
AgentRel — Devrel For Agents
Manicule is building AgentRel: the systems that help companies become discoverable, cited, and recommended by AI agents.
For the last twenty years, companies optimized for people searching Google. The next interface is an agent answering a question, comparing products, or deciding what to recommend. We want to understand how those decisions happen and build the infrastructure companies need to influence them honestly.
That means solving two hard problems.
Ideally, you write excellent TypeScript, but you also understand the fundamentals of GEO—or have spent enough time studying LLMs to form a view on how they surface sources and recommend brands. You care about writing quality and can tell the difference between a useful technical explanation and fluent filler.
This is not a role where you receive finished specs and close tickets. You’ll work directly with our customers to understand the problem, decide what to build, and own it through production.
What you’ll do
Who we’re looking for
Your first 30 days
Probably the highest thing on my list is building an extremely durable and powerful context system.
Our agents need to gather context from dozens of sources, from a company’s Slack and Linear to GitHub and its docs, and use it to produce amazing work on the first try. These sources contain huge amounts of noisy, constantly changing information. You’d build, eval, and improve this production infrastructure.
Bonus points if you’ve worked in context management before.
Why now
We’re growing very fast - went from 0 to $30K MRR in 2 months (yes, actual real revenue). You’ll be early enough to have meaningful contribution to the actual product direction and lots of autonomy.
Also, we’re 2 chill young cofounders. You’ll like working with us.
Details
Manicule is trying to solve a problem most people have given up on: getting AI to write well.
Everyone can get a model to produce words, but it feels so empty. Almost no one can get it to produce writing with a point of view, a logically sound structure, and a unique tone.
We want to solve these problems at scale. That means turning the judgment of strong writers into something repeatable that an AI can actually run on. Part of the work is engineering: prompts, evals, the harness around them. Part of it is editorial taste. Most of the work is experimentation, because nobody has the answers yet, and the frontier moves every few weeks.
We also think the audience for writing is changing. More and more, what we produce is read by agents as much as by people, and we're already working on catering to them.
We're small, we hire rarely, and people stay. If the idea of teaching a machine to write the way you'd want to read excites you more than it worries you, we should talk.