AI & Automation
Generative AI built
for production.
Generative AI is genuinely transformative — but the gap between a convincing demo and a reliable product is wide. GreaterHeight builds LLM-powered systems that are accurate, observable, and maintainable: the engineering disciplines that matter when AI becomes load-bearing.
Capabilities
What we build with generative AI
RAG Pipelines
Retrieval-Augmented Generation systems that ground LLM responses in your organisation's own documents, databases, and knowledge bases — reducing hallucination and making AI outputs verifiable.
LLM Integration & Orchestration
API integration with leading LLM providers (OpenAI, Anthropic, Google, Mistral) and open-source models, with prompt engineering, chaining, and output validation built in.
Fine-Tuning
Custom model fine-tuning for domain-specific tasks where general-purpose LLMs underperform — using your own data to produce models that know your terminology, format, and standards.
AI-Powered Applications
Full-stack AI products — chatbots, document summarisers, intelligent search, code assistants, content generators — built with proper engineering discipline around the LLM core.
Evaluation & Observability
LLM evaluation frameworks, response quality metrics, prompt regression testing, and production monitoring — so you know when your AI system is performing and when it is not.
Multi-Modal AI
Systems that combine text, image, audio, and structured data — document processing pipelines, voice-to-insight tools, and visual understanding applications.
Our Engineering Approach
From prototype to production-grade system
Generative AI projects that stall at proof-of-concept usually do so because of evaluation, observability, and cost — not model capability. We engineer for all three from the start.
Building something with generative AI?
We can help you move from concept to production — with the engineering rigour generative AI systems require.