Strategic Proposal
Simple AI Vertical Application Transformation Plan
Use the existing team as the seed for an AI Application Factory: rapidly incubate industry agents, vertical software, and automation systems, then connect them to robotics, intelligent hardware, and a long-term embodied AI platform vision.
1. Core Thesis
Automotive and robotics are both capital-intensive, hardware-heavy, and long-cycle businesses. For a team of several dozen people, continuing in these directions without strong sales or major funding can create serious financial pressure.
AI vertical applications are a more practical near-term opportunity: asset-light, fast to validate, easier to prototype, and easier to iterate. More importantly, this can become a strategic bridge back to the capital market: build applications first, accumulate data and workflows, create an industry agent platform, then integrate those capabilities with robotics and intelligent hardware.
2. Strategic Positioning
The company does not need to completely abandon automotive or robotics. Instead, AI applications can become the revenue engine, customer entry point, and data entry point. Industry workflows can become the moat, while robotics and intelligent hardware become the future execution layer.
Externally, the company can be positioned as building an AI Agent Operating System for traditional industries: first digitizing and automating marketing, sales, education, restaurants, operations, and enterprise management, then bringing these AI agents into robots and intelligent devices to close the loop from digital intelligence to physical execution.
3. Why Vertical AI Applications
Lower Technical Barrier
The company does not need to train foundation models from scratch. It can build products on top of OpenAI, Claude, Gemini, DeepSeek, Llama, and other existing model platforms.
Clear Market Demand
Small and medium-sized businesses often lack marketing, customer service, sales automation, education tools, operations systems, and analytics capabilities.
Fast Team Transformation
With vibe coding, AI tool training, and product prototyping workshops, existing employees can quickly build websites, tools, internal systems, and automation demos.
Parallel Experimentation
The team can be divided into multiple vertical pods. Each pod validates one market in 4-8 weeks, then the company doubles down on the most promising products.
4. Suggested Team Structure
Divide the team into 6 vertical pods, creating a more symmetrical set of strategic tracks. Each pod should not merely build a single tool; it should accumulate templates, data, customer cases, and reusable agent workflows around a specific industry or business function.
Education Team
AI tutor, homework grading, course generation, college application assistant, language learning assistant.
Customers: schools, tutoring centers, parents, education agencies.
Marketing Automation Team
Ad copy, short video scripts, social media content, email marketing, customer follow-up, SEO content.
Customers: small businesses, restaurants, real estate agents, law firms, clinics, e-commerce sellers.
Restaurant Team
AI menu optimization, review analysis, promotion planner, inventory forecast, employee scheduling, delivery platform assistant.
Customers: restaurants, cafes, drink shops, food brands.
Sales and Customer Service Team
AI chatbot, lead qualification, CRM automation, proposal generation, customer profile analysis.
Customers: B2B companies, service businesses, e-commerce companies.
Enterprise Automation Team
Document processing, meeting summaries, internal knowledge base, contract review, automated reporting.
Customers: traditional businesses, manufacturers, trading companies, consulting firms.
Healthcare and Local Services Team
Clinic appointment assistant, patient communication, health content generation, service quotes, customer follow-up, and local merchant automation.
Customers: clinics, dental offices, beauty and wellness providers, gyms, repair services, community service businesses.
5. Example Product: AI Marketing Copilot
AI Marketing Copilot can be one of the first flagship projects. It helps small business owners automate marketing planning, content generation, and customer follow-up.
Core Features
- Generate content for Instagram, TikTok, Xiaohongshu, Facebook, LinkedIn, and WeChat.
- Create ad copy, emails, SMS messages, and promotional scripts.
- Generate weekly or monthly marketing plans by industry and business type.
- Analyze customer reviews to extract pain points, selling points, and messaging angles.
- Connect with WhatsApp, WeChat, email, SMS, and CRM systems for semi-automated outreach.
Business Model
- SaaS subscription: $99-$499/month.
- Custom enterprise projects: $3,000-$20,000/project.
- Marketing service + software package, charged monthly.
- Paid industry templates for restaurants, education, real estate, beauty, and e-commerce.
6. Execution Roadmap
AI Training
Vibe coding, AI-assisted development, model APIs, product prototyping, user interviews, and MVP development.
Pod Prototyping
Each team produces customer definition, pain point analysis, prototype, demo/MVP, business model, and 3-5 customer interviews.
Market Validation
Select 2-3 promising products for trials, paid pilots, sales testing, iteration, and outsourced production engineering.
Fundraising Story
Position the company as a vertical AI Agent platform for SMBs and traditional industries, with long-term embodied AI expansion.
7. Investor Narrative
The company is not simply abandoning automotive or robotics. It is upgrading from a hardware-focused company into an AI-native company.
The larger narrative is that the company is moving from an intelligent hardware builder into an industry intelligence infrastructure company. In the short term, vertical AI applications can generate revenue. In the medium term, the company can build repeatable solution platforms. In the long term, software agents, data, workflows, and robotic execution can be connected into embodied AI and autonomous automation systems.
This is not a small software outsourcing story. It is a market-entry strategy through the application layer, a way to build organizational learning through customer scenarios, and a path toward a larger Physical AI and Autonomous Enterprise vision.