Concept

AssetLint is a SaaS platform that acts as an automated "Technical Artist" for 3D development teams. It analyzes 3D models against project-specific technical requirements and instantly provides a detailed QA report. Teams can define "profiles" for different target platforms (e.g., "Oculus Quest 2", "WebGL", "Unreal Engine 5"), and AssetLint ensures every asset submitted by artists conforms to those standards before it enters the main development pipeline. This eliminates the manual, time-consuming, and error-prone process of technical asset review.

Value Proposition

AssetLint saves 3D production teams hundreds of hours and prevents costly performance issues by catching technical errors in 3D assets early. It empowers junior artists to create compliant assets, frees up senior artists from tedious QA tasks, and streamlines the handoff between the art and engineering departments.

AI Integration

AssetLint uses a combination of traditional geometric algorithms and machine learning models to analyze 3D assets.

• Core Analysis Engine: Uses algorithms to check for quantifiable issues like polygon count, non-manifold geometry, texture dimensions, and correct file format structure.
• AI-Powered Heuristics: A machine learning model, trained on thousands of production-ready and broken models, identifies more nuanced issues that are harder to define with simple rules. This includes detecting bad topology flow that could cause issues with animation, or identifying poorly optimized UV layouts that waste texture space.
• Future AI Features: The platform is designed to later incorporate generative AI for "one-click fixes," such as automatically optimizing a model's polygon count or re-laying out its UVs to be more efficient.

First MVP

The first MVP will be a simple web application with the following features:

• User account system for artists and developers.
• Ability to create a "Validation Profile" with a simple checklist of rules (e.g., Max Triangles, Max Texture Size, Must be Manifold).
• A drag-and-drop uploader for .gltf and .fbx files.
• A backend processing job that runs the asset against the selected profile's rules.
• A clear, shareable report page that shows a pass/fail status for each rule and highlights specific errors.

This focused MVP can be built quickly by a small team and immediately delivers value to its first users, validating the core business concept.

Unique Selling Points

• Platform Agnostic: Works with standard file formats (.fbx, .gltf, .obj), making it a central source of truth regardless of the 3D creation tools your team uses.
• Focus on Workflow, Not Just Creation: While competitors focus on AI-based 3D generation, AssetLint solves the less glamorous but critical post-creation/pre-integration problem that every single 3D studio faces.
• Collaborative: Reports are easily shareable, improving communication between artists, leads, and engineers with clear, objective feedback.
• Scalable: Starts as a simple validator and can grow into an AI-powered optimization and automation suite for the entire 3D asset pipeline.

Core Concept

InboxGuard is a proactive email deliverability API for developers and marketers. Before you send an email through a service like Resend or SendGrid, you make a single API call to InboxGuard. Our AI analyzes the email's content, HTML structure, headers, and links, predicting its likelihood of landing in spam. It returns a simple score and, more importantly, a list of specific, AI-generated suggestions to improve inbox placement.

Value Proposition

We turn the black box of email deliverability into a simple, automated pre-flight check. Stop guessing why your emails go to spam. InboxGuard tells you why before you send and tells you exactly how to fix it, protecting your sender reputation and maximizing your email ROI.

How It Incorporates AI

• Predictive ML Model: The core of the service is a machine learning model trained on millions of emails and their real-world delivery outcomes (inbox, promotions, spam). It analyzes over 100 features from email content and headers to generate a highly accurate "inbox placement score."
• Generative AI Feedback: Instead of cryptic error codes, we use a generative model (fine-tuned for email best practices) to provide clear, human-readable feedback. For example: "Your subject line 'BIG SALE!!!' uses excessive capitalization and is likely to be flagged. Suggestion: 'Our Summer Sale is Here'." or "This image has no alt-text, which can negatively impact your score. Suggestion: Add alt-text describing the product."

Unique Selling Points

• Proactive, not Reactive: We integrate into your sending workflow to prevent problems, unlike existing tools that only test and report after the fact.
• Developer-First: It's a simple, fast REST API designed to be a single extra line of code in any email-sending script or application.
• Actionable & Prescriptive: We don't just give you a score; our AI tells you precisely what to change, saving hours of guesswork.

First MVP Product

The MVP will be a single API endpoint that accepts an email payload (JSON format with subject, HTML body, from_address, etc.). It will return:

• A simple deliverability score (e.g., 95/100).
• An array of suggested improvements, each with a "problem" and a "suggestion" field. The initial focus will be on content analysis (spammy words, HTML errors, link quality). A simple web dashboard will allow users to sign up, get an API key, and see their usage.

Potential Impact

InboxGuard can become an essential utility for any business that relies on email. For startups like smol.ai, it would have provided an immediate, automated way to diagnose and fix deliverability issues when launching their new domain, saving them from having to ask their users for help to escape "spam hell."