Fowel automatically reviews documentation in every GitHub pull request – catching inaccuracies, missing context, outdated code samples, and structural gaps before they reach production. Install in 30 seconds and scale across unlimited repositories.
Last week I got hit by a client with "sorry we took all the docs work your team did over the last 3 months which was great, fed it to Claude Code and we're good going forward". $5k+ MRR up in smoke.
I think that's when I might have finally gotten past the denial stage, that AI is coming for my company, Hackmamba, a technical content agency, even though we're focused on authenticity and technical creativity.
As an engineer and technical writer (now double-screwed I guess) I'm a big purporter that AI is like electricity, making things better, but the last 2 weeks have been, shocking (pun intended). Maybe I'd just been slow, doing too much talking and less doing.
So what did I do after J hit me with the contract cancellation line, I started looking for ways to do more with AI without crossing the blurry line that is generating slop. As a former PM, the first culprits of my evaluation were anything we spent more than 10 hours per month doing.
Technical reviews came up first. We work in teams shipping fast and need to get docs ready for developers and agents. Documentation is the ground truth before MCPs etc take over. So we spend a good amount of time reviewing docs PRs sent in by technical writers for accuracy, tone, shit code, typos, consistency with the overall style, persona match, clarity for sales and marketing usage etc.
So I did the next logical thing a software engineer (bless that job title) would do; I made a system prompt with everything we know and documented internally, plus everything I know about docs, individual frameworks, patterns etc. Then I built Fowel.ai (should sound like vowel, not foul) with it to handle deep GitHub PR reviews on documentation that was both written by a human or AI generated.
Frankly, I don't care at this point. If the end goal is to ship great docs for humans and agents, why care deeply about who wrote it. AI agents don't care, and I've worked with writers worse than GPT 5.4. We likely won't need documentation in the future too when we fix agent<>agent comms 🫠
Maybe I'm cooked for making such mental shift towards building the guardrails and quality enforcements. Time will tell.
We've seen a huge reduction in time to get PRs into production by about 80%, which I love. Do try Fowel if you're looking at the speed of getting great docs content out, and I appreciate any feedback shared. It's free to use. Thanks in advance and let me know if this is shit too.
This is one of the most honest founder stories I’ve read here @WiIIiam. AI isn’t just creating opportunities—it’s forcing companies to rethink their entire value proposition.
Fowel focusing on AI-assisted documentation reviews and quality enforcement is an interesting direction. Curious — what kind of documentation issues does it catch most often that humans usually miss? 🚀
This resonates hard. I maintain 440+ open-source developer tools and the docs review bottleneck is real — outdated code samples slip through PRs constantly. The fact that Fowel catches structural gaps (not just typos) is the key differentiator. Most linters stop at formatting. Curious: does it handle multi-language docs, like when a project has both English and Japanese README files?
Since I rely so heavily on Claude to help me with development work these days, I find myself questioning if the documentation that I write up is detailed enough or if I missed a key detail.
Is Fowel able to build upon context across different repositories that share dependencies?
An 80% reduction in doc review time is ambitious, but I'd accept it for certain use cases. Most of the time reviewing docs is spent on consistency checks and finding outdated references, and that's tedious work a human shouldn't be doing. I maintain docs for a SaaS with 5 language localizations and the drift between versions is constant. Does this handle multilingual documentation or just English?
The agency-to-SaaS pivot triggered by a client replacing your work with Claude Code is painfully honest and exactly the kind of signal that validates the product direction — if AI can write the docs, the value shifts to reviewing and enforcing quality standards. Does Fowel learn from a repo's existing documentation style over time, or does each PR review start from a generic baseline regardless of how much context already exists?
The 80% review time reduction tracks with what I've seen when you encode institutional knowledge into the prompt rather than relying on generic LLM behavior. How are you handling style guide drift over time? Like when a client updates their tone or deprecates certain terminology, is that a manual prompt update or do you have a way to version that context?
This is honestly such a smart pivot. Doc review is one of those things that eats up so much time but nobody really talks about it. We've had PRs sit for days just because the docs changes needed back and forth on tone and accuracy.
Curious about one thing though, does Fowel handle docs that reference external APIs or third party libraries? Like if a code sample imports something from a package that just shipped a breaking change, would it flag that?
fmerian,
Last week I got hit by a client with "sorry we took all the docs work your team did over the last 3 months which was great, fed it to Claude Code and we're good going forward". $5k+ MRR up in smoke.
I think that's when I might have finally gotten past the denial stage, that AI is coming for my company, Hackmamba, a technical content agency, even though we're focused on authenticity and technical creativity.
As an engineer and technical writer (now double-screwed I guess) I'm a big purporter that AI is like electricity, making things better, but the last 2 weeks have been, shocking (pun intended). Maybe I'd just been slow, doing too much talking and less doing.
So what did I do after J hit me with the contract cancellation line, I started looking for ways to do more with AI without crossing the blurry line that is generating slop. As a former PM, the first culprits of my evaluation were anything we spent more than 10 hours per month doing.
Technical reviews came up first. We work in teams shipping fast and need to get docs ready for developers and agents. Documentation is the ground truth before MCPs etc take over. So we spend a good amount of time reviewing docs PRs sent in by technical writers for accuracy, tone, shit code, typos, consistency with the overall style, persona match, clarity for sales and marketing usage etc.
So I did the next logical thing a software engineer (bless that job title) would do; I made a system prompt with everything we know and documented internally, plus everything I know about docs, individual frameworks, patterns etc. Then I built Fowel.ai (should sound like vowel, not foul) with it to handle deep GitHub PR reviews on documentation that was both written by a human or AI generated.
Frankly, I don't care at this point. If the end goal is to ship great docs for humans and agents, why care deeply about who wrote it. AI agents don't care, and I've worked with writers worse than GPT 5.4. We likely won't need documentation in the future too when we fix agent<>agent comms 🫠
Maybe I'm cooked for making such mental shift towards building the guardrails and quality enforcements. Time will tell.
We've seen a huge reduction in time to get PRs into production by about 80%, which I love. Do try Fowel if you're looking at the speed of getting great docs content out, and I appreciate any feedback shared. It's free to use.
Thanks in advance and let me know if this is shit too.
I don't mind brutal feedback.
William.