How 1,000 Customer Calls Built an AI Startup That Enterprises Actually Need

In 2026, the AI vendor landscape is more crowded than ever. Yet most enterprise AI tools still fail to deliver real ROI because they solve problems that vendors think exist—not the ones that customers actually face. That’s why Narada’s approach stands out: Before writing a single line of code, its founders conducted over 1,000 customer calls to identify the exact workflows where AI could drive measurable impact How 1,000+ customer calls shaped a breakout enterprise AI startup | TechCrunch.

For CTOs and product leaders at European enterprises, this isn’t just a feel-good story—it’s a blueprint for how to avoid the 80%+ failure rate of AI pilots by grounding innovation in real user pain points. Here’s what Narada’s customer-first approach reveals about building AI that scales.

The Problem: Why Most Enterprise AI Fails at Adoption

Enterprise AI adoption remains stubbornly low, despite the hype. Narada’s founders took the opposite approach: They spent months on calls with potential customers, asking not about AI, but about their daily frustrations TechCrunch.

What they discovered was a critical gap in the market:

• Enterprises didn’t need another chatbot for simple Q&A.
• They needed AI that could handle multistep, cross-system workflows—like onboarding a new vendor, resolving a customer dispute, or generating a compliance report—without constant human oversight.
• Most importantly, they needed AI they could trust to execute complex tasks autonomously, not just suggest next steps.

This aligns with what we’ve seen in Europe, where regulatory constraints and legacy system integration make trust and reliability non-negotiable. Narada’s insight? The best AI products don’t start with technology—they start with a deep understanding of the user’s mental model.

The Solution: Large Action Models That Work Like a Colleague

Narada’s product isn’t just another LLM wrapper. It’s built on “large action models” (LAMs), a newer class of AI designed to:

1. Understand intent (e.g., “Resolve this invoice dispute”) and
2. Autonomously execute across multiple systems (ERP, CRM, email, etc.) TechCrunch.

The key differentiator? Natural language as the interface. As founder David Park put it:

“These teams needed an AI product that they could speak to like a person and trust to take on multiple steps at once.” TechCrunch

For European enterprises, this is particularly relevant:

• Compliance teams can say, “Generate a GDPR audit trail for this customer” and get a complete, actionable report—not just a draft.
• Procurement teams can offload vendor onboarding by saying, “Set up this supplier in SAP and send them the compliance paperwork.”
• Customer service agents can resolve disputes end-to-end with “Refund this order, notify the customer, and update the CRM.”

This level of autonomy is rare. Most AI tools today still require human-in-the-loop validation at every step, which defeats the purpose of automation. Narada’s approach—rooted in those 1,000+ customer conversations—flips the script by focusing on outcomes, not just outputs.

The Proof: Why Customer-Centric AI Scales (and How to Measure It)

Narada isn’t the only startup proving that deep customer research correlates with explosive growth. Neuron7, another enterprise AI company, has over 50,000 users and its customers tend to double their spending 16 to 18 months after first using their products TechCrunch.

For European enterprises, the lesson is clear: AI that solves a specific, painful workflow will scale organically. But how do you identify those workflows? Narada’s playbook offers three tactical steps:

1. Talk to the Doers, Not Just the Buyers

• Narada’s founders didn’t just call CIOs—they spoke to the agents, analysts, and managers executing the work daily.
• Example question: “What’s the most repetitive 30-minute task you do every week?” (Not: “Where do you see AI fitting into your strategy?”)

2. Measure “Time to Trust”

• Most AI tools are judged on accuracy. Narada focused on how quickly users trusted the AI to act autonomously.
• Proxy metric: Track how often users override or edit the AI’s actions. (The goal? <5% intervention rate for mature workflows.)

3. Design for “Progressive Autonomy”

• Start with high-visibility, low-risk tasks (e.g., drafting emails).
• Gradually expand to cross-system workflows (e.g., “Process this return and restock inventory”).
• European context: This aligns with the EU AI Act’s risk-based framework, where high-impact use cases require stricter validation.

The European Opportunity: Where Most AI Vendors Get It Wrong

European enterprises face three unique challenges that Narada’s approach directly addresses:

1. Legacy System Fragmentation

   • The average EU corporation uses 12+ disjointed systems (SAP, Salesforce, custom tools).
   • Narada’s large action models bridge these silos by acting as a universal translator—something most AI vendors ignore.

2. Regulatory Scrutiny (EU AI Act)

   • The EU AI Act requires transparency in automated decision-making.
   • Narada’s natural-language audit trails (e.g., “Here’s why I refunded this customer”) make compliance built-in, not bolted-on.

3. Skepticism Toward “Black Box” AI

   • European users demand explainability. Narada’s conversational interface lets users ask, “Why did you do that?”—a feature missing in most enterprise AI tools.

The takeaway? **AI that works in Europe must be:

• Workflow-native (not just a chatbot),
• Regulation-ready (by design, not as an afterthought),
• Trust-first (with clear “why” behind every action).**

The Bottom Line: Stop Guessing, Start Listening

Narada’s success isn’t about having the most advanced model—it’s about solving the right problem in the right way. For CTOs and product leaders, the actionable insights are:

1. Spend 10x more time on customer research than on model tuning.

   • Narada’s 1,000+ calls weren’t a one-time exercise—they’re an ongoing feedback loop.

2. Focus on “jobs to be done,” not features.

   • Users don’t want “AI”—they want fewer manual steps, fewer errors, and faster resolutions.

3. Design for autonomy, not just assistance.

   • The future of enterprise AI isn’t suggestions—it’s trusted execution.

At Hyperion, we’ve seen firsthand how European enterprises waste millions on AI pilots that never scale because they skip the customer discovery phase. The difference between a failed experiment and a high-growth AI product often comes down to whether you built what users actually need—or what you assumed they needed.

If you’re evaluating AI vendors or designing an internal solution, ask yourself: Have we talked to 100+ end users about their workflows? If not, you’re flying blind. The best AI products aren’t built in labs—they’re built in conversations with the people who’ll use them every day.