"Data curation across scientific research and development (R&D) environments is a critical process for ensuring results from in-house experiments and external publications are effectively structured & integrated for further reuse, reinterpretation & reference. It supports a number of broad downstream activities associated with big data analytics, such as compound selection in drug discovery informatics or process optimisations in advanced materials manufacturing. These high value activities attract a broad range of competitive algorithmic software solutions (e.g. Reaxsys, Ingenuity Pathway Analysis, MaterialUniverse, EBI, IBM Watson) - all geared towards maximising existing content & are often reliant on data curation to produce it. Current methods of scientific data curation are wholly insufficient: up to 85% of scientific research is potentially wasted and 90% of scientists believe there is a reproducibility crisis (Munafo _et al_, 2017) - a global issue costing billions. This is largely down to the complexity of the task requiring experts to manually assess text & data - too slow and costly for scaling. Modern Artificial Intelligence (AI) approaches are still too inaccurate; nor do they address sparse-data completion - a common problem when integrating experimental data. Here we describe Chemeia ('Chem-ee-a'), an entirely new solution to general data curation that combines two state-of-the-art AI-centric technologies for turning static, pre built, experimental databases into more complete, reliable, and continuously monitored resources for use in these high-value R&D environments. Biorelate and Intellegens are two high-technology companies specialising in applying novel AI techniques to solve well known problems in optimising data curation for R&D. Biorelate have developed Galactic AI, an innovative platform unifying data mining, natural language processing & deep learning for curating textual data. It has been used to deliver for large pharmaceutical and biotech companies focusing on early stage drug discovery. Intellegens have developed Alchemite, which builds on existing numerical curated resources (sparse data) by using AI to predict ranked unknown data-points and uncertainties. No other solution on the market can replicate Alchemite's low cost modelling, prediction, error detection & parameter optimisation for big, sparse, numerical data. Earlier this year, both companies independently improved the volume and quality of content in a customers database and realised that each approach was limited by its ability to validate new results & predictions. Combining these two technologies so that they function synergistically, combining strengths & solving validation issues will result in a system that is capable of generating more detailed and accurate data."