Think first, then scale
Only a lot does not help much
Three tools in three steps
3 steps to the highest quality:
3 tools for speed and scale:
- C-01/ CONSULTING
Strategy that pays off
Let's take a thorough look at what you've been doing so far. In a structured audit, we'll find out what you can tell a credible story about. We define topics, tonality and style that best represent your brand. And build a strategy from there - for authentic content that you can scale day by day.
- C-02/ CREATION
The foundation for scaling
With me you can create what you are still missing. A name, a claim or a story for your brand? Individual creatives such as social media posts or ads? With the right P-01/ Prototypes In your collection, you set the benchmark for quality and can ensure that you scale sensibly. No matter who does it in the end - people or machines.
- C-03/ COLLABORATION
Human-in-the-loop. You often hear this demand. And rightly so: humans have to make decisions because AI alone cannot be trusted. Today, however, it is also true that humans alone are not fast enough. So bring on the machines-in-the-loop. With me you get: The P-02/ Promptsthat make the most of what you can expect from AI. And which P-03/ Processes - how people and machines work together.
If we can imagine it, we can do it
I have been working with artificial intelligence since 2018. What I see time and again is that humans and machines together can do what each of them cannot do alone. Here are five areas of application where this is most beneficial:
The right AI for the right purpose
Of the five examples above, only three are based on large language models, and even then only partially. Language models from the field of machine learning are currently in high demand. They are indeed an excellent choice for claims & headlines and for transforming content. But which model solves your task best - GPT-4, Bard, Llama or Claude?
However, language models alone are not enough. First of all, your content has to enter the machine as speech. This is where Whisper & Co help with transcription. Then the AI has to remember all the data. This is where the decision has to be made: Are you primarily interested in extensive content? Then rely on embeddings and perhaps a vector database. Do you need to be able to quote verbatim? Then choose a model with a long context window.
When it comes to naming and product texts, old-school algorithms still solve some tasks better. When you work with me, you don't have to worry about that. Because together we will not only find the ideal division of labor between man and machine, but also the right software for your task - beyond the hype du jour.
Where do you benefit most from artificial intelligence?
I'm happy to take 15 minutes and talk to you about ways you can start implementing next week.