Methodology
A structured, governance-first framework to move from scattered experiments to production-grade AI at scale.
This is the detailed 4D system walkthrough. Ready to buy? Head to the 4D packages for pricing and deliverables. Need a single install instead? Explore Kingsbury Systems.
Siloed pilots with no coordination
No clear policies on data or risk
Investments without success criteria
Tools selected before needs defined
Users reluctant to adopt new tools
Four distinct phases designed to de-risk adoption and accelerate value. Click any phase to explore.
Our approach
We build controls around what you own: agent platforms with usage logging, input sanitisation, output filtering, and monitoring. Provider-side behaviour remains outside your control, so we help you mitigate that through prompt engineering and response validation.
We evaluate current frontier models based on your data residency requirements, cost constraints, and latency needs, not hype. Different use cases often warrant different models.
Demos impress stakeholders; production systems serve users. We design for error handling, graceful degradation, monitoring, and the reality that models behave unpredictably.
We establish baseline metrics before deployment and track specific improvements: time-to-completion, error rates, throughput, cost-per-task. No vague claims of 'productivity gains'.
We train your team on prompt engineering, evaluation methods, and operational monitoring. The goal is independence, not a recurring consulting dependency.
Infrastructure
Each 4D phase ships specific infrastructure modules from our Kingsbury Systems stack.
Assessment of data foundation and initial governance needs
Build pilot infrastructure with connected systems
Full stack deployment with governance overlay
Continuous governance and agent improvement
Follow a representative journey through our 4D methodology, showing how a mid-size financial services firm could transform their compliance operations.
This illustrative example represents typical engagement patterns and outcomes. Actual results vary based on scope, complexity, and organisational readiness.
“We thought we knew where AI could help. The discovery phase revealed opportunities we hadn't even considered.”
“The blueprint gave our IT team confidence. Every workflow was documented, every risk mitigated.”
“Watching the agent handle its first real compliance query was surreal. It understood context we hadn't explicitly taught it.”
“The handover was smooth. Our team felt ownership from day one, not dependency.”
Representative results from a compliance automation engagement of this scope and complexity.
These figures are illustrative. Your actual ROI will be estimated during the Discover phase.
Get started
Stop the scattered experiments. Start building a production-grade AI capability.
Questions