Meet Moe. Moe is the mascot and AI guide we designed for our studio's website. We started with the character: a soft, friendly cube, modelled, lit and rendered in Blender. All six expressions (happy, wink, wow, thinking, chill, curious) are posed from the same 3D model, so he stays consistent across the site and our socials.

On the site, Moe is rebuilt in code. He waits in the corner, blinks, follows your cursor with his eyes, and dozes off when ignored, then opens into a chat that helps founders work out what they need: which package fits, what's included, the timeline and how soon work can start. His answers come straight from the site's CMS, so they stay current when pricing changes, and he hands off to the team when a question needs a person.

Need a mascot for your brand or an AI assistant on your website? Send us a message here; we'd love to build yours.

That 3D mascot branding combined with an AI-powered product studio concept is brilliant, Ibrahim! The clean UI and character expressions give the whole agency presence a ton of personality.

Most inbound bottlenecks aren't caused by low lead volume—they happen during triage.

When inquiries roll into a Google Sheet or CRM, operations teams often waste 10–15 minutes per submission reading through messy paragraphs, deciphering budget mismatch, and manually deciding who gets an immediate call vs. a standard rate sheet.

Instead of adding a bulky CRM add-on, I documented a lightweight 3-step automation pipeline built inside Make:

Intake & Extraction: An active listener triggers immediately upon a new submission, extracting core variables: client scope, target deliverables, timeline, and stated budget.

Context-Aware Evaluation (Gemini 2.5 Flash): The payload passes through an AI evaluation node with strict guardrails:

Data Return & Clean Pipeline: Appends the structured classification directly back to the master operational tracker, keeping the sheet filterable and clean.

The outcome: The sales and operations team can filter the entire board by priority status in two clicks, cutting first-touch response time to minutes without manually reading every single submission.

I’ve documented the full scenario map, execution logic, and data schemas below.

Curious how other agency operators and automation leads here handle their lead enrichment layer—are you doing AI scoring at the form level, or later in the CRM pipeline?:

For the budget gate, I'd keep an unstated budget separate from one explicitly below your minimum. Does the Make route ask for clarification when the amount or currency is missing, so a potentially suitable lead doesn't fall into Cold by default?