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If you are reading this, you are likely evaluating vendors for Voice of Customer or Support Analytics. You might be looking at us, but you are probably also looking at similar AI products, legacy VoC tools, or the native analytics inside your helpdesk.
I have spent the last decade in this space. I have seen the market shift from we need charts to we need AI. With that shift has come a lot of confusion. Every vendor claims to have AI. Every vendor claims to be real-time.
To help you cut through the noise, whether you choose SentiSum or not, I have compiled six “stress test” questions you should ask any vendor during a demo. These are based on real frustrations I hear from leaders at high-growth fintech, marketplace, and retail companies who came to us after other tools failed them.
Use this checklist to ensure you are not buying vaporware.
The 5-Point Evaluation Checklist
1. The “Granularity” Test
(Don’t settle for “Billing”)
Most AI tools can easily tag a ticket as “Billing” or “Bug.” That is useless to your Product Manager. They cannot fix “Billing”. They can fix “Apple Pay failing on iOS 17”.
The question to ask:
Show me how your AI drills down to a Layer 3 or Layer 4 root cause without me manually creating keywords. If I have a spike in ‘Login Issues,’ will the tool tell me it is specifically ‘SSO failure on Android’ automatically?
Why it matters:
If insights stop at high-level categories, product teams cannot act. Over time, the tool becomes a reporting layer, not a decision system, and adoption slowly dies.
2. The “Real-Time” Test
Reporting vs. Observability: There is a massive difference between a dashboard that updates every two hours and an alert that fires in two seconds. In high-volume support, a two-hour lag is the difference between a minor glitch and a PR disaster.
The question to ask:
What is the exact latency between a ticket arriving and an alert being generated? If a new issue spikes right now, will I know about it in five minutes, or do I have to wait for the data warehouse to sync?
Why it matters:
We have spoken to teams who missed major outages because their “real-time” dashboard actually had a two-hour delay. True observability acts as a kill switch for runaway issues, not a rear-view mirror.
3. The “Bot Accountability” Test
You are likely automating 30 to 50 percent of your traffic using AI agents such as Intercom Fin, Decagon, or Ultimate. Most analytics tools only analyze human tickets, leaving you blind to a large portion of customer interactions.
The question to ask:
Does your platform ingest and QA the logs of my AI agent? Can it tell me, in real time, when my bot is hallucinating or causing frustration, even if the ticket never reaches a human?
Why it matters:
If you do not monitor your bot, you do not know whether you are deflecting tickets or deflecting customers. You need a tool that audits the quality of bot conversations, not just the quantity.
4. The “Democratization” Test
Can the CEO use it?
If a Product Manager or Executive has to ask a Data Analyst to pull a report, the tool has failed. The modern standard is natural language querying, asking questions in plain English.
The question to ask:
Can I type a question like ‘Why did churn increase yesterday?’ and get a summarized, evidence-backed answer in 60 seconds? Or do I have to build a widget?
Why it matters:
When only one team can use the tool, insights never compound. During budget reviews, platforms that no one outside Support actively uses are the first to be questioned or cut.
5. The “Explainability” Test
Trust and Explainability: AI hallucinations are a real business risk. You cannot make strategic product decisions based on a black-box summary that may be making things up.
The question to ask:
Can the system prove its answer? If the AI says ‘Refunds are spiking due to a UI bug,’ can I click that insight and immediately see the 50 specific conversation logs that support it?
Why it matters:
Executives need evidence, not just summaries. If the AI cannot cite its sources down to the individual ticket ID, you cannot trust the output for executive reporting or compliance.
Final Thought
We built SentiSum to pass these six tests because we refuse to build shelfware. Whether you buy from us or someone else, make sure you are getting granular, real-time, and transparent insights.
If you want to see how your data looks through this lens, I am happy to chat. Book a Demo
If you are reading this, you are likely evaluating vendors for Voice of Customer or Support Analytics. You might be looking at us, but you are probably also looking at similar AI products, legacy VoC tools, or the native analytics inside your helpdesk.
I have spent the last decade in this space. I have seen the market shift from we need charts to we need AI. With that shift has come a lot of confusion. Every vendor claims to have AI. Every vendor claims to be real-time.
To help you cut through the noise, whether you choose SentiSum or not, I have compiled six “stress test” questions you should ask any vendor during a demo. These are based on real frustrations I hear from leaders at high-growth fintech, marketplace, and retail companies who came to us after other tools failed them.
Use this checklist to ensure you are not buying vaporware.
The 5-Point Evaluation Checklist
1. The “Granularity” Test
(Don’t settle for “Billing”)
Most AI tools can easily tag a ticket as “Billing” or “Bug.” That is useless to your Product Manager. They cannot fix “Billing”. They can fix “Apple Pay failing on iOS 17”.
The question to ask:
Show me how your AI drills down to a Layer 3 or Layer 4 root cause without me manually creating keywords. If I have a spike in ‘Login Issues,’ will the tool tell me it is specifically ‘SSO failure on Android’ automatically?
Why it matters:
If insights stop at high-level categories, product teams cannot act. Over time, the tool becomes a reporting layer, not a decision system, and adoption slowly dies.
2. The “Real-Time” Test
Reporting vs. Observability: There is a massive difference between a dashboard that updates every two hours and an alert that fires in two seconds. In high-volume support, a two-hour lag is the difference between a minor glitch and a PR disaster.
The question to ask:
What is the exact latency between a ticket arriving and an alert being generated? If a new issue spikes right now, will I know about it in five minutes, or do I have to wait for the data warehouse to sync?
Why it matters:
We have spoken to teams who missed major outages because their “real-time” dashboard actually had a two-hour delay. True observability acts as a kill switch for runaway issues, not a rear-view mirror.
3. The “Bot Accountability” Test
You are likely automating 30 to 50 percent of your traffic using AI agents such as Intercom Fin, Decagon, or Ultimate. Most analytics tools only analyze human tickets, leaving you blind to a large portion of customer interactions.
The question to ask:
Does your platform ingest and QA the logs of my AI agent? Can it tell me, in real time, when my bot is hallucinating or causing frustration, even if the ticket never reaches a human?
Why it matters:
If you do not monitor your bot, you do not know whether you are deflecting tickets or deflecting customers. You need a tool that audits the quality of bot conversations, not just the quantity.
4. The “Democratization” Test
Can the CEO use it?
If a Product Manager or Executive has to ask a Data Analyst to pull a report, the tool has failed. The modern standard is natural language querying, asking questions in plain English.
The question to ask:
Can I type a question like ‘Why did churn increase yesterday?’ and get a summarized, evidence-backed answer in 60 seconds? Or do I have to build a widget?
Why it matters:
When only one team can use the tool, insights never compound. During budget reviews, platforms that no one outside Support actively uses are the first to be questioned or cut.
5. The “Explainability” Test
Trust and Explainability: AI hallucinations are a real business risk. You cannot make strategic product decisions based on a black-box summary that may be making things up.
The question to ask:
Can the system prove its answer? If the AI says ‘Refunds are spiking due to a UI bug,’ can I click that insight and immediately see the 50 specific conversation logs that support it?
Why it matters:
Executives need evidence, not just summaries. If the AI cannot cite its sources down to the individual ticket ID, you cannot trust the output for executive reporting or compliance.
Final Thought
We built SentiSum to pass these six tests because we refuse to build shelfware. Whether you buy from us or someone else, make sure you are getting granular, real-time, and transparent insights.
If you want to see how your data looks through this lens, I am happy to chat. Book a Demo
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