VE
Vectara
Enterprise AI with RAG & hallucination prevention
Partner Solution - Governed, grounded, and auditable AI agents
AI/GenAI Strategy
- What is your organization's AI strategy?
- Have you deployed any generative AI solutions?
- What AI use cases are you exploring or planning?
- What concerns do you have about AI adoption? (accuracy, security, compliance)
Current AI Challenges
- Have you experienced issues with AI hallucinations or inaccurate responses?
- How do you ensure AI outputs are grounded in accurate information?
- What governance controls exist for AI systems today?
- How do you audit AI decisions and outputs?
Enterprise Search & Knowledge
- Do you need intelligent search across enterprise documents?
- What document types and formats need to be searchable? (PDFs, docs, images, tables)
- How much content needs to be indexed? (documents, pages, GB)
- What is the current search experience and its limitations?
Conversational AI / Assistants
- Are you building or planning enterprise AI assistants?
- What questions should the assistant be able to answer?
- What data sources should inform the assistant's responses?
- Who are the target users? (employees, customers, partners)
Customer-Facing AI
- Do you need AI capabilities in customer-facing applications?
- What customer interactions could benefit from AI assistance?
- What brand and tone guidelines must AI follow?
- What are the risks of incorrect AI responses to customers?
Data & Content
- What are the primary data sources for AI to access?
- Is the content mostly text, or do you have tables, images, diagrams?
- How frequently is the content updated?
- Are there access control requirements for different content?
Governance & Compliance
- What compliance requirements apply to AI systems? (HIPAA, SOC 2, GDPR)
- Do you need to audit AI responses and their sources?
- Are there data residency requirements?
- What controls are needed to prevent sensitive data exposure?
Deployment & Technical
- What deployment model do you prefer? (SaaS, Customer VPC, on-premises)
- What LLM/AI models do you want to use? (OpenAI, Anthropic, open source)
- Do you need "Bring Your Own Model" flexibility?
- What scale do you anticipate? (users, queries per day)
- What systems need to integrate with the AI platform?
Success Criteria
- How will you measure AI solution success? (Accuracy, adoption, time savings)
- What would a successful pilot look like?
- What is the timeline for AI deployment?