MarcoFLY Framework helps users evaluate AI-generated information through epistemic labels, configurable safeguards, multi-LLM comparison, web research, and peer review. It is a free BYOK Progressive Web App for developers, researchers, professionals,.
A startup from Salerno, Italy that is founded by MarcoFLY Motta.
This page is designed to help you find out whether MarcoFly.app is good and if it is the right choice for you.
Picture asking ChatGPT, Claude or Gemini anything and getting back an answer that openly declares how reliable it actually is. That's MarcoFLY Framework (MFF): a full web app, the APEX Ecosystem, wrapping every AI interaction in a rigorous epistemic protocol, turning a chatbot into a genuine critical ally.
Inside the PWA you connect your own API keys (BYOK, AES‑256‑GCM encrypted), choose from 17 AI providers, get real‑time streaming, and use MWAL for contextual web search, all in one interface. At its core sit six epistemic labels: Certain, Probable, Maybe, Depends, Unknown, Cannot. Without a citable source in session, the model cannot call itself "certain"; when data is missing, it must stop instead of inventing, removing generative AI's most dangerous silent risk: fluent text hiding real uncertainty.
Seven modular shields, L1–L7, guard the system: hallucination prevention, forced citation, cross‑model peer review, and defense against long‑session degradation. Individually activatable, they fit research, software, legal, medical, management or education. MFF aligns with NIST AI RMF, targets AI Act compliance, is free in public beta and fully verifiable: author Marco Motta, ORCID, OSF preregistration, public code.
Official claim: "A framework that doesn't apply critically to itself cannot be considered credible. MFF demonstrates what it teaches."
This isn't just another prompt — it's the closest thing to a definitive way to use AI with discipline, honesty and true intellectual freedom. Try it at marcofly.app.
Listed in
Epistemic Labelling System
Every AI response is tagged with one of six certainty levels (Certain, Probable, Maybe, Depends, Unknown, Cannot), making claim reliability explicit.
L1–L7 Modular Shields
Seven independently activatable protection layers: hallucination prevention, forced source citation, cross‑model peer review, and anti‑degradation over long sessions.
BYOK Multi‑Provider Support
Bring your own API keys across 17 AI providers (Claude, ChatGPT, Gemini and more), AES‑256‑GCM encrypted server‑side, never exposed to the browser.
MWAL Web Search Gateway
Built‑in web search module for real‑time contextual retrieval, paired with SSE response streaming.
NIST AI RMF Alignment
Designed in line with the international AI risk management standard, delivering governance and AI Act‑oriented compliance without changing existing infrastructure.
Cross‑Platform Compatibility
Runs as a PWA (APEX Ecosystem) or as an activation prompt on any AI platform, no installation required.
Open Science Verification
Validated through ORCID OAuth, OSF preregistration, and opt‑in peer review for traceable, scientifically rigorous output.
MarcoFLY is an epistemic control layer for generative AI. Instead of focusing only on generating answers, it helps users understand the status of each claim by distinguishing confidence, inference, uncertainty, and the need for verification.
It combines epistemic labels, configurable safeguards, multi-LLM workflows, web research, peer review, document attachments, transferable session context, and a BYOK architecture in one Progressive Web App.
MarcoFLY is model-agnostic: users can work with different AI providers while keeping control of their own provider accounts, API keys, and usage costs. It does not promise perfect answers or eliminate hallucinations; it makes uncertainty more visible and supports a more structured verification workflow.
People should choose MarcoFLY when they want more than a conventional AI chat experience. It provides a structured way to inspect AI-generated information, identify uncertainty, compare responses from different models, and decide what requires further verification.
MarcoFLY is also flexible: users can connect their own AI provider keys through BYOK instead of being locked into a single model or provider. The platform is free to use and supports developers, researchers, professionals, and AI power users who value transparency, control, and responsible human-in-the-loop interaction.
It is not designed to replace general-purpose AI assistants, but to add an epistemic control and verification layer above them.
MarcoFLY is primarily designed for developers, AI engineers, researchers, analysts, professionals, founders, educators, and advanced AI users who rely on generative AI for research, coding, decision support, documentation, and complex problem-solving.
It is especially useful for people who work with multiple LLM providers and want a structured workflow for identifying uncertainty, comparing model responses, checking sources, and keeping the human user responsible for final verification.
MarcoFLY began with a practical problem: generative AI can produce fluent and confident answers without clearly showing what is known, inferred, uncertain, or potentially incorrect.
As an independent full-stack developer and framework architect, I worked extensively with multiple AI models and saw that better prompts alone were not enough. I wanted to create a structured interaction method that would make uncertainty more visible and encourage active human verification.
This led to the MarcoFLY Framework and its multi-LLM Progressive Web App. The project continues to evolve as an independent, bootstrapped initiative focused on more transparent, controlled, and responsible human-AI interaction.
MarcoFLY is built as a Progressive Web App using modern web technologies, including SvelteKit, TypeScript, JavaScript, HTML, and CSS.
The platform integrates multiple AI providers and routing services through API-based connections and a BYOK architecture. Its infrastructure and deployment use Cloudflare services, while the broader ecosystem includes web research, real-time streaming, peer review, structured session state, document processing, and knowledge-base/RAG-oriented workflows.
The exact provider and infrastructure configuration may evolve as the framework and application continue to develop.
MarcoFLY currently does not have publicly disclosed enterprise customers. It is an independent, bootstrapped project in active development and validation.
The current audience includes individual developers, researchers, professionals, founders, educators, and AI power users who are testing the framework and providing feedback. Customer and user references will be added as formal partnerships or publicly shareable case studies become available.
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