Ai Solutions
Production-grade AI solutions tailored to your organization’s unique challenges. Whether working as an individual consultant or with a team, I design and deliver AI systems that scale—from research prototypes to production deployments serving millions of users.
Every solution is built with operational priorities in mind: cost efficiency, security, performance, and maintainability. Together we identify the optimal approach for each challenge and goal.
Core AI Solutions
RAG Pipelines
Build virtual assistants grounded in your proprietary data. Retrieval-augmented generation (RAG) combines large language models with internal knowledge bases to deliver accurate, context-aware responses with source attribution.
Example Use Case: Enterprise chatbot for answering internal policy queries across HR, compliance, and operations—returning relevant information with references to source documents.
Tech Stack: LangChain/LangGraph, Qdrant/Milvus, FastAPI, event-driven microservices
Model Fine-Tuning
When off-the-shelf models fall short, fine-tune LLMs and diffusion models on proprietary data to match your brand voice, domain terminology, and specific requirements. Production experience with multimodal models (Pixtral text+vision), embedding models, and custom diffusion models (Flux, Stable Diffusion).
Example Use Case: Generate consistent, on-brand marketing copy and technical reports using fine-tuned models that understand industry-specific terminology and house style.
Tech Stack: PyTorch, Hugging Face Transformers, vLLM, Weights & Biases, Azure ML/AWS SageMaker
AI Task Optimization
Build cost-effective, task-specific generative systems using advanced prompt engineering, sampling techniques, and structured outputs. Often faster and more economical than fine-tuning while delivering production-grade consistency.
Example Use Case: Automated quality checks on client deliverables—enforcing style guides, catching errors, and ensuring compliance before submission.
Tech Stack: GPT-4/Claude, structured output validation, FastAPI microservices
AI Agents
Deploy autonomous agentic systems that plan, make decisions, and take action using external tools. Production experience with CrewAI, Microsoft AutoGen, Semantic Kernel, and LangGraph. Built with enterprise-grade security, governance, and human-in-the-loop controls.
Example Use Case: Automated employee onboarding agent that sets up accounts, schedules meetings, assigns tasks, answers questions, and tracks completion—with manager approval checkpoints.
Tech Stack: CrewAI, LangGraph, NATS event bus, Kubernetes, comprehensive observability
Intelligent AI Workflows
Enhance structured business workflows with generative AI to handle tasks requiring reasoning, creativity, or context interpretation. Systems follow predefined orchestration patterns while incorporating AI for analysis, summarization, and content generation.
Example Use Case: Scale customer feedback analysis—automatically categorize comments, identify themes, generate executive summaries, and route insights to relevant teams with sentiment scoring.
Tech Stack: Event-driven microservices (NATS, Kafka), LangChain orchestration, PostgreSQL/CockroachDB
Workflow Automation
Automate rule-based, structured processes for maximum efficiency and accuracy. Execute fixed logic using integrated tools, APIs, and databases—running on-demand or scheduled. Ideal for predictable tasks without interpretation requirements.
Example Use Case: Streamline invoice processing—match incoming invoices against purchase orders, flag discrepancies, auto-approve within thresholds, and schedule payments with audit trails.
Tech Stack: Python, FastAPI, event-driven architecture, integration with ERPs and payment systems
Advanced AI Capabilities
Multimodal AI Systems
Deploy production-grade computer vision, OCR, and document intelligence solutions. Experience includes DeepSeek-OCR, SmolDocling, Tesseract, custom-trained YOLO models, Whisper speech recognition, and vision-language models for multimodal understanding.
Example Use Case: Automated document processing pipeline extracting structured data from invoices, receipts, and contracts—handling multiple languages, complex layouts, and table extraction at scale.
Tech Stack: PyTorch, Transformers, OpenCV, custom CNNs, Segformer, Vision Transformers
Computer Vision Solutions
Custom computer vision pipelines from segmentation to image generation. Production deployments include color-accurate fashion AI (DeltaE), custom object detection (YOLO fine-tuning), semantic segmentation, and generative models for creative workflows.
Example Use Case: AI-powered quality control for manufacturing—detecting defects, measuring tolerances, and classifying products in real-time with 99%+ accuracy on production lines.
Tech Stack: PyTorch, Segformer, YOLO, Stable Diffusion/Flux, DALL·E 3, custom training pipelines
Distributed AI Infrastructure
Design and deploy production AI infrastructure on Kubernetes with multi-GPU distributed inference, horizontal scaling, and comprehensive observability. Experience with bare-metal clusters, cloud providers (Azure AKS, AWS EKS, GCP GKE), and GPU cloud (Lambda Labs, RunPod).
Example Use Case: Multi-GPU inference cluster serving 100M+ users—distributed load balancing, autoscaling, A/B testing, and sub-100ms latency with Grafana/Prometheus/Loki monitoring.
Tech Stack: Kubernetes, NVIDIA Container Toolkit, vLLM, Hugging Face TGI, NATS, MinIO/S3, observability stack
AI System Modernization
Modernize legacy systems with AI-powered code analysis, migration planning, and automated refactoring. Production experience with agentic systems (ReforgeAI) that analyze Java codebases, generate documentation, create transformation plans, and execute Spring Boot migrations at scale.
Example Use Case: Legacy Java application modernization—automated analysis of 500K+ lines of code, dependency upgrades, framework migration to Spring Boot, security hardening, and comprehensive documentation generation.
Tech Stack: CrewAI multi-agent orchestration, AST parsing, code analysis tools, LangChain, GPT-4
Why Choose These Solutions
✅ Production-grade engineering – Event-driven microservices, fault tolerance, comprehensive testing
✅ Scale-first design – Built for horizontal scaling, distributed systems, millions of users
✅ Full observability – Grafana/Prometheus/Loki, Azure App Insights, CloudWatch monitoring
✅ Real-world validation – Deployed in production environments across multiple industries
✅ End-to-end delivery – From research prototypes to production deployments with proper DevOps
Ready to Build AI That Scales?
Whether you need a single solution or a comprehensive AI platform, I deliver production-ready systems built for real-world deployment.
📧 Email: gianpaolo.santopaolo {at} gmail.com
💻 GitHub: github.com/gsantopaolo
🔗 Portfolio: CogniX | ReforgeAI | Sentinel-AI | DeltaE
All solutions demonstrate production engineering patterns with proper testing, monitoring, security, and scalability—ready for enterprise deployment.