Multi-Model
Provider Integration
Production Scale
Vector Search
<100ms
Retrieval Latency
Production LLM Integration That Delivers ROI
Multi-Provider Expertise, GDPR Compliance, Cost Optimisation
Multi-provider integration: OpenAI, Claude, Gemini, or custom models with fallback strategies
Production RAG pipelines with sub-100ms retrieval and 95% accuracy on enterprise data
GDPR-compliant architecture with PII detection, data residency controls, and audit logging
Cost optimisation through hybrid prompt engineering and RAG, avoiding expensive fine-tuning
Infrastructure automation with Terraform and Ansible for repeatable, scalable LLM deployments
Monitoring and observability using Prometheus, Grafana, and application performance management tools
Enterprise reluctance to depend on single vendors drove Microsoft to adopt multi-model architecture combining OpenAI and Claude in November 2025. We build provider-agnostic abstractions using LangChain and LlamaIndex that let you switch between GPT-5, Claude Opus 4.5, Gemini 2.5 Pro, and DeepSeek-V3 without rewriting application logic. Your provider selection becomes a configuration change rather than an engineering project, protecting you from vendor lock-in and API pricing changes.
Multi-provider integration: OpenAI, Claude, Gemini, or custom models with fallback strategies
Production RAG pipelines with sub-100ms retrieval and 95% accuracy on enterprise data
GDPR-compliant architecture with PII detection, data residency controls, and audit logging
Cost optimisation through hybrid prompt engineering and RAG, avoiding expensive fine-tuning
Infrastructure automation with Terraform and Ansible for repeatable, scalable LLM deployments
Monitoring and observability using Prometheus, Grafana, and application performance management tools
Enterprise-Grade Technical Capabilities
Built for Regulated Industries and Production Scale
Provider-Agnostic Integration
Provider Agnostic
Integrate OpenAI GPT-5, Claude Sonnet 4, Gemini 2.5 Pro, or DeepSeek-V3 without vendor lock-in. LangChain and LlamaIndex handle orchestration and intelligent fallback when APIs fail.
Vector Database Architecture
10M+ Vectors
Pinecone, Qdrant, Weaviate, or Milvus with tuned indexing using NV-Embed-v2 or bge-base-en-v1.5. Get sub-millisecond queries across millions of vectors, built to handle production workloads.
RAG Pipeline Engineering
95% Accuracy
Retrieval-Augmented Generation systems that mix your proprietary knowledge with LLM reasoning. Semantic chunking, embedding selection, and retrieval tuning hit 95% accuracy.
Enterprise Security & GDPR
SOC2 Compliant
Microsoft Presidio PII detection, UK data residency, SOC 2 compliance, and full audit trails. Built for regulated industries with 44% citing data privacy as their top concern.
Strategic Fine-Tuning
Lower Costs
When RAG and prompt engineering hit their limits, we build custom fine-tuning for specialised domains. Parameter-efficient techniques substantially reduce training time and maintain accuracy.
Production Performance
<500ms P95
Sub-100ms embedding API calls with intelligent caching, token budgets, and exponential backoff retries. Even under enterprise query volumes, P95 latency stays under 500ms.
Intelligent Prompt Engineering
Days to Deploy
Optimise prompts using few-shot learning, chain-of-thought reasoning, and structured outputs. Get production results in days without the cost and complexity of fine-tuning.
Hybrid Deployment Options
Full Control
Choose between cloud APIs, self-hosted models, or hybrid approaches. AWS Bedrock, Azure OpenAI, or private infrastructure. Your data governance requirements drive the architecture.
Provider-Agnostic Integration
Provider Agnostic
Integrate OpenAI GPT-5, Claude Sonnet 4, Gemini 2.5 Pro, or DeepSeek-V3 without vendor lock-in. LangChain and LlamaIndex handle orchestration and intelligent fallback when APIs fail.
Vector Database Architecture
10M+ Vectors
Pinecone, Qdrant, Weaviate, or Milvus with tuned indexing using NV-Embed-v2 or bge-base-en-v1.5. Get sub-millisecond queries across millions of vectors, built to handle production workloads.
RAG Pipeline Engineering
95% Accuracy
Retrieval-Augmented Generation systems that mix your proprietary knowledge with LLM reasoning. Semantic chunking, embedding selection, and retrieval tuning hit 95% accuracy.
Enterprise Security & GDPR
SOC2 Compliant
Microsoft Presidio PII detection, UK data residency, SOC 2 compliance, and full audit trails. Built for regulated industries with 44% citing data privacy as their top concern.
Strategic Fine-Tuning
Lower Costs
When RAG and prompt engineering hit their limits, we build custom fine-tuning for specialised domains. Parameter-efficient techniques substantially reduce training time and maintain accuracy.
Production Performance
<500ms P95
Sub-100ms embedding API calls with intelligent caching, token budgets, and exponential backoff retries. Even under enterprise query volumes, P95 latency stays under 500ms.
Intelligent Prompt Engineering
Days to Deploy
Optimise prompts using few-shot learning, chain-of-thought reasoning, and structured outputs. Get production results in days without the cost and complexity of fine-tuning.
Hybrid Deployment Options
Full Control
Choose between cloud APIs, self-hosted models, or hybrid approaches. AWS Bedrock, Azure OpenAI, or private infrastructure. Your data governance requirements drive the architecture.
Cost-Optimised Integration Methodology
Start Simple, Scale Strategically
Discovery & Use Case Mapping
We analyse your workflows, define accuracy targets and cost constraints, then map existing processes to LLM capabilities. Quick wins get identified before a single line of code is written.
Architecture & Provider Selection
We design provider-agnostic abstractions with LangChain or LlamaIndex. You get optimal models, embedding strategies, and vector databases. Compliance, monitoring, and scaling are planned from the start.
Phased Implementation & Optimisation
Start with prompt engineering in days. Move to RAG pipelines over weeks. Fine-tuning comes last, only when needed for specialist work. Each phase proves its value before you commit further. In production, we watch accuracy, latency, token costs, and errors whilst A/B testing prompts, tuning retrieval parameters, and adjusting token budgets based on real data.
Discovery & Use Case Mapping
We analyse your workflows, define accuracy targets and cost constraints, then map existing processes to LLM capabilities. Quick wins get identified before a single line of code is written.
Architecture & Provider Selection
We design provider-agnostic abstractions with LangChain or LlamaIndex. You get optimal models, embedding strategies, and vector databases. Compliance, monitoring, and scaling are planned from the start.
Phased Implementation & Optimisation
Start with prompt engineering in days. Move to RAG pipelines over weeks. Fine-tuning comes last, only when needed for specialist work. Each phase proves its value before you commit further. In production, we watch accuracy, latency, token costs, and errors whilst A/B testing prompts, tuning retrieval parameters, and adjusting token budgets based on real data.
Research-Backed Business Impact
Productivity Gains from Enterprise Deployments
Semantic Knowledge Search
Better Results
Search your knowledge base by meaning, not keywords. RAG retrieval delivers superior accuracy and relevance. That saves approximately £31,754 per employee annually in search time alone.
Intelligent Customer Support
CSAT Improvement
Handle a significant portion of support tickets automatically with answers drawn from your documentation. Customer satisfaction improves substantially. Costs drop. Escalations come with full conversation history ready to go.
Document Processing & Legal
Faster Processing
Summarise contracts 5x faster with 91% risk detection accuracy. Pull structured data from messy sources instantly. Your legal team saves 80% of manual review time.
Productivity That Scales
40% Productivity
EY saw 40% productivity gains across 400,000 employees using private LLM deployment. McKinsey estimates £200-340 billion annual value in banking alone from LLM-driven improvements.
Semantic Knowledge Search
Better Results
Search your knowledge base by meaning, not keywords. RAG retrieval delivers superior accuracy and relevance. That saves approximately £31,754 per employee annually in search time alone.
Intelligent Customer Support
CSAT Improvement
Handle a significant portion of support tickets automatically with answers drawn from your documentation. Customer satisfaction improves substantially. Costs drop. Escalations come with full conversation history ready to go.
Document Processing & Legal
Faster Processing
Summarise contracts 5x faster with 91% risk detection accuracy. Pull structured data from messy sources instantly. Your legal team saves 80% of manual review time.
Productivity That Scales
40% Productivity
EY saw 40% productivity gains across 400,000 employees using private LLM deployment. McKinsey estimates £200-340 billion annual value in banking alone from LLM-driven improvements.
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