LLM Integration Services

Enterprise-grade Large Language Model integration with GPT-4, Claude, and open-source models. Secure, scalable, production-ready AI implementations for UK and European businesses.

Enterprise LLM Integration Expertise

Large Language Models have transformed how businesses operate, but successful integration requires deep expertise in AI architecture, prompt engineering, and production systems. At Codixera, we help organisations across the UK and Europe harness the full potential of LLMs while managing costs, ensuring security, and maintaining reliability.

Our team has integrated LLMs into dozens of production applications, from customer service chatbots handling millions of queries to document processing systems that save thousands of hours annually. We understand the nuances of different models and can guide you to the right choice for your specific use case.

Our LLM Integration Capabilities

  • GPT-4 & GPT-4 Turbo Integration — Full OpenAI API implementation with function calling, vision capabilities, and streaming responses
  • Claude Integration — Anthropic's Claude 3 family including Opus, Sonnet, and Haiku for different use cases
  • Open-Source Models — Llama 2, Mistral, and other models for on-premises deployment and cost optimization
  • RAG Implementation — Retrieval-Augmented Generation with vector databases for accurate, grounded responses
  • Prompt Engineering — Systematic prompt development, testing, and optimization for consistent outputs
  • Fine-tuning — Custom model training on your data for domain-specific performance
50+ LLM Projects Delivered
99.9% API Uptime Achieved
60% Average Cost Reduction

LLM Applications We Build

From conversational AI to complex document processing, we deliver production-ready LLM solutions.

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Intelligent Chatbots & Virtual Assistants

Customer service automation with context-aware conversations, multi-turn dialogue management, and seamless handoff to human agents. Our chatbots handle 80% of queries without human intervention.

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Document Processing & Analysis

Automated extraction of key information from contracts, reports, and forms. Summarization of lengthy documents, compliance checking, and intelligent categorization at scale.

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Content Generation & Automation

Marketing copy, product descriptions, email campaigns, and technical documentation generated with your brand voice. Human-in-the-loop workflows for quality control.

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Semantic Search & Knowledge Base

Natural language search over your internal documentation, support tickets, and knowledge bases. Find relevant information instantly without exact keyword matching.

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Process Automation

Intelligent workflow automation that understands context. Email triage, data entry, report generation, and complex decision support powered by LLMs.

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Translation & Localisation

High-quality machine translation with context awareness and domain-specific terminology. Support for 100+ languages with consistent brand voice.

How We Implement LLMs

01

Discovery & Assessment

We analyse your use case, data, and requirements to determine the optimal LLM approach. This includes model selection, architecture design, and cost projections.

02

Proof of Concept

Rapid prototype development to validate the approach with real data. We test multiple models and architectures to find the best fit for your needs.

03

Production Development

Full implementation with enterprise-grade infrastructure, monitoring, and safety measures. Includes prompt versioning, A/B testing, and performance optimization.

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Deployment & Iteration

Staged rollout with continuous monitoring. We track quality metrics, costs, and user feedback to continuously improve the system.

Technical Excellence in LLM Integration

Our engineers have deep expertise in the technical challenges of LLM deployment. We build systems that are not just functional, but production-ready and cost-effective.

Architecture Patterns We Implement

  • RAG (Retrieval-Augmented Generation) — Combining LLMs with vector databases for accurate, grounded responses with citations
  • Function Calling — Structured outputs and tool use for integration with your existing systems
  • Agent Architectures — Multi-step reasoning and autonomous task completion
  • Streaming Responses — Real-time output for better user experience
  • Fallback Systems — Graceful degradation when primary models are unavailable
  • Caching Strategies — Intelligent response caching to reduce costs by up to 60%

Safety & Compliance

We implement comprehensive safety measures to protect your business and users from LLM-related risks.

  • Content Moderation — Filter harmful, inappropriate, or off-topic outputs
  • PII Protection — Prevent exposure of sensitive personal data
  • Prompt Injection Defence — Protect against adversarial inputs
  • Output Validation — Ensure responses meet business rules and constraints
  • Audit Logging — Complete traceability of all LLM interactions
  • GDPR Compliance — Data handling that meets UK and EU regulations

Frequently Asked Questions

Which LLM should I use for my project?

The choice depends on your specific requirements. GPT-4 excels at complex reasoning and instruction following. Claude 3 is excellent for longer contexts and safety-sensitive applications. Open-source models like Llama 2 are ideal for on-premises deployment and cost-sensitive use cases. We help you evaluate and select the right model based on accuracy, cost, latency, and compliance requirements.

How much does LLM integration cost?

Costs vary significantly based on usage volume, model choice, and complexity. A typical enterprise chatbot might cost £500-2,000/month in API fees, while high-volume document processing could run £5,000-20,000/month. We optimize architectures to minimize costs through caching, model selection, and efficient prompt design—often reducing costs by 40-60% compared to naive implementations.

Can I use LLMs with sensitive business data?

Yes, with proper precautions. OpenAI and Anthropic offer enterprise agreements with data privacy protections. For highly sensitive data, we can deploy open-source models on your own infrastructure. We implement additional safeguards like PII detection, data anonymization, and secure data handling to ensure compliance with GDPR and industry regulations.

How long does LLM integration take?

A proof-of-concept can be delivered in 2-4 weeks. Production implementation typically takes 2-4 months depending on complexity, integration requirements, and safety needs. We follow an iterative approach, delivering value quickly while building toward a robust long-term solution.

Ready to Integrate LLMs Into Your Product?

Let's discuss how Large Language Models can transform your business processes and customer experience. Our AI experts will help you identify the best approach for your specific needs.