Picture this: You’re browsing a company’s website at 2 AM, curious about their product but not quite ready to buy. You drop a casual message on their Instagram, then forget about it until you get a perfectly timed WhatsApp follow-up three days later that addresses your exact concerns, followed by a personalized email with a demo that matches your industry. The entire experience feels seamlessly human, yet it’s powered by something far more sophisticated—agentic AI that doesn’t just respond, but acts.
This isn’t science fiction. It’s the reality that companies working with Zigment AI are delivering to their customers every day. While most businesses are still wrestling with basic chatbots that can barely understand simple questions, forward-thinking organizations are embracing a new paradigm of customer engagement powered by AI agents that think, learn, and act autonomously.
Age of Agentic AI
Welcome to the age of agentic AI—where artificial intelligence doesn’t just process queries but orchestrates entire customer journeys across multiple touchpoints with the sophistication of your best sales representative, the availability of a 24/7 support team, and the memory of your most detailed CRM system.
At the forefront of this revolution stands Dikshant Dave, Founder & CEO of Zigment, the serial entrepreneur who has transformed his frustration with low conversion rates into a groundbreaking platform that’s redefining how businesses engage with customers. His journey from building wellness platforms to creating AI agents that drive measurable business results offers profound insights into where customer experience is headed.
As we dive into this exclusive conversation, we’ll explore how Zigment’s agentic AI approach is helping companies like Tata Motors, Godrej Properties, and Nova IVF achieve conversion rate improvements of up to 38% while reducing human effort by 70%. More importantly, we’ll uncover the strategic thinking behind building AI that doesn’t just automate tasks but elevates the entire customer experience to new heights of personalization and effectiveness.
Welcome, Dikshant Dave, Founder & CEO of Zigment
Q1. You’ve been building companies for over two decades. What drew you to focus on AI-powered customer engagement specifically?
DD: All my previous ventures have been in the B2C space so direct customer interactions were the central part of us as a company. In my previous venture (1Balance), one of the defining problems for us as a business was engaging customers at scale. This was just before the GPT 3.5 or ChatGPT came about. So I know how hard it was to do that. We tried chatbots, call centers, nothing was really viable. So when GPT 3.5 was released to developers, one of the problems that came naturally to me was to try and solve this problem.
1Balance
Q2. Your previous venture, 1Balance, gave you a unique insight into conversion challenges. Can you walk us through that “aha moment” when you realized the power of human conversation versus automated forms?
DD: Yeah, thanks for asking that. As I said, customer engagement was a crucial cog in our conversion wheel. We were selling personalized wellness supplements. These were personalized to each individual based on an online assessment that we had in our funnel. We had built a pretty elegant algorithm to do this. The assessment had over 20 questions so it wasn’t frivolous. We knew that if someone completed the test, they would have to be a pretty serious buyer. But we were seeing that even though we fixed the funnel and super optimized it, conversions weren’t happening. So we (founders) started calling/emailing these dropped leads directly and for those who we would reach out to, we converted over 80% (8 out of 10).
Now this was a great outcome but we couldn’t scale this. The call center did not have the conviction and confidence of the founders and chatbots back then were pretty lousy. So we couldn’t solve the problem. In Nov 2022 when ChatGPT came about, the engineer-entrepreneur in me was super excited to see if intelligent, empathetic customer engagement at scale can now be solved.
Traditional Customer Engagement Tools
Q3. Zigment isn’t just another chatbot platform—you call it “agentic AI.” Can you explain what makes this approach fundamentally different from traditional customer engagement tools?
DD: Traditional chatbots follow scripts. They’re reactive, brittle, and limited to predefined flows. Agentic AI, on the other hand, is proactive. It doesn’t just respond, it interprets mood, urgency, and intent across every interaction and takes the next best action automatically. Instead of asking a human team to design endless decision trees, agentic AI adapts in real time, orchestrating customer journeys that evolve naturally. That’s a fundamental shift from automation that feels mechanical to engagement that feels truly human.
Q4. Your Conversation Graph technology seems to be at the heart of Zigment’s capabilities. How does it transform the way businesses understand their customer interactions?
DD: Most businesses only see fragments of their customer data—clickstreams in one place, CRM fields in another, conversations somewhere else. The Conversation Graph stitches all of this into a unified timeline. Every click, every query, every sentiment shift gets logged into one query-ready layer. That means businesses can finally ask holistic questions like, “Where are we losing ready-to-buy leads?” or “How does customer frustration correlate with drop-offs?” It turns messy, unstructured conversations into structured intelligence you can act on.
Partnership with Major Enterprises
Q5. You’ve secured partnerships with major enterprises like GrupoUMA across Latin America and Europe, and Bajaj Europe. What challenges do these large-scale, multi-market deployments present, and how does your platform address them?
DD: Enterprises like GrupoUMA operate across multiple countries, brands, and languages. The challenge is harmonizing customer engagement without forcing a single rigid template. Zigment addresses this by being flexible at the core. Our agents adapt to local context while still feeding into a unified data layer. That means GrupoUMA can see a consistent picture of customer journeys across markets while still giving each market the autonomy to engage in its own voice.
Q6. The expansion into Dubai and growing presence in the USA suggests significant international ambitions. What market dynamics are driving this global expansion strategy?
DD: Two dynamics stand out: one, enterprises everywhere are realizing that structured CRM data captures maybe two percent of customer reality. The rest is conversational, unstructured, and largely untapped. Two, the rise of omnichannel engagement across WhatsApp, Instagram, and voice is not regional—it’s global. These forces create demand everywhere, and our growth in Dubai, Europe, and the US reflects that global pull rather than a push strategy.
Conversion Improvements
Q7. Your clients report conversion improvements of 38% and lead qualification increases of 90%. What specific aspects of agentic AI drive these dramatic improvements?
DD: It’s the ability to read context. When an agent can tell that a lead is “curious but not ready,” the agentic system gets into nurture mode . When it sees “urgent intent,” it accelerates. Above all, when it senses frustration, it de-escalates. These micro-adjustments across thousands of interactions compound into big jumps in qualification and conversion.
Q8. From an analytical perspective, how do you measure the ROI of deploying agentic AI versus traditional customer engagement approaches? What KPIs should CX leaders be tracking?
DD: Beyond conversion rates and qualified leads, CX leaders should track resolution time, cross-channel continuity, and sentiment improvement. With agentic AI, ROI isn’t just higher sales, it’s reduced support costs, fewer hand-offs, and stronger retention. The Conversation Graph makes these KPIs visible by tying actions directly to outcomes.
Unstructured Conversational Data
Q9. Your platform processes both structured data from CRMs and unstructured conversational data. How do you solve the challenge of creating actionable insights from this merged dataset?
DD: We treat them as equals. A CRM update like “lead stage = proposal” sits right alongside a WhatsApp message saying “I need more time.” Both matter. Our system normalizes these signals into one layer so businesses can query them together. That’s how you go from siloed numbers to holistic insight.
Q10. Looking at the competitive landscape, you’re up against both established players like Salesforce and emerging AI startups. What differentiates Zigment’s approach to agentic AI implementation?
DD: Most incumbents bolt AI on top of legacy architectures. Startups often build narrow point solutions. We’re doing neither. Zigment was built ground-up for agentic AI. Engagement, workflow automation, and unified data aren’t modules we stitched together—they’re pillars of one system. That holistic approach is hard to replicate.
Q11. From a technical architecture standpoint, how do you ensure Zigment’s multi-LLM approach (OpenAI, Claude, LLaMA) optimizes for different conversational scenarios while maintaining consistency?
DD: Different models excel at different tasks—some are stronger at reasoning, others at tone or multilingual nuance. Our orchestration layer routes interactions to the best-fit model, then normalizes the output into a consistent experience. The customer never sees the complexity, but they feel the consistency.
Give.org
Q12. Your partnership with Give.org demonstrates a commitment to social impact. How do you see agentic AI transforming customer engagement in the non-profit sector specifically?
DD: Non-profits face the same challenge as enterprises: too many interactions, too few people to handle them. Agentic AI helps them scale outreach without losing empathy. It can guide donors, answer volunteer queries, and follow up on campaigns—all in a way that feels personal. With Give.org, we’re making sure this tech is used responsibly in sensitive sectors.
Q13. Voice capabilities are part of your roadmap. How will real-time voice interactions integrated with backend automations change the customer experience landscape?
DD: Voice adds immediacy. Imagine calling a dealership and the AI not only answers in seconds but also checks inventory, books a test drive, and sends you a confirmation—all while you’re still on the call. Voice plus automation collapses steps that used to take days into minutes. From a marketer’s point of view, she can see the key analysis of these calls in real time.
Cultural Nuances
Q14. As you scale globally, what role does localization play in your agentic AI approach? How do cultural nuances affect AI agent behavior and effectiveness?
DD: Language is only part of localization. Tone, urgency cues, even what counts as polite varies across cultures. Our agents learn to adapt not just linguistically but behaviorally. For example, a direct “Buy now” prompt might work in one market but feel aggressive in another. Agentic AI lets us tune for those nuances at scale.
Q15. Looking ahead, where do you see the intersection of agentic AI and customer experience evolving? What should CX leaders be preparing for in the next 3-5 years?
DD: The next frontier is proactive journeys. Instead of waiting for customers to ask, AI will anticipate needs based on signals and start the right conversations at the right time. CX leaders should prepare for a shift where AI doesn’t just support journeys—it designs and drives them.
Closing
As our conversation with Dikshant Dave draws to a close, what emerges is not just the story of a successful entrepreneur, but a vision of fundamentally transformed customer relationships. His journey from the frustration of low conversion rates at 1Balance to building Zigment’s agentic AI platform reveals a profound truth about modern customer engagement: the future belongs to businesses that can combine the scale of automation with the empathy and intelligence of human interaction.
Dave’s insights illuminate a critical shift happening in customer experience right now. While many organizations are still treating AI as a cost-cutting tool for basic support queries, the most forward-thinking companies are recognizing AI’s true potential as a relationship-building engine. Zigment’s approach—where AI agents don’t just respond but proactively orchestrate entire customer journeys—represents this evolution from reactive customer service to predictive customer success.
The numbers speak volumes: 38% conversion improvements, 1200% ROI, 70% reduction in manual effort. But perhaps more importantly, these metrics represent something deeper—customers who feel heard, understood, and valued throughout their journey. When an AI agent remembers a casual Instagram comment and follows up with a perfectly timed WhatsApp message three days later, it’s not just efficient automation; it’s the kind of thoughtful engagement that builds lasting business relationships.
Expansion Strategy
Dave’s expansion strategy, from the partnership with GrupoUMA across Latin America to the new Dubai office, reflects the global hunger for this level of intelligent customer engagement. Organizations worldwide are recognizing that in an age of infinite digital touchpoints and diminishing attention spans, the companies that will thrive are those that can create seamless, contextual, and genuinely helpful interactions at every customer moment.
The partnership with Give.org adds another dimension to this story, demonstrating that agentic AI’s impact extends beyond commercial success to meaningful social good. When AI can help non-profits engage donors more effectively and volunteers more meaningfully, it becomes clear that we’re talking about technology with the potential to strengthen the very fabric of human connection and community impact.
As we look toward the future Dave describes—where voice interactions trigger real-time automations, where cultural nuances shape AI behavior, where predictive engagement prevents customer issues before they arise—we see the outline of a customer experience landscape that’s more responsive, more personal, and more valuable than anything we’ve seen before.
For CX Leaders
For CX leaders navigating this transformation, Dave’s journey offers both inspiration and practical guidance. The message is clear: the question isn’t whether agentic AI will reshape customer engagement, but how quickly organizations can evolve from viewing AI as a support tool to embracing it as their most sophisticated relationship-building partner.
In Dikshant Dave and Zigment AI, we see not just the present state of agentic customer engagement, but a compelling preview of its extraordinary potential. The age of AI that truly understands, anticipates, and acts on behalf of both businesses and customers isn’t coming—it’s already here, and it’s transforming how we think about every customer interaction, one conversation at a time.
