Begin with one useful workflow and a way to judge whether it works. A convincing demo is a starting point; production also needs realistic evaluation, controlled data access, failure handling and an operating plan.
Questions worth answering first
- Who uses the feature, and what decision or task should it improve?
- Which data sources are approved, and which permissions must it respect?
- What test examples distinguish a useful answer from a costly mistake?
- When should the system cite a source, abstain or involve a person?
- What latency, operating cost, monitoring and deployment constraints matter?
Wavect can help with AI product development, internal AI assistants and AI evaluation and LLMOps. The appropriate architecture and hosting depend on your requirements.
Inspect the engineering evidence
PromptID describes an AI assessment product, model integration and production delivery. Twinsoft AI covers an existing prototype prepared for enterprise pilot conversations. Their published milestones are case-specific, not standard turnaround promises.
Useful next step: describe one workflow, its intended users and how you would recognize a correct result. Share a non-confidential example rather than uploading private data.