LLM Integration Services
We connect large language models — OpenAI, Anthropic Claude, Google Gemini, and open-source alternatives — into your existing products and internal systems, with custom prompt engineering, retrieval-augmented generation, and production-grade monitoring.
What Is LLM Integration?
Large language models are powerful, but the gap between a working demo and a production integration is significant. Getting an LLM to respond well in isolation is different from getting it to respond correctly, consistently, and safely inside your actual product — with your data, your users, and your business rules in play.
We handle the engineering work that sits between "the model works" and "this is live in production": prompt design, context management, retrieval pipelines, tool use, output validation, latency optimisation, and ongoing evaluation.
We work with B2B companies across the USA, UK, Canada, and Australia — whether you're building an AI feature into an existing product or replacing an internal process with an LLM-powered system.
What We Deliver
Prompt Engineering & System Design
We design prompt architectures that produce consistent, accurate outputs at scale — not just in the playground, but under production conditions with real user inputs.
Retrieval-Augmented Generation (RAG)
Connect LLMs to your knowledge base, documentation, or internal data. We build retrieval pipelines that surface the right context so the model gives accurate, grounded answers.
Tool Use & Function Calling
Enable LLMs to take actions: query databases, call APIs, update records, and interact with your systems — going beyond text generation into real operational capability.
Model Selection & Cost Optimisation
We select the right model for your use case — balancing capability, latency, and cost. Not every task requires the most expensive model, and we design accordingly.
Output Validation & Safety
Production LLM integrations need guardrails. We implement output validation, content filtering, and fallback logic so your system handles edge cases without exposing users to errors.
Evaluation & Monitoring
We set up evaluation frameworks to measure response quality over time, along with production monitoring to detect regressions, latency spikes, or unexpected behaviour.
Built In-House: CallMigo
CallMigo is an AI-powered voice automation platform we built internally. At its core, it's an LLM integration problem: a language model must understand a caller's responses in real time, decide what to say next, and take appropriate actions — all within the latency constraints of a live phone call.
Building it required solving prompt reliability under variable inputs, integrating LLM output with telephony and CRM systems, and implementing evaluation pipelines to track conversation quality at scale.
The same engineering rigour we applied to CallMigo is what we bring to every LLM integration engagement.
Models & Frameworks We Work With
We're model-agnostic. We select based on your use case, data privacy requirements, and infrastructure constraints.
Frequently Asked Questions
Do I need to fine-tune a model for my use case?
Usually not. Fine-tuning is expensive, slow to iterate, and rarely the right first step. In most cases, well-designed prompts combined with retrieval (RAG) outperform fine-tuned models at a fraction of the cost. We evaluate your use case before recommending fine-tuning.
Which LLM should I use?
It depends on your requirements: response quality, latency, cost, data privacy, and whether you can send data to a third-party API. We'll walk you through the trade-offs for your specific use case and recommend accordingly.
What is RAG and do I need it?
Retrieval-Augmented Generation connects an LLM to your own data — documents, knowledge base, database records — so it can answer questions grounded in your information rather than just its training data. If your use case involves answering questions about your products, policies, or internal knowledge, you likely need it.
How do you handle data security?
We design integrations with data minimisation in mind — only sending what the model needs to complete the task. Where data privacy is critical, we can integrate open-source models running on your own infrastructure so no data leaves your environment.
Do you work with companies in the UK, Canada, and Australia?
Yes. We work with B2B companies across the USA, UK, Canada, and Australia. All work is conducted in English, async-first, and structured to work across time zones.
Ready to Integrate AI Into Your Product?
Book a free consultation and we'll review your use case, walk through the right approach, and outline what a practical LLM integration looks like for your system.