What we do

Solutions need to be seen and not just talked about.

Nhillite’s process starts with aiding in identifying a goal through idea formulation, helping to translate a given future state, wish-list, business idea, or explorative innovation into approachable AI or machine learning and data centric solutions.

From here, a single or series of well-defined experiments are designed, centering around a practical working prototype, which is improved through the results of each experiment through iteration and testing. This is paired with clear performance measures and benchmarking to help with continued and measured improvement of the solution, in addition to identifying any potential risks.

Once a sutiable solution is agreed upon following experiment outcomes, performance measures and stakeholder feedback, a roadmap can be designed and undertaken. Roadmaps can range from stand-alone small scale solutions all the way through to large scale intergrations.

One size doesn’t fit all. Prototyping allows for continuous testing and tailoring.

The adoption of prototyping allows for a framework which can be rapidly designed, built, tested, evaluated and, if need be, adapted or abandoned – allowing for numerous solution pathways to be tested. At Nhillite, our experience is that the initial solution proposed will continue to evolve and be shaped through lessons learnt and failures measured.

Paired with domain knowledge and adaptability.

Our bespoke approach to experimentation and prototyping in artificial intelligence and machine learning has been applied to financial services, retail, online shopping, legal industries, and government. Nhillite’s range of solutions also centres around a given domain of interest which will guide the sourcing, analysis and engineering of data. The design of the solution ensures it evolves with the needs of your organisation through an adaptable framework which can be built upon as future needs arise.

Artificial Intelligence

Initial design, prototyping and implementation of solutions utilising proven approaches in current deep learning, planning, and reinforcement learning methodologies, combined with experimental frameworks for bespoke applications. Domain tasks include classification, natural language processing, game playing, scenario optimisation, prediction, and automation.

Machine Learning and Data Science

Application of machine learning for predictive and prescriptive analytics and insights utilising structured and unstructured datasets, including transactional data, customer data, images, text, and sensor data. Domains of application include fraud analysis, customer behaviour, image classification, social network and sentiment analysis, and predictive strategy.

Data Mining and Engineering

Gathering, analysis, cleaning and preparation of structured and unstructured datasets for the purpose of data enrichment, analysis, and preparation for bespoke modelling and reporting.