Case studies

One task, before and after it was written down

Each of these takes a single piece of work a team already does with AI, and follows it through: the private processes it started as, the workflow it became, and why each step is a model reasoning or the definition deciding.

Every scenario on this page is one we wrote. Murmurator is new, and we would rather show you a process worked through honestly than invent a customer. The workflows are real and buildable; the teams, the people and the volumes are not. No metrics are claimed anywhere on these pages, and none will be until a customer agrees to publish theirs.

Customer support Example scenario

How a support team standardized AI-powered refund investigations

Before Murmurator, every support specialist had their own AI-assisted way of deciding whether a refund request met policy, and no two of them gathered the same evidence.

The team now runs the same controlled workflow for every refund investigation: the same context, the same policy, one model call, and a refund that can only be issued by a condition the definition spells out.

ZendeskStripePostgreSQLSlack

Platform engineering Example scenario

How an on-call rotation stopped writing incident reviews from memory

Before Murmurator, the write-up after an incident depended on which engineer was on call, how much of the timeline they could still reconstruct, and how much of the week was left.

The draft review is now produced by a workflow that reads the same sources in the same order every time, and a person edits a document rather than reassembling an evening.

PagerDutyGitHubSlackDatadog

Revenue operations Example scenario

How a revenue team stopped researching the same lead three times

Before Murmurator, an inbound lead was researched by whoever picked it up, with whichever AI tool they preferred, against whichever qualification criteria they remembered.

Every inbound lead now gets the same research from the same sources, scored against criteria written in the workflow, with routing decided by conditions rather than by a model.

HubSpotHTTP APIsSlackPostgreSQL

Or compare the approaches

Learn

Prompt vs workflow

A prompt is an instruction one person gives a model. A workflow is how an organization does something. The gap between them is where most AI value quietly gets stuck.

Learn

AI agents vs AI workflows

Agents decide what to do next while they are running. Workflows decided beforehand. Both are legitimate, and the choice is really about what you need to be true before the run starts.

Comparison

Murmurator vs ad-hoc AI in a chat window

Chat assistants are where nearly all useful AI work starts. The question is what happens to a process once it matters to more than the person who invented it.

Turn an AI process into a workflow.

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