Custom LLM and SLM architectures, fine-tuning strategies, and RAG implementations. From GPT to Claude to domain-specific small language models.
From selecting the right foundation model to deploying fine-tuned domain specialists — we make LLMs work for your business.
Retrieval-Augmented Generation pipelines that ground LLM responses in your proprietary data — eliminating hallucinations and ensuring accuracy.
Domain-specific models trained on your data using PEFT, LoRA, and QLoRA — achieving expert-level performance at a fraction of the cost of full fine-tuning.
Compact, fast SLMs that run on-premise or at the edge — perfect for latency-sensitive or data-sovereignty use cases.
API design, prompt engineering, evaluation harnesses, and cost optimization for production LLM deployments.
Benchmark your use case against GPT-4, Claude, Llama, Mistral, and specialized SLMs. We pick on quality, latency, and cost — not hype.
Clean, chunk, embed, and index your corpus. We build the retrieval layer that makes RAG reliable — hybrid search, reranking, and citation tracking.
Systematic prompt engineering, RLHF, and automated eval suites. We measure what matters — accuracy, faithfulness, and user satisfaction.
Scalable inference with caching, fallbacks, and spend alerts. Your LLM stack runs lean and observable from day one.
Our team built NLP systems long before transformers. That foundation means we understand when an LLM is the right tool — and when a simpler approach wins.
We integrate OpenAI, Anthropic, Meta, Google, and open-source models. Your architecture stays portable and cost-optimized.
RAG systems processing millions of documents daily for financial services and consulting firms. Battle-tested, not demo-ware.
Tell us about your project
or email directly: fernandrez@iseeci.com