How Bruce plans the work

Clear thinking before a team spends time and money building.

These simplified examples show how Bruce defines a product, maps an AI workflow, checks quality, and prepares a launch. They are representative frameworks, not client or employer documents.

Product brief

A clear product definition before the team builds

The user, problem, business decision, scope, assumptions, information needed, signs of success, and important limits.

User: the person making a time-sensitive business decision
Problem: information arrives faster than the team can check and use it
Not included: automatic publishing or recommendations without support
Quality rule: important claims keep their source, date, and review status

Workflow map

An AI workflow with human review

The information that comes in, what the AI does, where a person reviews it, how exceptions are handled, and how a release can be stopped or reversed.

Collect only the information the task needs
Use trusted sources
Create a structured draft
Send uncertain cases to a named person
Publish only after the required checks pass

Quality checklist

A clear way to evaluate AI output

Accuracy, source quality, relevance, freshness, unsupported claims, when to stop, and who is responsible for review.

Accuracy: the output matches the source
Source: every important claim can be traced
Freshness: time-sensitive facts are checked before use
Stop rule: uncertainty or conflict blocks automatic publication

Launch checklist

A decision memo before production

Owners, dependencies, measurement, approvals, rollback, unresolved questions, and who decides when the work goes live.

The source version and deployment are known
Desktop and mobile experiences are checked
Credentials and outside integrations are tested separately
A named owner approves the production launch

What these examples are

Representative methods, clearly labeled.

These public examples show how Bruce works without exposing employer, client, customer, or other private information.

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