Normative source
Infinity Methodology
Defines lifecycle stages, standards, templates, gates and evaluation expectations without prescribing a repeated visual outcome.
Independent product development / Design operations and AI collaboration
A methodology, orchestration layer and local project workspace designed to make rigorous design practice usable, traceable and recoverable in AI-assisted work.
Inspect the operating modelBounded progression
The Runtime selects the next applicable activity, assembles only the required guidance and blocks progression when evidence or approval is missing.
Establish enough understanding to define the problem responsibly.
Evidence / risks / goals / briefMove from evidence to a distinct rendered direction, then refine it deliberately.
Structure / direction / approval / systemImplement approved authority and verify the experience under real conditions.
Build / accessibility / performance / parityRelease or hand over with ownership, rollback and support boundaries understood.
Readiness / documentation / sign-offCompare the delivered outcome with the original goals and return learning to the method.
Feedback / analytics / lessons / changeMinimum sufficient context
Four connected records stop an AI-assisted workflow from becoming an untraceable conversation. Each remains inspectable and has a different job.
A temporary pack assembles the current activity, relevant authority, live evidence, risks and unresolved questions.
Small enough to use / complete enough to actSources, transformations and confidence remain linked so client statements, observed facts and inference do not collapse together.
Source / checksum / relationship / boundaryConsequential decisions and Stage Gates record the named person, evidence, date and exact authority being accepted.
Decision / approver / evidence / scopeSnapshots and validation make rollback an explicit project operation rather than an improvised response after context is lost.
Snapshot / restore / validate / recordImplemented evidence
Evidence boundary
A passing rehearsal proves routing, state, authority, evidence boundaries and recovery. It does not prove that a real project’s research, strategy, design or commercial outcome is good. Controlled client use still requires engagement-specific readiness and named human responsibility.
Decision register
The system becomes useful by constraining where automation can act, not by pretending every stage can be automated.
Projects share lifecycle discipline and quality expectations while every visual language, component system and interaction model emerges from its own evidence.
The Method defines reusable practice. Studio selects and sequences it. The project workspace records what happened in this engagement.
AI can assemble, compare, validate and recover. Named people still interpret evidence, choose direction and accept consequential risk.
Continue through the work
Tell me what needs to be decided, which evidence exists and where the current workflow loses clarity or authority.
Start a conversationI usually respond within two working days.