LLM integration services
LLM integration services
for enterprise applications
Integrate OpenAI, Azure OpenAI, or Claude into your existing applications — with retrieval-augmented generation (RAG) grounded in your own data, not generic internet knowledge.
Multi-LLM
Vendor-agnostic architecture by default
RAG
Grounded in your own data, not guesswork
Cost-optimised
Token usage and latency tuning included
Platforms we integrate:
How RAG works
Grounding LLMs in your own data
Retrieval-augmented generation (RAG) is how we make an LLM answer accurately about your business — not just what it learned during training.
01
User question
"What's our refund policy for enterprise clients?"
02
Vector search
Finds the most relevant chunks from your knowledge base
03
LLM + context
Model generates an answer grounded in retrieved facts
04
Accurate answer
Response cites your actual policy, not a hallucination
Choosing the right approach
RAG, fine-tuning, or prompt engineering?
Most enterprise use cases don't need fine-tuning. We help you pick the right (and most cost-effective) technique.
RAG
Retrieves relevant data at query time and feeds it to the model as context. No model retraining required — update your knowledge base and answers update instantly.
Best for: knowledge bases, support, internal Q&A
Prompt engineering
Carefully structured instructions and examples guide model behaviour without any retrieval or retraining. Fast and cheap to iterate.
Best for: well-defined tasks, formatting, tone control
Fine-tuning
Retrains the model on your specific examples to change its underlying behaviour or style. More expensive and slower to iterate than RAG.
Best for: highly specialised tone, format, or domain language
Implementation approach
How we deliver your LLM integration
01
Use case & data assessment (1–2 weeks)
We assess your use case, data sources, and quality — and recommend the right approach (RAG, prompt engineering, or fine-tuning) along with model and vendor selection.
02
Architecture & pipeline build (2–4 weeks)
We build the data ingestion pipeline, vector store, retrieval logic, and prompt templates — with evaluation criteria defined before a single query goes live.
03
Integration & testing (2–3 weeks)
04
Launch & continuous improvement
Production launch with monitoring of answer quality, cost per query, and user feedback — feeding into ongoing prompt and retrieval refinement.
FAQ's
LLM integration questions
Without grounding, yes — this is a known limitation of LLMs. That’s exactly why we build RAG architectures by default for enterprise use cases: the model answers based on retrieved facts from your actual data, dramatically reducing hallucination. We also build citation and confidence-scoring into responses so users can verify answers.
If we build a vendor-agnostic architecture (which we recommend by default), yes. We abstract the model layer so switching from OpenAI to Azure OpenAI or Claude is a configuration change, not a rebuild. Some advanced features are provider-specific, which we’ll flag during design if relevant to your use case.
Running costs depend heavily on query volume and model choice — typically ranging from a few hundred dollars a month for low-volume internal tools to several thousand for high-volume customer-facing applications. We include cost projections and optimisation recommendations as part of every engagement.
We use enterprise agreements with model providers (OpenAI Enterprise, Azure OpenAI, Anthropic) that contractually exclude your data from being used in model training. This is a standard part of how we architect every integration.
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