I designed a Notion AI agent that helps turn messy client requests into a structured and manageable workflow. Instead of leaving important details scattered across messages, the agent organizes each request inside Notion, decides whether it should become a new entry or update an existing one, and creates a linked analysis page with the key details.
The analysis can include the request summary, deadline, risks, clarifying questions, next steps, and version history. When new information arrives, the agent keeps it as an update instead of overwriting the previous analysis, so the context is not lost over time.
I also made the system flexible, so users can adapt it to their own workflow - remove unnecessary sections, add missing ones, or keep the setup as simple as they need.
I intentionally built this agent in Ukrainian, even though the language is not officially supported, because the platform is quite popular in Ukraine and the competition rules allow this format.
One of our clients needed 70 ad video variants in a week, so we built an AI agent to do it in seven hours.
The build itself was a project. It required:
- Assembling a Claude agent connected to Remotion
- Building a knowledge base of the client's brand: brand guide, movement language, hook creation guidelines, past deliverables, briefing structure
- Teaching the agent to read the brief the way our team would
- Composing hook variants adapted for each aspect ratio (1x1, 9x16, 16x9)
- Assembling endcards and outputting files named and organized for QA
The first batch still needed heavy manual editing. The second, the agent assembled alone.
The third came out fully stitched (hook, body, CTA, disclaimer) needing only a review pass before shipping.
Each iteration meant testing what the agent got wrong, adjusting the skills, refining the knowledge base, and rerunning until the output matched the standard our team would ship.
When we sent the batch, the client feedback was: "The revised videos look great. We'll start using them immediately."
To show what this workflow can do, the video in this post was produced by this exact system.
If your business needs to ship ads at this volume, let's talk 👉 apta.agency/contact
Niva is a premium Framer template built for AI automation agencies, AI consultants, and automation-focused teams who want to attract serious B2B and enterprise clients. It helps you showcase your services, present real outcomes, and build trust with decision-makers through a warm, considered design that stands apart from the typical AI-agency aesthetic.
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Blog — a CMS-powered index and article template, ready for SEO content
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404, and legal pages — Privacy Policy, Terms of Service, and Cookie Policy included
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Interested in a custom version for your agency? Let's talk — DMs open.
Enter a fictional service request, see the itemized estimate, then open Operations to edit the quote, review it and export a printable quote or CSV. Records survive a reload in your browser. The demo uses fictional data and local storage; a shared live system would need its own agreed backend and access controls.
The point is to make a business workflow concrete enough to test: what the customer enters, what the team reviews, what changes status and what leaves the system.
I direct scope and review; GPT-6 Astra handles implementation and many checks. This is an original working sample, not a paid client case.
If an existing web workflow is causing friction, message me on Contra with the URL or redacted screenshots and what should happen. We can agree a small paid first milestone—such as a documented workflow review or a defined interface fix—with its deliverable, fee, inputs and timing confirmed before work.