AI Co-Founder for Behavior Prediction
Budget: $25 – $50 CAD
The concept is clear: an MVP that ingests real-world chat conversations and returns accurate, scalable behavior-prediction insights grounded in psychoanalytic theory. I have the market validation, a book-backed personal story, paying customers in sight, and investors waiting for a technical roadmap. What’s missing is the engineering force to turn vision into code.
Here’s the immediate focus:
• Build a cloud-native, multimodal architecture with chat analysis as the v1 core.
• Lay foundations for voice and video streams while keeping every component horizontally scalable (microservices, container orchestration, autoscaling).
• Integrate state-of-the-art NLP—think transformers, RAG, vector search—for prediction models that can evolve as new data arrives.
• Deliver an intuitive dashboard or API that demonstrates real business value to early design partners.
Scalability is paramount, so I’m leaning toward Kubernetes, Python, PyTorch/TensorFlow, and a modular data pipeline that can be stress-tested from day one. First release has no hard deadline; getting the architecture right outweighs rushing delivery.
The arrangement is pure equity with a shared, long-term vision. Vancouver is home base, yet remote collaboration works—as long as we can whiteboard over Zoom and occasionally grab coffee when schedules align.
If architecting a meaningful, large-scale product excites you as much as shipping clean, production-ready code, let’s talk and see whether our goals align.
Here’s the immediate focus:
• Build a cloud-native, multimodal architecture with chat analysis as the v1 core.
• Lay foundations for voice and video streams while keeping every component horizontally scalable (microservices, container orchestration, autoscaling).
• Integrate state-of-the-art NLP—think transformers, RAG, vector search—for prediction models that can evolve as new data arrives.
• Deliver an intuitive dashboard or API that demonstrates real business value to early design partners.
Scalability is paramount, so I’m leaning toward Kubernetes, Python, PyTorch/TensorFlow, and a modular data pipeline that can be stress-tested from day one. First release has no hard deadline; getting the architecture right outweighs rushing delivery.
The arrangement is pure equity with a shared, long-term vision. Vancouver is home base, yet remote collaboration works—as long as we can whiteboard over Zoom and occasionally grab coffee when schedules align.
If architecting a meaningful, large-scale product excites you as much as shipping clean, production-ready code, let’s talk and see whether our goals align.