Architecture, implementation, automation, integration and technical assurance for organisations where Salesforce has to work with real processes and connected systems.
Map users, process, current systems, data, risk and constraints; then define the target Salesforce architecture.
Configuration, Flow, Apex and Lightning Web Components are selected according to maintainability and the problem being solved.
APIs, data flows, external systems and orchestration are designed so ownership and failure paths remain understandable.
Testing, release assurance, recovery planning, documentation and knowledge transfer close the gap between build and operation.
A maintainable Salesforce platform begins with the organisation, its users, its data and the processes the technology must support.
The aim is not simply to switch Salesforce on. It is to create a platform users understand and teams can continue to develop.
Discovery, solution design, configuration, development, testing, migration, deployment and release support.
Native platform capability first, custom engineering where it genuinely adds value.
Salesforce rarely operates alone. We design APIs, external connections, migration strategies and data flows that remain understandable as the landscape evolves — including Data 360 (formerly Data Cloud) where it is the right foundation for trusted enterprise context.
Existing environments can become difficult to change. Architecture review identifies unnecessary complexity, fragile automation, unclear ownership and integration risk before a recovery path is designed. Release design can include source control, governed pipelines, automated quality gates and next-generation DevOps Center where those controls fit the delivery model.
Alongside Salesforce delivery, Substrate's data work spans digital forensics, security architecture and advanced analytical approaches. The common discipline is trust: knowing what data is, where it came from, who can access it and what evidence it leaves behind. Enterprise systems are judged not only by what they can do, but by whether the data they depend on can be trusted, protected and interpreted clearly.
Investigation of digital evidence across devices, systems and networks — from acquisition and forensic imaging through to analysis, timeline reconstruction and clear reporting.
Protection of sensitive information using layered controls: encryption, privilege boundaries, identity and access management, tamper resistance and disciplined administration.
Analytical work that extends beyond reporting, with an applied-R&D watching brief on emerging areas such as quantum-enhanced machine learning — clearly separated from production capability.
Digital forensics can support criminal or civil matters, commercial disputes, internal corporate investigations and specialist intrusion enquiries. The work often spans more than simple recovery — it is about understanding what the digital evidence can and cannot prove.
Security cannot rely on one control. Strong data protection requires the right combination of software safeguards, identity controls, privileged access management, hardware-assisted protection where appropriate, and practical operating discipline.
Some advanced analytical methods remain experimental, but that does not make them irrelevant. Quantum-enhanced machine learning is an example of a field worth tracking carefully: classical data is encoded for specialised processing, evaluated, and measured for useful outcomes — while recognising that many techniques remain theoretical or limited to specialist hardware.