Research
Applied AI research and industry insights
We publish research to help people and businesses make informed decisions about AI. From practical architectures and production methodologies to industry analysis and emerging trends — grounded in real experience, not hype.
AI Reliability
Hallucination prevention, structured outputs, and validation architectures for high-stakes domains.
Evaluation & QA
Measuring and maintaining AI output quality over time — golden datasets, regression testing, and red-teaming.
Domain-Specific AI
Building AI systems that respect the nuances of specific industries — maritime, education, e-commerce, and beyond.
In Preparation
Preventing LLM Hallucinations in Domain-Critical Applications: A JSON-First Architecture Approach
We present a practical architecture pattern for eliminating LLM hallucinations in procurement and financial systems. By constraining AI outputs to structured JSON validated against real catalogue data, we achieved zero-error rates on critical fields across 500+ maritime catalogue items.
Evaluating Multi-Model LLM Pipelines for Enterprise Research: Methodology and Results
A practical evaluation framework for multi-model LLM orchestration in enterprise research intelligence. We document how cross-model verification between ChatGPT, Perplexity, and Grok improved factual reliability by 30%.
Culturally Contextual AI Image Generation at Scale: Lessons from Educational Content Production
How we built a 5-tier prompt engineering system that generates culturally appropriate educational images for African language learning at scale.
From Fragmented to Unified: Replacing Multi-Tool E-Commerce Workflows with AI
E-commerce sellers use 4-6 separate tools to create product content. We analyse the workflow fragmentation problem and present an architecture for a unified AI platform.
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