Cloud & Data Platform

We set up and build the systems that make an organisation ready for AI, particularly IoT, cloud and data platform work. This is the layer that sits underneath every AI project: if the data is still scattered, has no dependable update cycle, or the systems cannot carry the processing load, then however good the model is, its results will not be consistent once it goes live.

Scope of work

We look after everything from bringing the data in, through the infrastructure that runs the systems, to the tools for watching them once they are live.

• AI Cloud Infrastructure Setup: infrastructure on GCP, Azure or AWS built to carry the processing work and to put models into production.
• Data Pipeline & ETL Automation: extracting, transforming and loading data from several sources so that it flows into one place on a set schedule.
• Data Scraping: collecting data from websites and public documents, then turning unstructured material into tables ready for analysis.
• IoT Solution: connecting devices and cameras to the processing systems, so that data and alerts arrive in near real time.
• Payment integration: credit top-up and charging that supports both credit cards and PromptPay, with a transaction history.
• Operations tooling such as BuildOS: a single screen for server status, the ports in use, and the progress of projects being delivered.

How we work with you

Work at this layer shows results more slowly than anything front-of-house, so we set it out in phases with a clear deliverable at each one, and progress you can follow throughout.

• 1. Assess where things stand: where the data sits, who owns it, how often it is updated, and who is pulling from it today.
• 2. Design the architecture: choosing the cloud and the storage model to fit the workload, the budget and your organisation's data requirements.
• 3. Build and test the pipeline: data flowing on its own to schedule, with quality checks and alerts when something is wrong.
• 4. Hand over with a maintenance plan: access rights, system documentation, and how to keep watch on it in production.

What you get

The result of work at this layer comes down to a single question: does the team still have to sit and merge files by hand every month?

• A central data source that the analytics team and the AI team can both work from.
• An ingestion process that runs itself to schedule, cutting down the manual gathering and copying of files.
• Cloud infrastructure that scales with use, with access control in place.
• Architecture documentation and an operations guide, so your own team can take it over.

It connects straight to the rest of our work

The platform built at this layer connects directly to our other three areas. Data arriving on a steady schedule becomes the dashboards in Data Strategy Analytics; the same data becomes the knowledge base for the chatbots and forecasting models in AI Solutions; and a team that has been through the Data & Governance for AI course can take over running it. All four areas then work from the same data from the outset.