ai-engineerv1.0.0

Deploy a RAG-based chatbot using open-source LLMs and vector databases to answer customer queries by retrieving relevant documentation. The system reduces support tickets and improves response time, applicable in e-commerce and SaaS industries.

Use computer vision and NLP to extract text from scanned invoices, classify them, and perform NER to capture key fields (dates, amounts). This streamlines accounts payable workflows for finance and insurance companies.

Implement a YOLO-based model on edge devices to detect defects or safety violations on production lines. The system alerts operators immediately, reducing waste and accidents in manufacturing.

Build a recommendation system using collaborative filtering and deep learning (e.g., transformers) to suggest content based on user history. This increases engagement and subscription retention for streaming platforms.

Train a time-series model on sensor data to predict equipment failures before they occur. This reduces downtime and maintenance costs for industrial IoT applications in energy and logistics.

Offer pre-trained models via API endpoints (e.g., LLM inference, image classification) with pay-per-use or tiered subscription pricing. This allows customers to integrate AI without infrastructure overhead.

Provide end-to-end AI solution development for enterprises, including data preparation, model training, and deployment. Charge project-based fees or retainers for ongoing maintenance and fine-tuning.

Integrate ML models into existing SaaS platforms as premium features (e.g., smart search, automated tagging). Customers upgrade to higher tiers to access these capabilities, increasing per-user revenue.