build
smarter language AI
From use case to
production
LLM
Use Case Scoping & Architecture Decision
We define the target behavior, assess your data, and make the RAG vs fine-tune vs prompt engineering call — before writing a single line of code.
Data Preparation & Indexing
We clean, chunk, embed, and index your document corpus — engineering the retrieval pipeline for your specific query patterns and document structures.
Model Development & Evaluation
Iterative development with systematic evaluation on your domain-specific test set — measuring accuracy, hallucination rate, citation correctness, and latency.
Production Deployment & Monitoring
We deploy via secure API or on-premise — with output monitoring, query logging, and feedback loops for continuous improvement post-launch.
Ready to build an LLM that actually knows your business?
Tell us your use case, your documents, and your accuracy requirements. We'll scope the right approach within a week.
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