Product development
Bugs, feedback and product ideas arrive with the evidence attached.
Software issues and physical-product opportunities need different questions. Both get a structured request, an owner, a due date and a recorded product decision — with AI output reviewed by a person.
Product Request
Software
Product opportunity
Physical product · configured type
How it works
From the first message to a recorded outcome.
- 01
The right request type
Bug, feature request, research insight or product question — each with problem, user impact and evidence fields.
- 02
A physical product is different
Configure a request type for commercial opportunities: account, market, price and cost targets, forecast and packaging.
- 03
An owner reviews
The product workspace requires human review of AI output and never sends a reply automatically.
- 04
The decision is kept
Write the decision into the resolution notes, reply to the reporter, and keep what you learned as product knowledge.
CSV export drops accented characters
Submitted answers
- User impact
- Every French and Spanish customer list; weekly export
- Desired outcome
- Exports keep UTF-8 characters intact
export-sample-fr.csv · export-sample-es.csv
Problem Signal
Looks for: the user problem, expected behavior, observed behavior, evidence, and reproduction details.
Product Next Steps
Looks for: research or delivery uncertainty, acceptance signal, and the next product decision or investigation.
Trailside compact fire pit
Submitted answers
- Retail / cost target
- $149 / $49
- First-year forecast
- 3,000 units
Resolution notes: “Proceed to sampling; revisit packaging cost.”
Set up for this work
The Product Development profile, out of the box.
A workspace set up with this profile starts with these defaults. Rename terms and switch modules on or off later; the review policy stays with the profile.
- A request is called
- Product Request
- Who asks · for whom
- Reporter · Product Area
- Owned by
- Owner
- Time tracked by
- Requested due dates
- Starter form
- Product Request: Request type, Problem or signal, User impact, Evidence, Desired outcome
- AI review
- Human review of AI output by default; automatic sending blocked
- Knowledge filed as
- Product Learning in Product Knowledge
The analysis is guided to look forProblem Signal · Product Next Steps
- the user problem, expected behavior, observed behavior, evidence, and reproduction details
- affected segment, frequency, reach, severity, dependencies, and current workaround
- research or delivery uncertainty, acceptance signal, and the next product decision or investigation
Priority rises for
- data loss, security, severe regression, or inability to complete a core workflow
- broad customer reach, strategic-account impact, or a launch blocker
- high-frequency friction with no reasonable workaround
From the product’s profile instructions. The analysis can be wrong; your team decides.
What it won’t do
- Not a replacement for an issue tracker, roadmap tool, PLM or production planning system.
- Commercial fields are configured by you; Assist Engine does not calculate margins or forecasts.
- Suggested replies are drafts; a person sends them.
Try it with this team’s questions.
The sample runs in your browser. Nothing is submitted, and no account is needed.