Premier Nexus

Teams needed a controlled internal AI workspace that could answer questions from private documents while keeping users, workspaces, and costs separated.

• Designed document ingestion across PDFs, DOCX, and CSV files with vector retrieval for grounded answers.
• Added model-aware routing and retries across providers to improve reliability.
• Built workspace access, API keys, and usage tracking for enterprise control.

• Documents are uploaded, parsed, embedded, and stored per workspace.
• Chat requests retrieve relevant context and route through the selected model provider.
• Usage logs track tokens, costs, and response behavior.
• Speech integrations support voice-based workflows where needed.

• Workspace isolation reduces accidental data bleed.
• Rate limits and API key controls support external integrations.
• Streaming responses improve perceived latency for long answers.

• Workspace isolation and auditability were prioritized over a single shared retrieval index.
• Retrieval quality still depends on document structure, so ingestion diagnostics remain an important next step.

The platform created a practical internal AI layer for knowledge work, with governance features beyond a simple chatbot.