I’ve been building a tool to fix my biggest bottleneck as a solo dev: marketing. Not as a startup, just something for myself.
The core problem is pretty straightforward. I can build pretty fast, but distribution still feels like guesswork. There are too many directions to go in, and the feedback loops are slow and noisy. Instead of trying to “learn marketing” the traditional way, I’m experimenting with something else: removing myself from most of the decision-making entirely with a system where I just review and approve what gets done.
While somewhat complex behind the scenes, the system I’m building is pretty simple in concept. I give it a product URL, and it tries to figure out what the product is, who it’s for, and what it competes with. From there, it generates positioning, content ideas, and suggested distribution channels. It writes actual content, articles, replies, outreach drafts, and then surfaces everything in a queue where I either approve it or ignore it.
The goal is to turn marketing into something closer to a PR review queue instead of a creative discipline.
What’s been interesting over the past few days though, has been learning from building the AI side of it, especially about cost and where things actually break.
My initial instinct was just to use the “best” model for everything. Let's just call that strategy sub-optimal.
While it's perhaps pretty obvious that not all AI tasks are equal, I've really been schooled on the degree to which this is true. There’s a big difference between things like writing an article or an outreach email versus summarizing a page or scoring whether a Reddit thread is relevant. One actually needs quality and nuance, the other really just needs to be roughly correct.
After some trial and error as to what goes where, I ended up splitting responsibilities like this:
- heavier writing (articles, positioning, outreach) goes through a higher-quality model
- EVERYTHING else (summarization, scoring, extraction, filtering) goes through a MUCH cheaper, faster one
Surprisingly - at least to me - early estimates say this change alone probably cut costs by something like 60–80% without noticeably affecting output quality. Most of the system doesn’t need intelligence. It just needs to be roughly correct, quickly.
While I wasn't blind to the fact that output is generally more expensive than input due to requiring more sequential computational power, the degree to which this is true was another 'gotcha'. Long-form content adds up fast. And it’s rarely just one pass. You generate an outline, then a draft, then maybe refine it, run an SEO pass, maybe regenerate parts of it. Suddenly one “article” is multiple expensive calls stacked on top of each other.
If you don’t put guardrails around that, costs drift in a way that’s not immediately obvious.
Regeneration is probably the worst offender. If you allow unlimited “try again” loops, you’ll burn money quickly. The fix isn’t really technical, it’s more product design. Limit regenerations. Make the first output good enough. Push toward editing instead of full rewrites.
On the flip side, cheaper models are surprisingly capable for a lot of the background work:
- scoring threads or conversations
- clustering keywords
- checking whether something sounds too promotional
- summarizing competitors
For those kinds of tasks, using a premium model is just wasteful.
Right now I’ve got a basic version working. It can ingest a product, generate content, and push things into an approval queue. The model routing is in place, so expensive tasks and cheap tasks are handled differently.
What’s still missing entirely is the harder part:
- a consistent distribution layer
- a feedback loop that actually adapts based on what works
- any real signal that this improves outcomes over time
It's early days yet and while I'm quietly optimistic, there are still a lot of open questions. To what degree is the increased consistency enough to justify any drop in content quality? Is “good enough content posted regularly” enough to move the needle? Can a system like this improve over time without becoming generic? And at what point does automation start hurting authenticity instead of helping?
If you’ve tried building anything even remotely similar, I’d be interested in what broke for you. Most of what I’ve seen either assumes you already know marketing or just generates content with no system behind it.
I’m trying to build something that sits somewhere in between.