What we deploy
- LLM applications: enterprise knowledge Q&A, document understanding and extraction, support and sales assistance
- Process automation: hand repetitive reporting, entry, reconciliation and follow-up to AI agents
- Data analysis and visualisation: turn scattered operational data into decision-ready dashboards
- Custom AI tools: efficiency tools for a specific role, not another generic chat window
How we implement
- Pick one high-frequency, clearly bounded scenario to validate
- Evaluate with real data and agree on an acceptable success rate
- Roll out narrowly, with human review in the loop
- Expand to adjacent scenarios once stable
Mistakes we have already made
Inside Shenggong we have hit "the model is smart but the process does not connect" and "accuracy is fine but nobody uses it". Our proposals therefore include permissions, audit trails, human fallback and cost control by default.