YundaDeskMost support software RFPs ask broad questions: Do you support AI? Do you support multiple channels? Do you have reporting? Vendors can say yes to all three and still leave you with unpredictable AI fees, unreviewed learning, risky refund promises, and agents switching between WhatsApp, TikTok, email, and your store backend.
A useful RFP makes the operating model visible before you buy. These 30 support software rfp questions are for procurement teams, support leaders, and founders choosing AI support software for cross-border e-commerce. Do not accept only yes-or-no answers. Ask for screenshots, workflows, sample bills, and failure cases.
Start With Fit: What Problem Does It Really Solve?
Before comparing feature grids, define the job. Are you buying a basic inbox, or omnichannel AI support where AI answers first, humans back up?
- What are the core product objects: AI support agent, shared workspace, knowledge base, internal AI assistant, or marketing automation?
- When the customer-facing AI answers, what sources does it use? Can it point to policies, knowledge base content, or customer history?
- What happens when the AI cannot answer, the customer asks for a human, or a high-risk rule is triggered?
- Is there an internal AI assistant like Yuna that helps merchants query business data, configure rules, and teach know-how to the AI agent without talking to customers directly?
30 RFP Questions to Ask AI Support Vendors: start platform evaluation with the market shift
Ask About Pricing: Look Past the Monthly Fee
The pricing risk in AI support often appears after launch. When peak season raises volume, vague metering makes budgets hard to forecast.
- Are AI credits included in every plan? How are they displayed, alerted, and extended?
- Do you charge extra by conversation, resolution, outcome, ticket, or message? What triggers billing?
- Are agents, channels, knowledge base capacity, or multilingual support billed separately?
- During seasonal spikes, do overages bill automatically, or do we approve more capacity first?
- Can you provide a sample monthly bill based on our expected channel volume?
| Pricing item | What to clarify | Risk |
|---|---|---|
| AI credits | Included amount and overage handling | Unpredictable budget |
| Agents | Whether pricing is by agents (seats) | Seasonal staffing cost |
| Outcome billing | Whether outcome or resolution billing applies | Better automation can cost more |
| Channels | Whether WhatsApp, TikTok, email, and others cost extra | Omnichannel rollout gets fragmented |
YundaDesk’s model is that AI credits are included in every plan, with no per-conversation or per-resolution surcharge. Ask every vendor directly: if our support volume doubles, what happens to the bill?
Ask About Learning: Smarter or Less Controlled?
“The AI learns” is not automatically good. Wrong agent replies, customer pressure, or temporary policies should not silently become future AI behavior.
- How are questions the AI cannot answer collected?
- When an agent replies manually or corrects the AI, does the system create learning suggestions you confirm?
- Must a manager or admin approve learning suggestions before they take effect?
- Is every learned item traceable, testable, and revertible?
- Can the system separate knowledge, executable capabilities, and customer memory instead of mixing them together?
Ask one concrete scenario: if an agent mistakenly says sale items are always refundable, will the AI learn that tomorrow? The right answer is no. At most, it should create a pending learning suggestion an admin can reject.
Ask About Channels: Omnichannel Means One Workspace
Cross-border support becomes complex because customers arrive through many doors: storefront widget, custom API, email, WhatsApp, Telegram, Messenger, Instagram, TikTok, LINE, WeChat, VKontakte, Zalo, and YouTube. When vendors claim omnichannel coverage, ask what happens after messages arrive.
- Can you connect all of those channels?
- Do all channel messages enter one workspace, or do agents still need separate backends?
- Can the same customer across channels be merged into one customer profile?
- Does the profile include country, language, time zone, and social IDs?
- Does the AI follow the customer’s language automatically? When a human takes over, can the agent see the original message and full context?
Do not stop at “can you connect it?” Ask whether agents still switch tools, whether the AI answers from the same knowledge base, and whether customer history becomes reusable. For a deeper framework, compare vendors against /en/blog/omnichannel-inbox-explained/.
Ask About Risk Boundaries: Who Approves Money Moves?
AI support buying must treat high-risk actions separately. Refunds, compensation, price changes, and escalated complaints are not places to maximize automation. They are places to enforce approval and audit.
- Which intents force handoff to human? Can merchants configure those rules?
- Do refunds, compensation, and price changes always require human approval?
- Can the approver see the order, conversation, customer history, AI recommendation, and source evidence in one place?
- Is the AI limited to collecting information and suggesting next steps, without executing high-risk actions automatically?
- Are approvals, corrections, and rollbacks logged for audit?
Ask the vendor to demonstrate this live. Watch how a refund request moves from AI intake, risk detection, human handoff, summary generation, approval, and final human decision. For more on the boundary, see /en/blog/ai-first-human-backed-boundary/.
Ask About Proactive Outreach: It Should Know When to Stay Quiet
Proactive outreach can help when shoppers hesitate at checkout, delivery exceptions appear, or sizing questions repeat. It can also become annoying fast.
- Does proactive outreach support three modes: observe only, require my approval for every message, and send automatically?
- Are there guardrails for cooldown, frequency caps, quiet hours, no interruption during active chats, do-not-disturb lists, and human approval for sensitive actions?
- Can these guardrails be turned off? If yes, who can do it, and is the change logged?
The best answer is not “we can automatically chase every cart.” It is “we can speak first under controlled conditions.” Many teams should start with observe-only mode, then approval, and automate only the safest patterns.
Ask About Launch Validation: Make It Testable
The last group determines whether the project can move from demo to production. The deliverable is a working setup across knowledge base, channels, rules, approvals, and reports.
- Before launch, can we test the AI against historical conversations and record the answer, source evidence, and handoff result?
- How do we build the knowledge base: document upload, website crawling, and manual Q&A?
- After launch, can reports show unanswered AI questions, agent replies, AI corrections, accepted learning suggestions, and rollbacks?
Turn those questions into an acceptance checklist:
- Select 50 real historical conversations covering shipping, sizing, returns, complaints, and multiple languages
- Every answer shows its source, and unknown questions hand off to humans
- High-risk terms trigger approval instead of automatic refund or compensation promises
- Agent corrections create learning suggestions you confirm, not automatic changes
- Accepted learning can be tested, traced, and rolled back
If a vendor only shows a polished demo and avoids your test set, rollout risk is high. A team willing to review failure cases with you usually understands real support operations.
An RFP surfaces the places most likely to break later: unpredictable bills, uncontrolled learning, fragmented channels, and risky actions without approval. Ask these 30 questions, and you are no longer just buying support software with AI. You are choosing an operating system for cross-border support that can handle daily pressure and get smarter over time.