Gain deep insight across users, products, operations and agents, in real time.

Support and sales are often treated as isolated functions. In reality, they are reflections of the product you offer, the process you establish, and the market you are in.

That is why the biggest opportunities for improvement often do not come from support or sales themselves. They come from the continuous improvement around them: better product decisions, stronger processes, clearer operations, and faster response to change.

Every customer interaction contains signals about what is working, what is breaking, what customers value, and what the business should improve next. Those interactions are not overhead. They are one of the most valuable sources of business truth.

AI creates a new opportunity here. It can connect the dots across massive data, surface the important signals, and turn service from a cost center into a growth engine.

How this differs from [Agent Insight](/product/agent-insight). Pulse looks outward: it reads conversations to tell you what customers, products and processes are doing — demand patterns, failure modes, emerging issues, where revenue leaks. Agent Insight looks inward: it scores how well each agent, AI or human, handled the work against a standard you define. Pulse answers "what is happening in the business?"; Agent Insight answers "how good was that agent?". Most teams run both, and they share the same conversation data.

What Pulse actually measures. Every conversation, not a sample — which is the point. Manual QA typically reviews 1–2% of tickets, so 98% of what your team does is never assessed by anyone. Pulse scores 100% of conversations, AI-handled and human-handled, on resolution, CSAT, sentiment, customer effort, policy adherence and root cause, and it runs on the same conversation data as Agent Insight and Evolve. It is included in the platform rather than priced separately — see pricing.

Pulse is the analysis engine of Aissist. It generates deep insight across customers, products, agents, processes, and business performance.

It can identify patterns, trends, outliers, what went well, and what needs improvement. The goal is not just more reporting. The goal is sharper understanding and more precise measurement of what is really going on.

The Ask Engine makes Pulse something you can interrogate. Ask a question the way you would ask an analyst — about your product, your customers, your operation, or a single agent — and the answer comes back in seconds, drawn from the full conversation record rather than a sample.

That turns reporting into investigation. A dashboard tells you resolution dropped overnight. The Ask Engine tells you the drop was concentrated in QR-code and installation failures, that one tag carried 1,085 conversations with 62.0% escalated and only 10.6% resolved, and that the volume peak sat between 01:00 and 02:00.

Every answer is grounded in the conversations behind it and explicit about what the evidence does not support. Where the data shows a coincidence rather than a cause, it says so instead of inventing a root cause — which is what makes an answer safe to act on.

Teams use it to find the failure mode driving escalations, check whether a configuration change hurt a flow, compare a new agent against the team, see what customers have said about a feature since launch, or spot where a policy is being applied inconsistently.

The point is speed while the signal is still fresh: discover the issue in the hour it appears, understand the operation behind it, and decide with evidence — without waiting on an analyst queue or the next weekly cycle.

Quality assurance for both human and AI agents becomes more important as automation expands. Pulse allows you to continuously evaluate traffic and generate insight based on customizable metrics defined by your business.

That means the measurement can reflect your actual standards instead of relying on a generic template.

For each agent, you can also go deeper and generate an assessment of what went well, what did not, and where improvement is needed next.

Explore Agent Insight — one standard for every AI and human agent, with a loop that turns results into improvement.

At Aissist, NPS and CSAT are automatically measured, monitored, and analyzed. The analysis is broken down by category so teams can see exactly what customers like, dislike, and respond to most strongly.

The auto-rating system is continuously compared with actual customer feedback and adjusted over time so that the interpretation stays aligned with what is happening in the field.

This makes the score more than a surface KPI. It becomes a structured way to understand the drivers behind customer sentiment.

That kind of measurement is only useful when the underlying system is trustworthy. For more on how Aissist approaches reliability and governance, see Reliable AI.