ChatGPT & LLM Development
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LLMs Integrated into Products That Ship
ChatGPT wrappers are easy to build. Products where LLMs reliably do useful work at production scale are not. CimpleO integrates large language models — GPT-4, Claude, LLaMA, and Mistral — into your applications in ways that are accurate, safe, and cost-controlled. From customer support automation to internal knowledge retrieval to document processing, we build LLM features that earn their place in the product.
Custom Chatbot & Assistant Development
AI assistants scoped to your domain, grounded in your data, and controlled with the guardrails your use case requires. We implement RAG (Retrieval-Augmented Generation) pipelines that connect language models to your knowledge base — product documentation, support history, internal policies — so answers are accurate, not hallucinated.Document Processing & Extraction
Contracts, invoices, reports, and forms processed at scale. We build LLM pipelines for structured data extraction from unstructured documents — pulling specific fields, classifying content, summarising long documents, and flagging anomalies. Integrated into your existing document workflows, not a standalone tool.LLM API Integration
Embedding LLM capability into existing applications via OpenAI, Anthropic, or open-source model APIs. We handle prompt engineering, context window management, streaming responses, token cost optimisation, and fallback strategies. Your users get fast, coherent AI features without the infrastructure complexity.Fine-Tuning & Custom Models
When a general-purpose model doesn’t perform well enough on your domain-specific tasks, we fine-tune. Custom datasets, training pipelines, and evaluation frameworks. We also evaluate whether fine-tuning is actually needed — sometimes better prompting and RAG architecture gives you 90% of the way there without the overhead.How We Build LLM Features That Work in Production
- Evaluation before deployment — we measure accuracy, hallucination rate, and latency before features go live
- Cost control — token usage optimisation, caching strategies, and model selection that keeps costs predictable
- Privacy options — on-premises LLaMA/Mistral deployment for sensitive data that can’t leave your infrastructure
- Observability — logging of inputs, outputs, and latencies so you can improve the system with real data
Tell us what you want to build with LLMs — we’ll tell you whether it’s a good fit and what the realistic scope looks like.