Problem
Potential Problems:- No single view of what has been built: Large Fluent Commerce implementations accumulate workflows, rules, and settings over time with no master record of what exists, making it difficult for anyone new to form an accurate picture of the full system.
- Features understood in isolation: Individual workflows are often well understood by the people who built them, but how they connect to and depend on other features is rarely documented, creating blind spots when making changes.
- Gaps and risks that have never been formally identified: Orphaned rules, missing workflows, unhealthy webhooks, and dead-end event paths tend to accumulate quietly and are rarely surfaced without a deliberate audit.
- Slow and incomplete onboarding: Getting up to speed on a new account typically involves piecing together information from multiple sources over days or weeks, with no guarantee that the picture formed is accurate or complete.
- No objective measure of customisation depth: It is often unclear whether a feature is running on standard out-of-the-box behaviour, lightly configured, or heavily customised, which matters significantly when estimating the effort or risk involved in making changes.
- Undocumented implementation knowledge: When the developers who built the system move on, the understanding of what was built and why moves with them. Without a generated inventory, that knowledge is simply lost.
You're onboarding to a new Fluent Commerce account and need to understand the full implementation — what features exist, how they connect, and where the gaps are.
Solution Overview
Mapping a full Fluent Commerce implementation begins with a single request that triggers a structured, five-phase analysis of the account. The process is entirely read-only, meaning it can be run safely as a first step on any account without any risk of making changes to the environment.The analysis starts by collecting a raw inventory of everything that exists: workflows, custom rules, settings, and local source code. This raw data is then interpreted to identify the logical features it represents, grouping related workflows and rules together based on how they interact, what naming conventions they follow, and which entity types they involve.With features identified, the tool maps how they connect to each other, tracing cross-feature dependencies and generating visual diagrams of the full implementation landscape. This makes it possible to see not just what has been built, but how the pieces relate and what would be affected by changes in any one area.A gap analysis is then run across the entire implementation, surfacing missing workflows, rules that are no longer connected to anything, webhook configuration issues, and areas where out-of-the-box coverage may be insufficient. Each gap is recorded with a severity rating and a remediation recommendation.The output is a structured set of documents covering the full implementation: a master summary, a machine-readable inventory, a dedicated gaps report, and individual summaries for each identified feature. Each feature also receives a customisation depth score and a confidence rating based on the quality of evidence found, so the reader knows how much weight to place on each finding.The result is a comprehensive, ready-to-share picture of the account that would otherwise take days or weeks to assemble manually.Solution
