AI + email: where we stand

AI Manifesto

Since 2014 we've been working on one question: what makes digital content good. AI changes what our tools can do, and with it, what good means. Here's how we're using it at Beefree and Really Good Emails, and where we draw the line.

Cover and a page from The Beefree and AI Manifesto with a purple background and white text, stating 'Expanding the definition of good digital content' along with happy faces of two women near the documents.
Our mission

Why we exist

Digital content is key to the way people communicate. Since 2014, Beefree's mission has been to help more people create good digital content, with a particular focus on email.

When we started, by good we mostly meant consistent with design best practices, responsive on mobile devices, and displaying as expected.
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Over the years, we expanded the definition of good to include ways to comply with brand guidelines, fix problems, and produce more accessible content.
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We added new dimensions to good with Really Good Emails: celebrating excellence that inspires others through a curated email gallery, research, events, and community.

Man wearing glasses and a red cap with the word HUMAN, sitting and working on a laptop with a black t-shirt that says Really Good Emails, surrounded by icons including a paper airplane, accessibility symbol, globe, chat bubble, and translation symbol on a purple grid background.

We expanded the definition of good.

A smiling woman with glasses, wearing a red sweater and blue pants, sits cross-legged in a meditative pose on a purple background. To her left are digital elements including a black chat interface with the OpenAI logo, icons, and a webpage titled 'BrightPath' with the headline 'Small Actions. Big Impact.' showing a group of people outdoors at sunset.

AI can help advance our mission.

AI impact on our mission

Why we use AI

Artificial intelligence can expand what our tools are able to do, and with it, what good means.

A few examples to illustrate the point:

AI allows natural language interactions, no matter which language our customers speak.

It can help diagnose and fix issues based on rules they set.

It can show inspiration related to the content they are working on.

It can flag off-brand design elements.

There are many others.

We believe that, as a result, AI can help us advance the mission we've pursued since we started, enabling more people to create better content.

Our constitution

How we use AI

We are aligned with Human-Centered AI (HCAI), the discipline intent on creating AI systems that amplify rather than displace human abilities.

It's the application to artificial intelligence of human-centered design, which prioritizes the needs, values, and experiences of people.
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HCAI is a broad discipline. We have intentionally decided to focus on three key principles, expressed here in our own words, which can be our constitution.

Smiling man riding a red tandem bicycle with a small white robot seated behind him, both facing forward.

Collaboration between humans and AI

AI in Beefree is meant to augment, not replace, people. It enhances human capabilities, handling speed and scale, while humans retain control over quality, judgment, and creative direction.

Explainability and traceability

AI in Beefree is explainable, allowing people to understand how it draws conclusions. It is also traceable, transparently and reliably logging its actions, which earns people's trust and may also be a legal requirement.
A young man wearing a red cap with the word 'FOCUSED' makes two 'OK' hand signs next to a chat interface showing a green message bubble that says 'Ooook!' with a check mark.

Human accountability

AI in Beefree includes meaningful human interaction, and forces human supervision when required, by law or otherwise. Logging "human-in-the-loop" events ensures accountability.
What we're committing to

How this shapes what we build

We see Beefree and everything it creates becoming a human-centered, AI-augmented content creation platform. Our goal is to help create better content at scale while keeping people in control. As AI expands what our tools can do, it also broadens what good digital content can mean.

Earlier in this page we mentioned a few practical ways in which good becomes broader. We are committed to exploring and bringing to market product improvements to make that possible.

Our commitment

We will prioritize alignment with this manifesto over speed to market.

The product roadmap will evolve as capabilities do, so we are intentionally not setting it here. The principles in this manifesto are our current best thinking, but we expect them to evolve too, as we learn, as the technology advances, and as the regulatory landscape develops.
We commit to revisiting this document regularly and openly, and, when it changes, to communicating how and why.

Download the full Manifesto
Appendix

Glossary

Traceability

Noun, property. Traceability is the ability to follow the origin and history of something through a process: knowing where it came from, what happened to it, and who made decisions about it along the way. Applied to AI, it means being able to see what a system did, with what inputs, and at which step. In a content creation workflow, traceability means that at each stage (drafting, editing, reviewing, approving) the people involved can see what the AI generated, what a human changed, and what was left untouched. That visibility is what makes meaningful judgment possible: you can only truly own a decision if you know what you're deciding on.

Human accountability

Noun, principle. Means that a person remains responsible for the outcomes an AI system produces. It's the principle that AI can assist, recommend, or automate, but when something goes wrong, there is always a human who owns the result. This matters because AI systems can fail in ways that are hard to predict, and diffuse responsibility ("the AI decided") is both ethically insufficient and, in many jurisdictions, legally untenable.

Human-centered design

Noun, practice. Is a design philosophy and practice that starts with the people who will use a product, not the technology behind it. It prioritizes understanding users' needs, mental models, and contexts before defining solutions. The process is iterative: prototypes are tested with real users, insights feed back into design, and the cycle continues. It contrasts with technology-centered design, which starts with what's technically possible and works outward. More on HCD? See IDEO, What is Human-Centered Design? or the foundational ISO 9241-210 standard.

Human-centered AI

Noun, HCAI, discipline. Applies the principles of human-centered design to the development and deployment of AI systems. Where traditional AI research has focused primarily on capability (making systems more accurate, faster, more powerful) HCAI asks a prior question: does this system actually serve the people using it? It was formalized as a discipline by researchers including Ben Shneiderman at the University of Maryland, whose 2022 book Human-Centered AI laid out the framework. HCAI calls for AI systems that amplify human ability, support human oversight, and remain controllable even as they grow more capable. More on HCAI? See Stanford HAI and the seminal Shneiderman, Human-Centered AI, OUP, 2022.

Augmentation

Noun, approach. In the context of AI, it refers to using artificial intelligence to enhance what a person can do rather than to replace what they do. An augmenting system handles the parts of a task where speed, scale, or pattern recognition give it an advantage, freeing the human to focus on judgment, creativity, and context. The distinction matters: automation removes the human from the loop; augmentation keeps them in it. Research shows that augmentation-style human-AI collaboration often produces stronger outcomes than either humans or AI working alone, particularly for creative and knowledge work. More on augmentation? See Licklider, "Man-Computer Symbiosis," 1960 (the foundational text). For current data, see the AI Index Report 2026, Chapter 4.