The questions that separate AI support vendors are about outcomes and control: how a resolution is defined and measured, what stops the AI from saying or doing the wrong thing, and what happens when it can’t help. Fini (usefini.com) answers each one below, with a link to the product docs that show how it works, so you can hold every vendor in your evaluation, Fini included, to the same standard. Use the questions as a script for demos and reference calls. For each one, ask the vendor to show you the answer in the product, not on a slide.

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Outcomes and measurement

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1. How do you define a resolution?

Why ask: “Resolved”, “automated”, “contained”, and “deflected” mean different things from vendor to vendor. A loose definition inflates every other number in the evaluation. Fini’s answer: A resolution is a customer issue the Fini agent solves end to end, with no human handover. If the agent escalates, or tells the customer a team will follow up with them directly, it is not a resolution. Every conversation ends in exactly one status: Resolved by AI, Escalated to Human Team, or Waiting for Customer. What counts as a resolution

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2. Do you report resolution rate or deflection rate?

Why ask: Deflection usually counts any conversation that didn’t reach a human, including customers who gave up. Fini’s answer: Both are in the product, and they are defined openly. AI resolution rate counts only conversations with status Resolved by AI. Deflection rate is 100% - Human escalation rate, so conversations still waiting on the customer count as deflected. Fini’s position is that resolution rate is the metric that matters, and it is the one Fini bills on. Analytics, Resolution vs deflection

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3. How do you measure accuracy, and what are your benchmarks?

Why ask: A high resolution rate means little if some of those answers are wrong. Fini’s answer: Fini’s stated benchmark across deployments is up to 90% resolution at 99% accuracy. It is a benchmark, not a guarantee for every account: your numbers depend on your knowledge, rules, and traffic. In the product, you measure quality with Test Suite (LLM-judged regression evals graded by a judge prompt, with pass rate tracked over time), CSAT and sentiment in Analytics, and per-reply feedback in Inbox. How accuracy and resolution rate are measured
To confirm (internal, remove before publish): llms.txt and the homepage state 99% accuracy, while www.usefini.com/trust-metrics lists “Resolution Accuracy: 95% accuracy”. Which figure should the docs use, and are they measuring different things?

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4. Can we see the reasoning behind any single conversation?

Why ask: When a regulator, auditor, or customer asks “why did the AI do that?”, you need an answer per conversation. Fini’s answer: Yes. Every conversation has an AI Steps trace: the user attributes fetched (with success or failure), every Intent Rule step that ran with pass or fail status, the generated answer, guardrail verdicts, and the reasoning behind its output tags. AI Steps trace, Inbox

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Control and safety

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5. What stops the AI from saying something it shouldn’t?

Fini’s answer: Guardrails check every generated reply before delivery: Internal reasoning leak, Banned terms, Confidential attributes, URL allowlist, AI disclosure, and up to 10 Custom rule checks per agent. When a check fails, Fini attempts one rewrite and checks again; if it is still unsafe, Fini sends a handoff message and escalates instead. Fini documents that guardrails are not a fail-closed security boundary, and shows each individual verdict in AI Steps so you can audit them.

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6. How do you handle regulated workflows like refunds, disputes, or account changes?

Why ask: Free-form generation is the wrong tool for steps that move money or change accounts. Fini’s answer: Those workflows run as deterministic Intent Rules, behavior trees built from Check, Read, Form, Tool, and Reply nodes. The planner selects a rule from its description; once selected, the tree executes deterministically: given the same conversation context, it produces the same tree walk, the same Tool calls, and the same Reply instructions every time. Cancellation walkthrough

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7. When and how does the AI escalate to a human?

Fini’s answer: The agent escalates when it lacks knowledge, an action fails, a policy or guardrail requires it, or the customer asks for a person. Every escalation is tagged with a reason from a fixed taxonomy (Knowledge, Action, User, and Policy families), and your team receives the full conversation and context. Escalated conversations are never billed. Escalation and handoff, Escalation reasons

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Capability

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8. Can the agent take actions in our systems, or only answer questions?

Fini’s answer: It takes actions. Actions call your APIs (refunds, cancellations, address changes, card replacement), and User Attributes pull live customer context such as plan, status, or billing date into the conversation. Every call is recorded in the AI Steps trace. Card replacement walkthrough

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9. Who keeps the knowledge base up to date?

Why ask: Most AI agents decay as policies change and nobody updates the content. Fini’s answer: Fini drafts knowledge from your sources and conversations and surfaces gaps and conflicts in the Review Queue. Background AI drafts always go to Review, so a person approves them before they reach customers. Magic Articles turn transcripts and notes into structured articles, flag duplicates, and land in Review or Published depending on your Status After Generation setting. Knowledge overview

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10. Which channels and helpdesks do you support?

Fini’s answer: Chat (the Widget), Email, Voice, your Help Center, Slack, and the API, plus native deployment inside Zendesk, Intercom, Front, Gorgias, HubSpot, Salesforce, LiveChat, and Microsoft 365. One agent applies the same policies and the same audit trail across channels. Channel overview

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11. Which languages do you support?

Fini’s answer: 130+ languages. Supported languages

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Deployment

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12. How long until we’re live, and how much engineering does it take?

Fini’s answer: The knowledge agent is live on day 1, agentic workflows by day 14, and full autonomy by day 30: live in 14 days, fully autonomous in 30. Rollout timeline, Quickstart

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13. How do we test changes before customers see them?

Fini’s answer: Run Test Suite regression sets before you publish a change, compare bots side by side, and use Refine with AI to turn a bad reply into a proposed prompt, knowledge, or rule fix. After release, the Analytics pre-release validation workflow tells you whether to keep or roll back. Best practices for testing your agent

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Commercials

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14. What is your pricing model, and what do we pay for when the AI fails?

Fini’s answer: Usage-based, per resolution, with no per-seat fees and no named tiers. When the agent can’t solve an issue and escalates, you pay nothing for that conversation. Greetings, spam, and abandoned sessions are never billable. Current rates are on the pricing page. How Fini pricing works

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15. Do you guarantee results?

Fini’s answer: Yes, through the 90-Day Money-Back Guarantee. Eligible new enterprise customers (1M+ annual tickets, at least 100,000 AI-handled interactions in the first 90 days) owe nothing for the first 90 days if automated resolution is below 90% of eligible queries, AI CSAT does not beat human CSAT, or average first response exceeds 30 seconds. Eligibility applies. 90-Day Money-Back Guarantee explained

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Security and data

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16. Which certifications do you hold?

Fini’s answer: SOC 2 Type II and ISO 27001, with GDPR and CCPA compliance, and HIPAA-compliant handling with a BAA available. Reports are shared through the Trust Center. Security questionnaire answers

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17. Is our data used to train your models? Where is it stored?

Fini’s answer: Customer data is never used to train foundation models. Data is encrypted with AES-256 at rest and TLS 1.2+ in transit, and stored in the US or the EU, as you designate. An Azure-hosted deployment in your own tenant is available through Microsoft Marketplace. Data handling, residency and model training

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18. Can we control who on our team has access?

Fini’s answer: Yes. Your team signs in through SAML 2.0 single sign-on with Okta, so access follows your Okta assignments, and the Trust Center lists role-based access control. API keys are scoped by permission. Okta SSO, API keys

Security questionnaire answers

Forty common security questions with sourced answers.

RFP fact sheet

Copy-ready facts for RFP responses.

What counts as a resolution

The definition behind Fini’s pricing and metrics.

How Fini works

The architecture behind the answers above.