Sheshgiri Kamath, CEO and Co-founder of Kapture CX, explains how agentic AI, automation and real-time customer insights are helping retailers manage festive sales spikes
Festive sales often lead to a sharp increase in customer queries, putting pressure on retailers to manage higher support volumes without escalating operational costs. AI-powered customer experience solutions are increasingly helping businesses automate routine interactions, streamline order and refund management, and improve service delivery across channels.
In an interview with BW Retail World, Sheshgiri Kamath, CEO and Co-founder of Kapture CX, discusses how agentic AI is transforming retail customer service, the growing role of autonomous AI agents, and why retailers need to build more intelligent and scalable customer operations to prepare for recurring demand spikes.
With festive sales driving a surge in customer interactions, how are retailers using Kapture CX’s AI-powered customer experience solutions to manage higher query volumes without proportionately increasing support costs?
Festive sales compress several weeks of customer demand into a few days, while seasonal hiring adds to costs, onboarding time and quality risks. Kapture CX helps retailers absorb this surge through AgentOS, its agentic operating system.
AI agents on the platform instantly resolve high-volume queries such as “Where is my order?” and “How do I start a return?” across chat, WhatsApp, email and voice channels by retrieving live data from order and logistics systems. Capacity scales automatically with incoming query volumes, allowing human agents to focus on complex cases such as payment disputes and high-value escalations.
Ahead of each sale, Kapture’s CX load prediction tool forecasts query volumes, helping retailers plan their human support teams more accurately.
From order tracking and product queries to payment failures and refund delays, which customer service bottlenecks can AI agents resolve autonomously, and where does human intervention remain critical?
Autonomy on Kapture’s platform is governed by risk. Retailers define policy guardrails that determine what an AI agent can execute independently and what must be escalated to human teams.
Typically, low-risk, rule-based transactions such as order tracking, payment status checks and eligible cancellations can be handled autonomously. However, disputed payments, suspected fraud, policy exceptions and sensitive complaints require human intervention.
Kapture’s orchestration layer routes these escalations to the appropriate human team, along with the complete interaction history, ensuring that customers do not have to repeat themselves. Its audit layer ensures that every AI and human interaction adheres to defined quality standards.
Together, these capabilities allow retailers to progressively expand the scope of AI autonomy as confidence in agent performance grows, while maintaining complete traceability at every stage.
Which festive season use cases, such as order tracking, payment failures, cancellations and refund requests, are best suited for AI agents, and how far can these interactions be resolved autonomously?
The most suitable use cases are those governed by clearly defined rules and backed by reliable data. Shipment tracking, stock availability, return eligibility and standard cancellations are among the tasks that can be resolved autonomously.
Payment failures and refunds can also be handled autonomously, provided the agent has API-level access to the order management system, payment gateway and warehouse data. The depth of integration is the deciding factor.
An agent connected to these systems can initiate a refund, confirm the timeline and trigger a fallback process if a downstream service fails, all within a single interaction. For refund claims, Kapture’s AI self-service solution uses optical character recognition (OCR) to verify submitted evidence before approving a request.
The results are evident across Kapture’s deployments. For instance, a leading quick-commerce platform uses Kapture to handle more than 200,000 customer queries daily, achieving a 98% bot resolution rate and a 22% improvement in customer satisfaction. Average handling time has fallen from 4–6 minutes to less than 50 seconds.
With AI managing volumes and human agents handling complex cases, retailers can scale customer service in line with demand while keeping support costs steady. Across its retail deployments, Kapture delivers 99% resolution accuracy.
Kapture owns its entire technology stack, from models and the agentic layer to the user interface, allowing it to optimise each component for retail-specific workflows.
How can retailers use AI-driven insights to identify recurring customer issues, prevent service bottlenecks and improve resolution times during peak shopping periods?
Customer conversations are among the earliest indicators of operational failures. Kapture’s conversation intelligence platform uses intent classification and anomaly detection across interactions in real time, identifying patterns such as delivery delays concentrated in specific PIN codes, rising failures on a particular payment method or refund backlogs building up around a single product line.
These signals can trigger automated actions, including alerting the responsible operations team, sending proactive notifications to affected customers or initiating corrective workflows.
This transforms customer service from a reactive support function into an operational intelligence layer that proactively identifies and addresses gaps.
During peak shopping periods, this translates into fewer avoidable customer interactions, faster resolutions and better visibility into issues affecting the customer experience.
The long-term advantage lies not merely in handling demand more efficiently, but also in preventing recurring problems from reaching customers in the first place.
Based on Kapture CX’s enterprise experience, what trends are emerging in retailers’ adoption of AI agents for customer service? Are there any relevant data points or customer outcomes that demonstrate this shift?
Four key trends are shaping retailers’ adoption of AI agents.
First, retailers are moving towards agentic AI that can execute transactions within core business systems. They are increasingly evaluating technology providers on measurable resolution outcomes. Governance is also gaining importance, with leadership teams placing greater emphasis on auditability, explainability and controlled escalation to human teams.
Second, adoption is increasingly being shaped by customers’ preferred languages and channels. A significant share of India’s retail growth is coming from Tier 2 and Tier 3 cities, where customers often prefer WhatsApp and voice interactions in their own languages. Retailers are deploying multilingual voice and messaging agents to serve this customer base at scale.
Third, retailers are choosing technology partners that can scale alongside their businesses. Croma, one of India’s largest consumer electronics retailers, uses Kapture to manage unified customer service across more than 550 stores in over 200 cities. The same platform supports Croma’s website, Tata Neu, BigBasket, voice, chat and WhatsApp channels.
Kapture’s partnership with BigBasket spans more than seven years. In 2026, BigBasket recognised Kapture with its Ignite Award for enabling scalable, AI-driven customer support.
Fourth, customisation has become a key deciding factor. Every retailer has its own products, policies and fulfilment processes, and generic AI solutions often struggle to accommodate these differences.
Kapture worked closely with Croma to customise workflows, integrations and omnichannel processes for rapid deployment. Its ability to adapt to Croma’s pace and requirements was central to the partnership, highlighting the need for technology providers that can move as quickly as businesses.
Tata 1mg has similarly credited Kapture with tailoring its platform to meet its requirements. Retail brands such as Hamilton Housewares, The White Teak, Plum Goodness and Wow Skin Science also use Kapture, with workflows designed around their respective operating models.
This demand is reflected in Kapture’s growth trajectory. The company has grown fourfold since its Series A funding in 2023 and serves more than 1,000 enterprises across 18 countries. A Kapture survey found that around 60% of retailers consider 24/7 virtual assistance the most transformative application of agentic AI in retail.
Based on Kapture CX’s enterprise experience, what changes are you observing in how retailers approach AI-led customer experience and peak season preparedness? Are there any data points, customer examples or measurable outcomes that illustrate these changes?
Peak season preparedness now begins months before a sale, with a strong focus on knowledge readiness.
Retailers update their AI agents’ knowledge bases with festive catalogues, promotional offers, pricing rules and revised return policies. This ensures that responses reflect the latest commercial terms from the very beginning of a sale.
Human support teams are prepared in parallel. Kapture’s AI co-pilot provides agents with contextual response suggestions and next-best actions, while AI-powered performance coaching identifies skill gaps ahead of peak traffic.
After the season, retailers conduct structured reviews of interaction data to refine policies and improve agent behaviour for the next cycle.
Tata 1mg, one of Kapture’s enterprise customers, has publicly credited the company’s agentic AI capabilities with enabling faster and smarter customer experiences at scale.
Looking beyond the festive season, what operational capabilities should retailers build now to manage recurring demand spikes and move from reactive customer service towards more autonomous customer operations?
Retailers need to move beyond treating every demand spike as a staffing challenge and build operations that can scale intelligently.
The foundation is an integrated data architecture that connects customer, order, payment and logistics systems in real time. Retailers also need clearly defined decision boundaries specifying which actions AI agents can undertake independently.
This must be supported by continuous evaluation frameworks to monitor agent accuracy and compliance, along with feedback loops that convert operational data into model and process improvements.
Running these capabilities on a single agentic platform such as AgentOS can also help avoid the integration overheads associated with multiple point solutions.
