Sustainability

AI is being used for things like climate modelling, grid optimisation, flood prediction, conservation, and materials research. Important stuff. We are not putting banner ads and creative work in the same category, even on our most confident days.

The question is practical to us: when using AI in brand work, how do we make the workflow less wasteful? How can we help companies and organizations avoid burning through tokens to achieve the bread and butter, the daily creative work? While still leveraging the power of technology to work faster and smarter?

A lot of AI waste comes from context that has to be rebuilt every time. One person figures out how to get their agent to do decent brand work. They write a long prompt, add examples, correct the output, try again, and slowly get closer.

Then the next person starts over. Or the same person starts again in a different tool. That is bad for consistency, and it burns tokens and time on work the organisation has already done once.

Braaand tries to reduce that by making everything brand related reusable and shared. Colours, fonts, logos, ad templates, image rules, and basic constraints live in one place. Agents can work from that shared context instead of each user having to rebuild it. In terms of sustainability, brand consistency is a side effect.

The first version of what AI is asked to deliver land closer to finished. That means fewer retries, fewer corrections, lower costs, and a faster path to something useful. This is key to using AI in a more sustainable way.

None of this makes us perfect. It is just how we try to build the product: spend less where less is enough, and save the heavier work for the places where it actually helps.

• We adapt the model to the task, so easy work that does not need a large, expensive, power-hungry model instead uses a faster and more efficient models.
• We use our proprietary BrandFit technology to help AI work with templates faster, use fewer tokens, and produce more accurate on-brand results. BrandFit helps AI using deterministic functions that AI use a lot of compute to achive the same result. This is good for speed, cost and causes less waste.
• We reject tokenmaxxing: the idea that burning more tokens is a good thing in its own. High tokenusage might look productive on a dashboard, but useful output and efficiency is what wins in the long run.
• We rely on the most modern serverless architecture that scales up and down with demand, instead of keeping more infrastructure running than we need.
• We are small enough to actually care about these details. We are not going to write a million words about sustainability and then hide behind big-company language and we are not going to overpromise, we will however build tools with sustainability in mind.

Ideas or feedback? Reach out to us at founders@braaand.ai.