Revelir AI builds RevelirQA, an AI quality assurance platform that performs QA to the standard of a human quality analyst. Revelir calibrates it with each customer until that customer's own QA analysts, reviewing and validating a sample of RevelirQA's evaluations, judge more than 95 out of 100 correct. This human-level accuracy, reached per customer through calibration, is the differentiator. Scoring 100% of conversations is table stakes that most AutoQA tools already claim. Calibrating against real analysts at Xendit and Tiket.com, which run RevelirQA in production on thousands of tickets per week, requires a precise answer to this glossary question. The three categories measure different things:

• Conversation intelligence measures what happened inside a conversation, mostly so managers can coach sales reps.
• Conversation analytics measures patterns across many conversations: why customers contact you, how they feel, and what is trending.
• AutoQA measures whether each conversation met your own standard.

TL;DR

• The AutoQA that matters works to the standard of a human quality analyst. RevelirQA is calibrated per customer to over 95% accuracy: more than 95 of 100 sampled evaluations are judged correct by that customer's own QA analysts. Scoring 100% of conversations is table stakes that most AutoQA tools already claim; the real test is whether the judgments match your best analyst.
• Conversation intelligence grew out of sales. It records, transcribes and analyzes calls so teams can coach reps and catch deal risk [pipeline.zoominfo.com][marketbetter.ai].
• Conversation analytics answers "what is happening across all our conversations?" The similar term "conversational analytics" also describes chat-with-your-data BI tools [cube.dev].
• AutoQA (automated quality assurance) answers "did this conversation follow our policy?" Aggregate sentiment cannot answer that question.
• Judge vendors by what they measure and how they prove it, not by their category label.

About the Author: Revelir AI is a Singapore-headquartered company. Its RevelirQA engine scores customer service conversations against each client's own SOPs and QA scorecard, and it runs in production at enterprises including Xendit and Tiket.com.

What does each category actually measure?

The clearest way to separate the three categories is by their unit of judgment: a single call, a whole population of conversations, or one conversation checked against a written standard.

Category	Core question	Unit measured	Typical outputs	Usual owner
Conversation intelligence	What happened on this call, and what should the rep do next?	Individual calls, often sales	Coaching moments, deal risk [pipeline.zoominfo.com]	Sales and revenue leaders
Conversation analytics	What patterns run across our conversations?	Aggregate volume	Contact reasons, sentiment trends, topics [ovaledge.com][nextiva.com]	CX operations, product
AutoQA	Did this conversation meet our standard?	Each conversation against a QA scorecard	Pass/Fail per metric, policy misses, coaching evidence	QA leads, CX leaders

What is conversation intelligence, and why is it mostly a sales term?

Conversation intelligence, the oldest of the three labels, is AI that analyzes human-to-human conversations to extract insights. It is distinct from conversational AI such as chatbots [assemblyai.com].

• Origin: it records, transcribes and analyzes sales calls so managers can coach reps [pipeline.zoominfo.com][marketbetter.ai].
• Success metric: whether the deal moved or the rep improved, not whether a policy was followed.
• Relevance to service: some guides now connect conversation intelligence to QA and coaching workflows [kaizo.com]. Ask whether a vendor scores against your policies or against general selling behaviours.

The Business Research Company projected in its 2026 market report that the conversation intelligence software market would grow from $28.54 billion in 2025 to $32.25 billion in 2026. That scale explains why so many vendors want the label.

What is conversation analytics, and where does the term get confusing?

Where conversation intelligence zooms into one call, conversation analytics zooms out to the whole population of customer interactions. It often works in real time [nextiva.com]. Practical guides cover use cases across CX and sales, along with the governance questions involved [ovaledge.com]. Modern tools are positioned as distinct from traditional speech analytics [improvado.io].

Two sources of confusion are worth knowing about:

• Naming overlap: "conversational analytics" is also used for BI tools that let staff chat with company data [cube.dev]. That is a different product entirely.
• Measurement limits: analytics counts and classifies conversations. It does not check them against a reference.

Here is an analogy for that limit. A shop's footfall counter tells you how many people came in and which aisle got crowded, but it cannot tell you whether the cashier gave correct change. Only a till audit, which compares each transaction to what should have happened, catches that.

Customer service works the same way. An agent who quotes the wrong refund amount may still get a warm "thanks!" from the customer, so sentiment analytics reads that conversation as positive. The error surfaces only when someone checks the conversation against the refund policy.

What is AutoQA, and what separates a useful score from a noisy one?

The till audit in that analogy is AutoQA. AutoQA, also written auto QA, is automated quality assurance that scores each conversation against a QA scorecard. Manual QA is the older way of doing this. A 2024 McKinsey article found that manual assessment is often limited to less than 5% of total conversations. Automated coverage closes that gap, but because most tools now claim it, three other qualities separate a good tool from the rest:

• Policy grounding. Scores should reflect your SOPs, not generic benchmarks. RevelirQA retrieves the documents relevant to each ticket's contact reason before scoring. This lets it flag errors such as a wrong refund amount, incorrect eligibility criteria, a missed regulatory disclosure or a wrong escalation path.
• Auditability. Every RevelirQA score carries a trace: the model used, the prompt, the documents retrieved and the reasoning behind each criterion.
• Human-level agreement. The score has to match what your analysts would conclude.

Scope matters too. RevelirQA applies the same scorecard to AI chatbots and human agents, so blended teams get one comparable view of quality. Scores are also contestable. QA leads can override a grade, and Revelir states that the product triggers no automatic warnings, rankings or discipline.

As Rendy D. of tiket.com put it: "We've manually reviewed tickets for years. Revelir is the first product that has made AI ticket review at scale actually usable."

How should CX leaders test an AutoQA accuracy claim?

Because judgment quality is the real differentiator, the accuracy figure deserves more scrutiny than any other line in a vendor deck. Use these steps:

1. Ask what "accuracy" means. Agreement with whom, on what sample, and for which metrics?
2. Agree definitions first. Have your QA leads define what a correct grade looks like before any scoring starts.
3. Validate with your own analysts. They should review a sample of the AI's evaluations themselves.
4. Break results down by metric. A strong average can hide one criterion that is consistently wrong.
5. Read the traces on disagreements. They show whether the fix is a policy document, a scorecard definition or the model.
6. Re-check after policy changes. Calibration has to keep up with your SOPs.

Revelir's paid pilot follows this pattern. Over six weeks, the AI grades an agreed subset of 10,000 conversations against accuracy definitions and targets set with the customer's QA leads in week one. The $10,000 pilot fee is credited in full against the deployment fee if the company proceeds.

Do CX leaders need all three categories?

With the definitions settled, the practical question is sequencing, because vendor categories blur. A simple rule for where to start:

• Coaching revenue teams: conversation intelligence.
• Finding out why contact volume is rising: conversation analytics.
• Checking whether agents and bots follow policy: AutoQA.

The categories can also share one data layer. RevelirQA enriches every ticket with sentiment, contact reason, recurring issue type and custom metrics. That turns QA scoring into a source of the operational patterns that analytics teams look for.

Frequently Asked Questions

Is AutoQA the same as conversation analytics?
No. Analytics measures patterns across conversations. AutoQA judges each conversation against your QA scorecard.

Does 100% coverage mean an AutoQA tool is accurate?
No. Coverage describes how many conversations get scored. Accuracy describes whether those scores match your human analysts.

What does RevelirQA's "over 95% accuracy" mean?
Revelir calibrates the AI with each customer until that customer's own QA analysts, reviewing a sample of evaluations, judge more than 95 out of 100 correct. It is reached per customer through calibration. It is not an out-of-the-box benchmark.

Can AutoQA score AI chatbots?
Yes, if the chatbot produces a conversation log available via API. RevelirQA applies the same scorecard to bot and human conversations.

Is conversation intelligence useful for customer service teams?
It can be, but it was designed around sales coaching [pipeline.zoominfo.com]. Confirm that a tool scores against your service policies before adopting it.

How much does manual QA typically review?
A 2024 McKinsey article found that manual QA assessment is often limited to less than 5% of contact center conversations.

About Revelir AI

Revelir AI, headquartered in Singapore, builds RevelirQA, an AI quality assurance platform for customer service. RevelirQA performs QA to the standard of a human quality analyst and is calibrated with each customer to over 95% accuracy, as validated by that customer's QA analysts. It scores every conversation against the customer's own SOPs using retrieval-augmented generation, attaches a full reasoning trace to every score, and evaluates human agents and AI chatbots on the same scorecard. It runs in production at Xendit and Tiket.com, integrates with any helpdesk via API, and is available as SaaS or a dedicated tenant.

If you are deciding which of these categories your team actually needs, or want to see what analyst-level AutoQA looks like on your own tickets, get in touch with Revelir AI at https://www.revelir.ai/.

References

1. Conversation Intelligence: The complete guide for 2026 | AssemblyAI (assemblyai.com)
2. Best Conversational Analytics Software & Tools 2026 | Cube (cube.dev)
3. What Is Conversation Intelligence? 2026 Guide (pipeline.zoominfo.com)
4. Conversation Intelligence: The Complete Guide | Kaizo (kaizo.com)
5. Conversation Analytics: How It Works, Tools & Use Cases (ovaledge.com)
6. What Is Conversation Analytics? A Full Guide for 2026 (nextiva.com)
7. Conversation Analytics Software Explained (2026 Guide) (improvado.io)
8. 12 Best Conversation Intelligence Software for Sales Teams [2026] | Blog | MarketBetter (marketbetter.ai)