  High accuracy localisation technologies exist but are prohibitively expensive
to deploy for large indoor spaces such as warehouses, factories, and
supermarkets to track assets and people. However, these technologies can be
used to lend their highly accurate localisation capabilities to low-cost,
commodity, and less-accurate technologies. In this paper, we bridge this link
by proposing a technology-agnostic calibration framework based on artificial
intelligence to assist such low-cost technologies through highly accurate
localisation systems. A single-layer neural network is used to calibrate less
accurate technology using more accurate one such as BLE using UWB and UWB using
a professional motion tracking system. On a real indoor testbed, we demonstrate
an increase in accuracy of approximately 70% for BLE and 50% for UWB. Not only
the proposed approach requires a very short measurement campaign, the low
complexity of the single-layer neural network also makes it ideal for
deployment on constrained devices typically for localisation purposes.
