LLM Integration
Reading time: 2 minutes.
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.
Frequently Asked Questions
Should I use GPT-4 API or fine-tune my own model?
For most business use cases: GPT-4 or Claude API with a good RAG architecture. Fine-tuning is expensive, slow to iterate, and usually unnecessary when the alternative is better prompting and retrieval. We recommend fine-tuning only when your domain is highly specialised (medical, legal jargon), API inference cost is genuinely prohibitive at scale, or data privacy prevents external API calls.
How do you prevent the AI from making things up?
RAG — the model answers from your retrieved documents, not from its training data. Structured output schemas that constrain what the model can return. Confidence scoring and fallback flows for low-confidence responses. We evaluate hallucination rate systematically before going live, not just demo it and hope.
How much does a ChatGPT integration cost?
A simple LLM integration (one endpoint, one use case, basic prompt engineering): $8,000–$20,000. A RAG system with knowledge base ingestion, retrieval pipeline, and UI: $25,000–$60,000. A full AI feature with evaluation framework, monitoring, and ongoing model maintenance: $60,000+. Ongoing API costs depend on usage volume.
Can you integrate LLMs with our existing CRM or knowledge base?
Yes. We connect LLMs to your existing data sources — Notion, Confluence, SharePoint, your database, PDF document libraries, or Zendesk ticket history. We build the ingestion pipeline, chunking strategy, embedding model, and vector store so the LLM answers from your actual data.
What if we need to keep our data on our own servers?
We deploy open-source models on your infrastructure — LLaMA 3, Mistral, Phi-3, or Qwen depending on task requirements and hardware constraints. Quality is benchmarked against your specific use case before committing.