Pain points

• Hard to turn AI experimentation into production-grade solutions
• Data quality, governance, and security concerns
• Lack of clear ROI and adoption for AI initiatives

How Softplex solves them

• Discovery to identify high-ROI AI opportunities
• RAG, copilots, and automation with safety and guardrails
• MLOps pipelines, monitoring, and human-in-the-loop workflows

Features

• Use-case discovery and success metrics
• Data pipelines, feature stores, and governance
• Model integration, evaluation, and prompt engineering
• Deployment, monitoring, and continuous improvement

Technologies

• OpenAI, Anthropic, Vertex AI, Azure AI
• Vector databases (Pinecone, Weaviate, pgvector)
• LangChain, LlamaIndex
• MLflow, Feast, and model monitoring
• Event-driven and streaming architectures