Governance loop before output is used
Loop guides intent, constraints, and review so drafts are structured before they become team output.
Product
Governance workflow for human decisions under real deadlines.
AI use is already happening across your organization. Loop is Foryn's software platform for making AI-assisted work structured, reviewable, and consistent.
This is not a prompt library. It is a governance workflow for AI use.
foryn loop
Evolving prompt
Step 1/4Explain how Foryn guides prompt quality from first draft to reusable standard.
Suggestions
Latest improvement
Pick a suggestion to improve the prompt.
Designed for accountable team decisions
Teams can apply one standard, keep review status visible, and make decisions easier to defend.
See the standard in action
Apply one change at a time and watch the draft get stronger.
Status: Not started
The Foryn difference
Changes applied: 0
Select READ, PROMPT, or EDIT to apply one change.
Before
Project status update
The project is slightly delayed due to issues with vendor coordination. We are working through them and expect things to improve soon. The team is aligned and progress is being made.
After
AI response appears here after you pick a step.
Standards enforced at the human decisions layer
What Loop does
Loop guides users through the loop before AI output is approved for use.
- Clear intent
- Context and constraints
- Defined output expectations
- Explicit review before export
No blank box. No unreviewed output.
Structured loop, not open chat
Most AI tools start with an empty prompt field. Loop does not.
- Clarify what they are asking for
- Narrow scope and assumptions
- Identify risk or sensitivity
- Shape output toward a defined standard
The goal is reliable output someone is willing to stand behind.
Review is built in
AI output should not move forward without review.
- Review confirmation before export
- Manager visibility into draft status
- Structured review notes
- Clear status indicators
This creates a repeatable review habit across the team.
Decision trail and traceability
- Every draft is linked to the user
- Every draft is linked to the workflow steps taken
- Every draft is linked to the review status
- Every draft is linked to the team context
Managers can see how AI is being used, not just the final result. AI usage becomes visible and structured instead of informal and fragmented.
Better over time
Team performance trends for AI work quality and speed.
Hours saved
66h
from 36h via reused prompt library standards
Reuse rate
64%
from 39% of library-ready prompts
Readiness rate
86%
from 68% across prompts promoted to library
Cycle time
5.1d
from 7.1d from submission to library approval
Time saved
Cycle time
Quality trend
Reuse rate
Top teams
Ops Team88%
QA Team A84%
QA Team B46%
Insight
Time saved increased from 36h to 66h over the last 30 days.
Momentum
Reuse rate moved from 39% to 64% while quality rose to 86%.
Top improving team
Ops Team leads this window with +32h saved.
Example data shown.
How it works
Control PlaneStandards and governance
- Activation defines acceptable AI work.
- Admin sets templates and review criteria.
- Roles define who can create, review, and export.
Work PlaneDaily execution in platform workflow
- Users create governed work items.
- Structured workflow forces clear inputs and refinement.
- Review gates prevent unreviewed export.
Evidence PlaneDecision trail and exports
- Each work item has revision history.
- Each export has a review event.
- Managers see usage and review status.
Designed for teams where decisions must be defensible
- Communication quality matters
- Decisions must be defensible
- Standards must be consistent
- Managers need visibility
If AI output leaves your organization, it should follow a standard first.