Fine-tuning, RAG, private deployment, LLM-powered products — end-to-end LLM engineering on GPT-4o, Claude 3.5, Llama 3, and Mistral. Delivered in 4–8 weeks.
Everything from model selection to production deployment — under one roof
Fine-tune GPT-4, Llama 3, and Mistral on your domain data for superior accuracy and 80% lower inference costs
Connect any LLM to your private knowledge base for accurate, grounded responses with source citations
Deploy open-source models (Llama, Mistral, Phi) inside your own cloud — zero data leakage, full compliance
Integrate GPT-4o, Claude, Gemini, or any LLM into your product with robust, production-grade wrappers
Systematic prompt design and automated evaluation pipelines to maximise accuracy and minimise cost
End-to-end AI product development — from idea to production SaaS powered by the latest LLMs
We're model-agnostic — we pick the best LLM for your use case, budget, and compliance requirements
Complex reasoning, tool calling, vision
Long context, analysis, safety-critical apps
Multimodal, large context windows
Private deployment, fine-tuning, cost-sensitive
Fast inference, European data residency
Edge deployment, low-resource environments
Contract review taking senior lawyers 4–6 hours per document, limiting throughput
Fine-tuned Llama 3 model trained on 10,000 contracts — highlights risk clauses and deviations from standard terms
Review time: 4 hours → 12 minutes. 98% clause detection accuracy.
Clinical notes unstructured and inconsistent, making data analysis impossible
Custom RAG system with fine-tuned medical NLP model extracting structured data from free-text notes
40% improvement in early risk detection. HIPAA compliant, deployed on private AWS.
Product catalogue of 500K+ items with inconsistent, low-quality descriptions hurting conversion
Batch LLM pipeline generating SEO-optimised descriptions at scale, with brand voice fine-tuning
10x faster catalogue updates. 18% increase in organic product page traffic.
Choose the right model, hosting strategy, and architecture based on your accuracy, cost, and compliance requirements
Clean, format, and augment your training or retrieval data — the most critical step for performance
Fine-tuning, RAG pipeline construction, or product integration — with continuous evaluation throughout
Production deployment with latency monitoring, cost tracking, and model drift detection
From a single-model integration to a full fine-tuned deployment — we scope it, build it, and ship it.