As AI weaves its way into every corner of enterprise software, leaders in niche verticals like event-tech and travel-tech are confronting a dual challenge: delivering innovation while navigating real-world complexity. For Gaurav Mishra, CEO and Founder of Prinknest Technology LLP, this balancing act is not theoretical—it’s operational, immediate, and global.
With a portfolio spanning enterprise SaaS platforms, immersive event tools, and smart travel ecosystems, Mishra sits at the intersection of digital experience and AI integration. In this exclusive interview with ObserveNow, he opens up about the delicate dance between generative potential and infrastructure limitations, the growing demands of data localization, and why explainability—not velocity—is the true north for trustworthy AI.
Here’s the complete conversation.
- What are the challenges of implementation of AI and its derivatives like Gen AI and Agentic AI in your line of business? Are there any particular infrastructural challenges that you face?
In our line of business, which spans across event-tech, travel-tech, and enterprise SaaS products, the biggest challenge with AI implementation—especially Gen AI and agentic systems—is contextual accuracy and control at scale. Gen AI is powerful for dynamic content, but hallucination risks producing inconsistent or non-factual outputs, especially in B2B, where accuracy is tied to credibility. Agentic AI, promising for autonomous tasks, still lacks robust guardrails. Real-world success depends on prompt chaining accuracy and task boundary definitions, which are unpredictable.
Infra-wise, cost-effective GPU provisioning is a bottleneck. Even with hybrid setups (part cloud, part edge), training or even running fine-tuned LLMs at scale is compute-intensive. We’re currently leaning more towards model orchestration frameworks that allow switching between hosted APIs (like OpenAI or Cohere) and self-hosted smaller models (like Mistral or Ollama) based on workload and sensitivity.
- What are the key strategic considerations and challenges in managing data sovereignty, data residency, and cross-border data flows in your multi-cloud environment, especially with evolving global regulations?
As we start onboarding international clients—especially for our Interactive Floor Plan, visitor management, and expo registration systems—the data residency has become a default design constraint. It’s not a post-deployment patch.
We re-architected modules for database-level geo-fencing to keep PII in-region for compliance. Multi-region deployments with containerized services and RDS splitting help control data. Challenges remain: real-time analytics across regions are complex due to latency/regulation, cross-border dataset training creates compliance uncertainty (exploring federated learning, but it adds overhead), and local compliance (DPDP, GDPR) with APIs like Aadhaar eKYC impacts log retention, previously vital for debugging/audits.
- Give a broader perspective of use cases of advanced AI in CRM platforms in India. Is India ready for the sea change? Can you identify a few gaps?
AI adoption in CRM in India is happening, but not very evenly. In our implementations for event registration and travel B2B platforms, we’re using AI for Intent recognition from enquiries (e.g., classifying travel leads for travel agents based on urgency or budget using NLP), Dynamic segmentation for remarketing (especially useful for expos where user interest shifts quickly), and AI-driven recommendations (like suggesting packages based on past agent behaviours).
We still see gaps:
- Data Quality/Quantity: Indian businesses lack structured, long-term CRM data, hindering model learning.
- AI Explainability: Users distrust arbitrary AI outcomes. Custom dashboards explaining lead scoring or priority tagging add development overhead.
- Localized Language Models: Multilingual NLP is immature in India, excluding Tier 2/3 users with English-only support.
It means, India is ready technically. What’s missing is a push for open CRM ecosystems that allow third-party AI plugins—much like Salesforce’s Einstein, but adapted for Indian SMBs.
- How are you balancing the drive for rapid AI model deployment with the critical need for model explainability, interpretability, and auditable decision-making, particularly in highly regulated or sensitive domains?
We follow a strict “model transparency with human-in-the-loop” framework, especially for sensitive modules. In our visitor verification module, AI flags potential duplicates. We log the decision with full input trace, prediction confidence, and model version, with the user making the final call. SHAP values and counterfactual examples explain why a record was flagged, aiding trust and model debugging.
While complex models aren’t yet in production, our roadmap prioritizes explainability, including structured logs, clear decision thresholds, and model versioning from early testing. We’re exploring Trulens and LangChain monitoring to simulate LLM usage with traceable, auditable outputs.
Our philosophy is clear: if it can’t be explained or monitored, it’s not ready for enterprise deployment. This principle ensures we focus on long-term reliability over short-term hype. This adds a slight delay in our AI enablement, but for us, trust is non-negotiable, especially with public sector clients or financial tools within our travel platform.
- In what ways is AI transforming enterprise customer experience strategies? Furthermore, what are the primary hurdles to overcome when implementing and expanding AI solutions within cloud-based infrastructures?
AI is undeniably reshaping customer experience, especially in making platforms more proactive and context-aware. From guided workflows to chat-based onboarding, the shift is toward anticipating user intent rather than reacting to clicks.
We’re seeing transformation through: AI-driven chatbots reducing support costs while increasing resolution speed, Predictive analytics helping forecast customer behaviours and pre-empt churn, Sentiment analytics refining the context of interactions, and Journey orchestration tools that adapt based on real-time behaviour.
Expanding AI in cloud infrastructure faces challenges: latency, data pipeline complexity, data interoperability with legacy systems, model drift without robust MLOps, and security/privacy concerns (encryption, consent, access control). Additionally, unpredictable inference costs on cloud GPUs (especially for LLMs) pose a significant hurdle. Our immediate goal is AI-readiness, not AI-heaviness, to enable future meaningful deployments.
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Gaurav Mishra’s perspective reveals a grounded but forward-thinking approach to AI adoption in India’s evolving tech economy. By focusing on model transparency, regional compliance, and infrastructure flexibility, Prinknest Technology isn’t rushing into an AI arms race—it’s building systems that are future-proof, trusted, and contextually aware. In a landscape filled with hype, this clarity of purpose—AI that’s explainable, adaptable, and localized—may just be what Indian enterprises need to bridge the gap between experimentation and enterprise-scale impact.
