Research
Applied AI research from real systems
We publish insights and methodologies drawn directly from our production AI systems. Every piece of research is grounded in real-world deployment — not theory alone.
Research Programme Launching Q2 2026
Our first publications are in preparation. Each paper documents architectures, methodologies, and results from production AI systems we have built and operated. Based on real data, real deployments, and real outcomes.
Upcoming
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.
Our approach to research
Every paper we publish is based on a system we have built, deployed, and operated in production. We do not write about theoretical architectures or benchmark-only results.
Our research focuses on three themes: AI reliability and hallucination prevention (how to make AI outputs trustworthy in high-stakes domains), evaluation and quality assurance (how to measure and maintain AI output quality over time), and domain-specific AI at scale (how to build AI systems that respect the nuances of specific industries and cultures).
We believe the most valuable AI research right now comes from practitioners who are building and shipping production systems — not from labs alone.
Interested in collaborating on research?
We welcome collaboration with academic institutions, industry partners, and fellow practitioners on applied AI research.
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