Plan your first agent

Marketing agents that know how you sell

AI engineering for lean B2B marketing teams. We build your agents and skills, powered by a repo of GTM context your AI tools can read.

See the agents

Launch your first agent in as little as 2 weeks.
Every agent and skill is powered by a GitHub repo you own.

Results for B2B PLG DevTool Client

3.4×
organic qualified signups
5.3×
guide traffic
2.8×
qualified signup rate
Aptible Case study: 3.4× organic qualified signups →

Agents

One context layer. Dozens of marketing jobs.

Build agents that run continuously (or via trigger) and skills your team calls when they need them, all grounded in the same ICP, positioning, proof, voice, and customer evidence.

02

Create Demand

03

Capture Demand

Explore all agents & skills →

Built on Context

Most marketing agents start empty

They can write. They can't know that you stopped saying "platform" in March, that one customer quote is cleared for public use and the other isn't, or that your team settled on a new definition of qualified signup 2 weeks ago.

So somebody digs up the context that re-explains your company each time they prompt ChatGPT. Then, of course, fixes what it produces every. single. time. It's why AI output keeps arriving generically correct and specifically wrong.

We put your context in a GitHub repo you control that your AI tools can read and update. Your messaging and positioning, the customer proof you're allowed to use, each person's voice, the definitions, and the standards you want to evaluate each draft by all in a tightly organized repo your team owns. Every agent and skill after that starts from it, which allows us to move faster after the first one.

Always a repo you own. Fix a customer story in one file and every agent or skill that references it is corrected automatically. Plus, your team's AI tools along with your vendors can work from the same repo.

Run by Code

The code decides. The model writes.

Most of the ways your team uses AI have it backwards. Someone drops a lengthy prompt into ChatGPT or Claude along with whatever context files are saved on their desktop. But what happens when the prompt doesn't follow your standards, or the context is out of date?

Manual workflow

Writing it yourself

Claude

+ChatCoworkSonnet 5 High

10 filesDraft the Northwind Security case study…

Northwind Security made audit readiness a weekly habit

The team had a reliable product. Its audit process was still a scramble.

Before Northwind, evidence lived across tickets, spreadsheets, and calls. Each audit began with the same hunt for owners, screenshots, and the latest control language.

“The workflow finally gave us a repeatable audit path.” — Northwind Security

In the first quarter, 42 active accounts cut audit prep from 6 weeks to 9 days.

Same job, with Protocol
Skill Protocol

Case Study Builder

Claude

/case-study

Context resolved

messaging/components v6

proof/quotes 4 of 7 cleared

× proof/quotes — 3 withheld not cleared for public use

proof/audit-evidence-workflow cleared for public use

enrichment/company-profile v3

product/top-features v5

Model writesone generation step

Case study ready to editNorthwind Security · all published proof traced

/case-study Case study builder

/webinar-recap Webinar recap package

/announcement-cascade Announcement cascade

How can I help you today?/case-study

+ChatCoworkSonnet 5 High

Enter prompt inputs

case-study

Turn the verified story into a case study.

Customer *

Northwind Security

Key takeaway *

Audit evidence now has one repeatable collection path

CancelAdd prompt

Same case study. One step is a model. The rest is code you can read.

You ask for a case study, your rules check what's cleared, approved proof loads, the model writes, and you see what was held back.

Your team uses one skill that provides a solid draft that meets all your expectations. Nobody has to remember to drop the latest docs or describe the output just right. Your team focuses on editing and publishing instead of crafting better and better prompts and fixing inevitable slop.

Writing the story should be the model's job. Deciding what to say and how to say it should be yours.

Live in Two Weeks

Two weeks to the first agent. And we build each one faster than the last.

We do the work with your team to build out the repo of just the context your first agents and skills need so we can launch quickly. Each subsequent agent comes faster, because it reuses (and if necessary adds to) the context repo.

  1. Week 1

    We push what your first agent needs to your GitHub repo.

    Interviews, your existing docs, customer calls, workflows, and whatever's in people's heads.

  2. Week 2

    Your first agent goes live.

    You pick a marketing job your team wants to do more of, and we launch a scheduled (or triggered) agent or a reusable skill that works inside your team's existing AI tools.

  3. After that

    New agents in as little as a day.

    The context is the hard part: pulling it out of Notion or Google Docs, your CRM or your data warehouse, even your team members' heads. Each new agent inherits the same context and we add whatever else it needs, so the foundation gets stronger over time.

Proof

Aptible

B2B SaaS · PLG · DevTools

Aptible grew organic qualified signups 3.4×

Aptible didn't need a better model. Instead, it needed to put SEO data, customer calls, subject matter expert interviews, and its high quality standards all into one place an agent could read.

3.4×
organic qualified signups
5.3×
guide traffic
2.8×
qualified signup rate
I'd used ChatGPT and Perplexity and written longer and longer prompts, and still got mediocre content every time. Since we started drafting from our Protocol context layer, we keep setting records for top of funnel and converted customers.
Kelsey Ellis Kelsey EllisSenior Product Marketing Manager, Aptible
Read the Whole Case Study →

What the agent produced

Aptible guide cover: HIPAA compliance for digital health startups
Aptible guide cover: HIPAA-compliant AI toolsAptible guide cover: MCP security: a guide for developersAptible guide cover: HIPAA AI security: a guide for developersAptible guide cover: Heroku alternatives: how to evaluate your options
Guides published on aptible.com

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What should your marketing team be able to do more of?

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