Architecture & Documentation Review
Case Study
About Our Client
Overview
Our client is an asset finance provider replacing its RPA-based loan originations platform with a modular solution designed to scale as origination volumes grow. The client had a proposed target-state architecture in place and an internal architecture team that had documented the framework underpinning the build across two knowledge bases — an architecture framework and an engineering framework. Before committing further to the build, the client wanted an independent, expert opinion on whether the architecture was fit for production-grade automated credit decisioning, whether it avoided the structural weaknesses of the platform it replaced, and whether its documentation and supporting tooling were fit for the engineers who would rely on them.
The Challenge
An independent opinion before committing to the build
The client was moving away from an RPA-based originations platform that had reached its structural limits, toward a modular architecture intended to scale with growth. Significant investment and delivery risk rode on whether the proposed design was genuinely production-ready — and the client needed an honest, independent view before progressing, not reassurance after the fact.
Key challenges included:
- Confirming whether the proposed target-state architecture could support production-grade automated credit decisioning at both current and projected volumes, rather than requiring costly re-architecture later.
- Ensuring the new design avoided the structural weaknesses of the legacy RPA platform it was replacing.
- Validating that the credit decisioning rule engine was designed to support credit policy change through configuration rather than redevelopment.
- Assessing integration architecture, error handling, retry and timeout behaviour, and failure isolation, alongside the foundational elements gating the build — environments, CI/CD, observability, security, and access model — and the adequacy of non-functional requirements.
- Determining whether the internal documentation was clear, complete, and navigable enough for a new engineer to rely on from day one.
- Independently evaluating a set of AI-authored documentation tools the client had built to support its software development lifecycle — an emerging capability with no established benchmark to test against.
Services:
- Independent Target-State Architecture Review
- Credit Decisioning Rule-Engine Design Assessment
- Integration Architecture & Failure-Mode Review
- Foundational Elements & Non-Functional Requirements Assessment
- Scalability Assessment
- Documentation Framework Review
- AI Documentation Tooling Usability Testing
- Severity-Rated Findings Report & Fit-for-Purpose Opinion
Sector
Financial Services (Asset Finance / Credit)
The Approach
An honest, independent view — collaboratively delivered
Avocado worked collaboratively alongside the client’s internal architecture and engineering team, with a single clear purpose: to provide an honest, independent view of the proposed architecture, the documentation framework, and the AI tooling, surfacing any gaps or improvements before the build progressed further. True to how Avocado works, the remit was to tell the client what it needed to hear, not what it wanted to hear. The engagement was structured across a focused review window:
Kick-off & alignment: A session to confirm access to the architecture documentation, both documentation frameworks, and the AI tooling, agree the review approach, and establish a point of contact for questions throughout the review.
Target-state architecture assessment: An independent review of the proposed architecture, the credit decisioning rule-engine design, integration patterns, foundational elements, non-functional requirements, and scalability — assessed against resilience, supportability, failure modes under load, and the capacity to support business growth without re-architecture.
Documentation framework review: A structured read-through and independent assessment of both the architecture and engineering knowledge bases, evaluated against whether the content made sense, whether anything critical was missing, whether it was easy to navigate, and whether a new engineer could use it immediately — with particular focus on the day-to-day standards an engineer would rely on from day one.
AI tooling usability testing: Blind usability testing of the client’s AI-authored documentation tools, with the level of prior briefing agreed at kick-off, capturing structured feedback on whether each performed as expected and where it could be improved.
Findings consolidation: Consolidation of all review outputs into a single structured findings report — an overall fit-for-purpose opinion (fit for purpose, fit for purpose with conditions, or not fit for purpose), with conditions explicitly listed and rated, findings graded by severity (Critical, Major, Minor) with the risk each represented and a recommended action, and a clear caveats section defining what the review did and did not cover.
The Result
Clarity and confidence ahead of a major build decision
The engagement was designed to give the client a clear, evidence-based basis for its next decisions — an independent verdict on whether to proceed, refine, or rethink before committing further investment to the build.
- The architecture review was structured to deliver a definitive fit-for-purpose opinion, with every condition and finding rated by severity and paired with the risk it represented and a recommended action, so leadership could act on it directly.
- Assessing the design against current and projected volumes was intended to surface any structural risk early, before it became expensive re-architecture — and confirm the platform could scale without needing to be rebuilt.
- Independent review of the documentation frameworks was designed to improve their clarity, completeness, and usability, so a new engineer could become productive quickly.
- Blind usability testing gave the client objective, structured feedback on an emerging AI tooling capability that had no external benchmark, highlighting where it delivered and where it needed refinement.
- Delivered collaboratively but independently, the engagement was designed to give the client an honest expert view — identifying structural risk early and supporting a confident, well-informed investment decision.
Overall, the engagement was designed to give the client independent assurance over its proposed architecture, documentation, and tooling — the evidence base to move forward on its originations platform with confidence, or to course-correct before the cost of change grew.
