You Survey Customers. Why Not Your Employees the Same Way?

01 /The Concept

Ontora is an AI assistant that company leaders can ask complex, high-stakes questions: "How can we improve sales team performance?" "What's the ROI from R&D?" "How do we grow revenue?"

To answer those questions, the platform first builds a picture of how the company actually operates. It connects to CRMs, document repositories, knowledge bases, employee email, and video conferencing platforms – then AI agents begin analyzing everything they find.

But the document layer is only the foundation. On top of it, Ontora deploys AI agents that conduct employee surveys via ordinary text chats. These agents run in parallel, enabling hundreds of conversations in the span of a few hours and achieving full coverage across the organization.

The surveys serve a specific purpose: finding the gap between how work is *supposed* to happen and how it *actually* happens – capturing everything from complaints to improvement suggestions.

Combining document analysis with direct employee input produces a complete operational picture: gaps between documented processes and reality, identified problems, proposed fixes, and more.

Leaders can dig into this data at any time. But the simplest interface is just asking the platform whatever's been on their mind – and letting the AI draw on the collected information to answer.

Ontora entered Y Combinator's current batch this month and published its launch post on YC's site under a fitting headline: "Read your company like an open book"

02 /Why It Matters

Ontora touches two problems that exist in virtually every organization above a certain size.

Incomplete operational visibility is the more structurally damaging of the two.

Leaders can document processes and set standards – but they can't verify that what's actually happening matches what was intended. Processes have gaps, internal contradictions, blind spots, or simply haven't kept up with how the business has evolved.

You can't improve what you don't understand. And you can't effectively automate what you don't understand either – you'd just be locking in the wrong process.

Startup Edra ([related review](/review/buterbrod-s-bolshimi-dengami)) identified the same problem and built a platform for AI agent deployment that follows a two-stage principle: "First, we learn how your business actually works. Then we automate it." Edra appeared on the radar only last month – but already with $30 million in funding, which validates the approach.

Root cause invisibility is the second, equally common problem.

Front-line employees – salespeople, field technicians, drivers, support staff – encounter bottlenecks daily. They know exactly what's broken and often have a sense of how to fix it. The challenge is asking them at the right time, in the right way.

Arbor ([related review](/review/bolshie-dengi-na-malenkoj-jekonomii)) built a platform for surveying exactly those front-line employees and raised $6.3 million this February. Some of its early customers have already saved around $1 million by identifying the root causes of operational problems they hadn't previously been able to pinpoint.

Groopit ([related review](/review/neochevidnoe-sledstvie-pooshhrenie-iniciativy)) has been doing employee feedback collection longer than most and has raised $10.8 million. It started with conventional surveys and traditional analysis but has since evolved into a fully AI-native platform.

Your360.ai ([related review](/review/vtykaj-ii-mezhdu-ljudmi)) takes a different slice: AI-powered anonymous peer feedback for individual employees – not just leadership-to-team, but colleague-to-colleague – so each person can improve their own work based on real input from the people around them.

03 /Opportunities

Two complementary opportunity areas emerge from this landscape.

The harder and more valuable territory is giving leadership genuine visibility into what’s actually happening – where documented processes diverge from reality, and what to do about the gap. Whether that means fixing the process or fixing how people follow it. A closely related task: deploying AI agents to automate the parts that are now properly understood, because clarity about the real process is what makes automation safe.

Alongside that sits the continuous employee feedback challenge – surfacing hidden problems and incremental improvements through the people doing the actual work.

These two problems are natural complements, and Ontora is explicitly trying to combine them in a single platform.

The larger point: companies that build operating models grounded in this kind of continuous operational intelligence will have a structural advantage. That creates a real opportunity now, before these practices become standard. Which side of the problem would you start with?