The AI framework that
applies to itself.
MFF wraps your AI interactions in a structured protocol that prevents hallucinations, tracks epistemic reliability, and keeps the model accountable — not the other way around.
APEX Ecosystem v1.5.3 · L1–L7 active · 13 AI providers
The Problem
AI tools hallucinate.
Confidently.
Most AI interactions produce fluent, well-formatted responses that feel authoritative — regardless of whether they're accurate. There's no built-in mechanism to signal uncertainty, cite sources, prevent scope drift, or maintain consistency across long conversations. Users pay with their time, their trust, and sometimes their decisions.
"A framework that doesn't apply critically to itself cannot be considered credible. MFF demonstrates what it teaches."
The Engine
Six degrees of certainty
Every MFF response carries an epistemic label — a colour-coded reliability signal the model must apply to each claim it makes. No more uniformly confident output.
Grounded in cited sources or demonstrable evidence.
Consistent with available knowledge, not directly verified.
Insufficient evidence to assess — the model says so explicitly.
Weakly supported; inference from analogy or partial data.
Contradicted by available evidence — flagged, not hidden.
Insufficient information to evaluate. The model admits the limit.
The Name
What is FLY?
Critical AI use is a leadership skill, not a technical one. MFF gives everyone — from researchers to managers to first-time users — the same structured tools that experts use to keep AI honest.
For
Built for people who think
Code review, architecture decisions, debugging sessions with full epistemic tracking.
Scientific writing, literature review, and hypothesis generation with ORCID verification.
Professional contexts where precision, citation, and source attribution are non-negotiable.
Strategic analysis and risk assessment without delegating judgment to the model.
Teaching AI literacy and critical thinking — MFF as curriculum, not just a tool.
AI governance, policy, and responsible deployment at organisational scale.
Why MFF
Unique in the global landscape
MFF is a structured interaction layer, not a browser extension or wrapper API. It works on every AI platform.
Seven independently activatable protection layers — from hallucination prevention (L1) to peer review (L5) and anti-degradation over long sessions (L7).
Your API key, your conversations. Nothing passes through MFF servers. Zero data retention on the AI side.
Designed in alignment with the US National Institute of Standards AI Risk Management Framework — the international standard for responsible AI.
History
Built in public, starting March 2026
The MFF framework goes public with the Activation Generator and the first 7 protection shields.
PWA with BYOK, 13 AI providers, SSE streaming, and the MWAL web-search gateway join the framework.
ORCID OAuth, OpenScience Validation (F1/F2/F3), Peer Review opt-in, and admin analytics panel reach production.
Complete rebuild on new infrastructure — better IA, mobile-first, unified stack across all three subdomains.