GenAI Expertise Missing from Your Content Strategy?

Two things: quality and customization.

Quality is relative. Most brands have a vision of quality in their content, and some of the popular AI tools can already cater to their needs. But when you require something more — when you need to meet an even better quality of writing or address certain standards of compliance — then those tools are too rigid for your needs.

Yes; it’s rather straightforward. Emulating voice and tone is easy, but service providers and vendors usually require a body of existing work to use as reference.

What I can do is a step beyond that due to the 1:1 nature of the consulting relationship: I can help mimic a subject matter expert’s specialization and use that in the content.

This is done primarily through meetings and interviews to understand what constitutes the expert’s knowledge and knowhow, and then provide a similar intuition to genAI LLMs. A typical scenario is where you work with an SME whose insight or experience is necessary for your content. We essentially copy the part of their expertise required to generate your content. This frees up your SME to focus on other tasks, and if ever needed, just lightly post-edit AI-generated content.

We can also develop a collaborative environment where an SME or user works with an expert chatbot to develop content — the best of both worlds where human expertise actively informs AI-powered automation.

What’s great about highly regulated industries is that they clearly detail the regulations. It might be hard to process for human writers, but it’s much easier for AI.

Technically, Retrieval Augmented Generation (RAG) is the foundation of this. Through RAG, AI can be forced to ground its output on strict guidelines. Naturally, guardrails included in the prompt or system instructions of specific bots are also a necessary measure. Additionally, specialized checkpoint agents are usually employed at specific stages of the content pipeline to drastically reduce the human checks required.

For LLMs, the fact-checking is heavily reliant on the start of the pipeline, as opposed to being used mainly at the end. Yes, there can still be checkpoints in post-processing, but the key is due diligence in providing grounding that the AI will use in its output. Via proper and tested Retrieval Augmented Generation (RAG) methods, grounding prevents hallucinations and guarantees accurate AI output.

Additional measures can be put in place where required.

As long as the output does not constitute plagiarism nor run afoul of any regulatory guidelines, there is practically no legal implication for your side. This is also the reason why we rely on the top commercial generative AI providers like OpenAI or Google. They shoulder most of any legal ramifications for the way their AI works.

Of course, accountability and transparency are best practices from your side. It’s best to be transparent when AI is involved and how much it’s involved. And regarding building your pipelines, it is also our responsibility to guarantee that we hold the right to use any material employed for AI training or reference.

If a single piece of content takes you or a member of the team a few hours or even days, AI can shave that time down to minutes. To give you a more concrete analogy: a full AI pipeline can develop a high quality draft for a 5,000-word white paper in less than 5 minutes — this includes research, outlining, marketing message alignment to buyer personas, and the actual writing.

The time saved is variable, depending on the task involved or the work being done. A collaborative chatbot, for instance, can speed up research, ideation, conceptualization, and drafting. But the overall time will still depend on the user. Still, the magnitude of time saved will always be considerable.

The primary upfront cost is the one-time consulting fee, which can be per project, per hour, or a combination, depending on our agreement.

You can reasonably expect a project consultation fee that results in a customized pipeline you can apply to your content creation via your own platform or, if possible, integrated into your CMS or other SaaS. Additional per hour fees might apply to meetings and calls depending on your needs. Once your pipeline is developed, you can choose to pay a low flat fee per month for me to maintain it, or I can hand it over to you completely, and you can pay your AI provider of choice directly for API access to the engines that power your pipeline. If you choose the latter, you then just pay your AI provider for your usage monthly.

As soon as the pipeline is done, you can use it right away. If you require integration with your existing tech stack, that will take a bit more time.

From initial consultation to deployment, the genAI content pipeline can be up and running in as little as 4 to 6 weeks. If a pre-built engine can be customized for your use case, it will be significantly faster — up to less than a week depending on requirements.

This depends on your needs, as more complex endeavors like cloning SME expertise (a collaborative and iterative process) requires more flexible scheduling.

You just need to know your own content strategy and requirements. You don’t need to know much about AI at all, though of course in our collaboration I can help you gain a grasp of concepts where required.

If you choose to pay your own AI provider for the engine that runs your pipeline, I will check up on your developed pipeline once a month for 6 months and handle any updates required free of charge. For any issues that arise like bugs and behaviors outside expectations or specs, I offer to fix any problems free of charge for the first 3 months after deployment.

If you decide to retain me for a flat fee monthly to handle your pipeline, I shoulder the AI provider costs and support is generally included for as long as you engage in the monthly retainer arrangement, with few exceptions.

Get in touch. Not every content need can and should be answered by AI, and I would be the first to tell you if someone else can solve your issues sufficiently well or even better. But first we need to come to an understanding of what you need, and if and how I can translate that into a genAI content pipeline.

So first, we need to set up a time to talk