Research
Pre-launch · Public-safe researchResearching the proof layer for AI-built software.
Software 4 All studies how AI-built software moves from demo to controlled software path.
Our research focuses on evidence packets, builder/checker separation, protected files, false-green detection, non-developer approval review, and claim discipline. All research themes shown are public-safe only.
Public-Safe Research Themes
Eight public-safe research themes.
These are the public-safe research themes Software 4 All is investigating. Internal policy engine mechanics, proprietary prompts and rules, full harness scripts, private logs, source code, and automation keys are not disclosed.
Approval review for AI-built apps
How can a non-developer founder safely approve AI-built software changes? The approval workflow, evidence requirements, and decision structures needed for founder-readable gate decisions.
Evidence packets
Structured records of what was checked, what evidence exists, what is missing, and what decision the Approval Authority must make. The format, contents, and claim discipline for evidence packets.
Builder / checker separation
Why the AI that builds must not be the same system that verifies. The independence requirements, checker design patterns, and evidence standards for meaningful builder/checker separation.
False-green detection
AI builders can report completion when work is incomplete, incorrect, or unsafe. Research into patterns that produce false-green signals and how evidence requirements can surface them.
Protected files and locked zones
How to define, enforce, and audit areas of a codebase that must not be touched by an AI builder without explicit authorized approval. Lock zone design, enforcement, and violation detection.
Non-developer founder workflow
What does a non-developer founder actually need to make real software approval decisions? The information requirements, decision formats, and escalation paths for non-developer founders who cannot read code.
AI demo to real software gap
Why AI builders create convincing demos that are not real software. The gap between a working demo and a maintainable, reviewable, deployable software path — and what is needed to close it.
AI-built app failure patterns
Research into how AI-built apps fail — missing tests, broken auth boundaries, unreviewed secrets exposure, demo data in production, and other patterns that create invisible risk for non-technical founders.
Evidence Outputs
Research produces evidence, not claims.
All Software 4 All research is captured in structured evidence outputs. Claims are not registered until evidence exists. The following outputs are produced by the Software 4 All research and proof system.
Lab Notes
Ongoing research notes on AI-built app failure patterns, approval review workflows, and evidence discipline.
Evidence Packets
Structured records of what was checked, what evidence exists, and what decision the Approval Authority must make.
Proof Ledger
Running record of accepted gate runs, decisions, accepted claim mappings, and open proof gaps.
Claim Register
Accepted claims mapped against evidence. Unsupported claims remain blocked until evidence exists.
Benchmarks
Baseline measurements for gate performance, evidence completeness, and proof path progress over time.
Failure Patterns
Catalogued patterns from internal proof projects documenting how AI-built software fails in practice.
Research themes shown are public-safe only. Internal policy engine mechanics, proprietary prompts and rules, full harness scripts, private logs, source code, and automation keys are not disclosed. Accepted claims should map to evidence before registration. No commercial, production, platform, or security claim is made until evidence gates pass.