Always On Customer Intelligence: Introducing Sondar 2.0

Published: February 12, 2026

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Every founder knows the feeling. In the early days, you're sitting across from your customers. You hear their frustrations firsthand. You feel the urgency in their voice when they describe a workflow that's costing them hours. There's zero ambiguity about what to build next.

Then the company grows and the signal starts to fade.

The Insight Paradox

Scaling a SaaS company creates a cruel paradox: the more customers you have, the less you understand them. Not because you care less, but because of a structural problem I call Signal Decay.

Early Stage: Founder → Customer. 0% Signal Loss.

Growth: Rep → Manager → Product. 40% Signal Loss.

Enterprise: Rep → Manager → Director → VP → C-Suite. 80% Signal Loss.

What gets lost isn't the "what." It's the "why." In fact, 96% of customer feedback never reaches the teams who can act on it. Not because people don't care, but because the work of collecting, collating, and sharing unstructured feedback is so laborious that teams default to passing along what's convenient. The specifics that make feedback actionable get left on the cutting room floor.

Teams have already recognized this problem. That's why we've built internal wikis, feedback repositories, and adopted specialized SaaS tools designed to centralize the voice of the customer. These efforts help. But they don't solve the core challenge: customer feedback is highly unstructured in nature. And centralization without synthesis is just creating a bigger haystack.

Three Truths About Feedback at Scale

After a decade as a Product Manager in the bowels of SaaS, I have observed these patterns across most B2B product teams.

Aggregation is not actionable. Getting teams to send consistent, structured feedback into a database, repository, or CRM is already hard enough. But to make it actionable requires hours of synthesis to connect the dots across hundreds of data points to surface the patterns that matter. That requires a level of analytical throughput that most teams simply can't sustain.

The loudest voice hijacks the roadmap. When the signal is weak, decisions default to whoever shouts the loudest. The top-performing sales rep with a strong opinion. The enterprise account threatening to churn. Without rigorous synthesis, you end up building for the exception, not the rule, and your roadmap drifts away from what the majority of your customers actually need.

Proximity drives confidence. In a world where features are increasingly commoditized, the only defensible advantage is how fast you can turn customer pain into the right product. Signal decay breeds doubt. When leadership places anecdotes over ground truth, they hesitate. They second-guess. They slow down. But when they're close to the unfiltered voice of the customer, they regain the confidence to move at startup speed, regardless of company size.

From Chatbots to CoWorkers: What Changes with Sondar 2.0

When we launched Sondar 1.0, we introduced an AI chat assistant that could interrogate hours of qualitative interview transcripts and surface answers in seconds. It was a genuine unlock. Teams could finally query their customer conversations like a database.

But Sondar 1.0 had a ceiling. It only worked with data you manually imported—interview transcripts, call recordings you remembered to upload. That's a fraction of the conversations your teams are actually having with customers every day. The hundreds of support tickets, sales calls, QBRs, and check-ins that happen across your org? Most of that never made it in.

Sondar 2.0 solves both problems. It connects directly to the tools where customer conversations already live—so nothing falls through the cracks. And instead of a chatbot waiting for your questions, you can deploy a team of AI agents that synthesize actionable insights from those conversations around the clock. Think of it as always-on streams of context-specific, actionable insights delivered into the tools your teams already use.

An advisor doesn't wait for you to ask the right question. They tell you what you need to know before you think to ask. That's the shift.

Your Always-On Customer Intelligence Layer

Sondar 2.0 is built on three pillars.

Fits with your existing stack: Sondar 2.0 connects directly with a host of customer-facing tools including meeting tools, support desk software and CRMs. Every customer touchpoint feeds into a unified intelligence layer—no manual uploads, no context gaps.

Always-on intelligence: Your AI analysts work 24/7, categorizing sentiment, identifying patterns, and scoring the business impact of every signal. They never take a day off, and they never lose context between conversations.

Proactive insights: Critical signals don't wait for your next QBR. Sondar delivers context-rich notifications via Slack and email the moment something important surfaces—so the right people can act immediately.

Meet Your New AI Coworkers

Sondar 2.0 ships with over 100 ready-to-deploy agents—and a builder so you can create your own. Here are a few I've found incredibly useful in growing Sondar itself.

The Sales Analyst scours customer calls for deal blockers and winning talk tracks. The impact: higher win rates and dramatically faster rep onboarding, because every rep now learns from the patterns behind your best performers.

The Retention Scout detects subtle signals of frustration early in the renewal cycle—long before a customer sends the dreaded "we need to talk" email. The impact: zero-surprise churn for your CS team.

The Product Engine takes raw feature requests and scores them against actual business impact—revenue at risk, deal frequency, customer segment alignment. The impact: a roadmap backed by data, not the loudest voice in the room.

The Growth Specialist identifies the "aha" moments and ROI proof points buried in your customer conversations. The impact: a self-replenishing pipeline of advocates, case studies, and expansion opportunities.

Why "Agents" Matters Right Now

There is a ton of gold buried in customer conversations. The problem has never been a lack of it—it's been a scarcity of capacity to synthesize it into actionable insights. AI agents solve that capacity problem.

For decades, technical excellence was the moat. If your app had richer features, you won. But when AI agents can replicate a tech stack in a weekend, features are no longer a defensible advantage—they're a commodity.

The new moats look fundamentally different. Proprietary code has given way to proprietary data. Engineering headcount has given way to problem-market fit. System uptime has given way to brand trust and distribution. Feature richness has given way to solving the right problem simply.

The companies that will win the next decade aren't the ones with the best engineering teams. They're the ones with the deepest, most real-time understanding of their customers and the operational discipline to act on it continuously.

The New Normal

For too long, B2B SaaS teams have treated customer feedback like a filing cabinet—something to store, not something to learn from. The tools to actually query that data, to ask it questions and get real answers, simply didn't exist.

Sondar 2.0 represents a different way of working. With Sondar, anyone in the organization can "talk directly" to your entire customer base.

Your customers are telling you exactly what they need, in every call, every support ticket, every check-in. The signal is there. It's always been there. Now you have a team of AI agents working around the clock to make sure it reaches the people who can act on it.

Welcome to the agentic era of always-on customer intelligence.

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