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Flipkart’s Returns Process Boosted by TableSprint’s AI

Flipkart, one of India’s largest e-commerce platforms, faced a persistent challenge in its returns management process—delivery personnel often missed cosmetic defects during doorstep exchanges. This issue, though operational on the surface, has had far-reaching business implications, leading to losses and increased handling costs, and customer dissatisfaction. To address this, Flipkart implemented TableSprint’s AI-powered platform and achieved significant results: a 7% improvement in doorstep accuracy, cost savings per unit translating into high ROI and customer satisfaction, and rapid deployment of a scalable, mobile-first solution that empowered their business teams.

Returns Process Gaps

The returns process in ecommerce operations is riddled with inefficiencies. Delivery staff often missed or overlooked product defects—sometimes unknowingly—when accepting items during exchanges. As a result, these faulty items get reintegrated into the supply chain, only to be flagged later during quality checks. This leads to revenue leakage, operational overhead due to additional processing, and a poor customer experience due to inconsistent service. Moreover, the sheer scale of ecommerce operations makes manual inspection unscalable and unreliable.

AI-Powered Task-flow

TableSprint stepped in with an innovative, AI-first solution tailored to this specific use case. The core of the solution revolved around real-time image capture and AI-based defect detection. Delivery personnel used a smartphone interface to photograph returned products at the customer’s doorstep. These images get processed instantly by TableSprint’s AI backend with human touch, which detects defects and relays results back to the staff within seconds. This immediate feedback enables accurate on-the-spot decisions and transparent communication with customers.

Underlying Technology

Technically, the solution was powered by several cutting-edge features of the TableSprint platform. First, the Vibe Code AI assistant allowed business teams to build the application using natural language prompts, reducing the need for a lengthy development cycle. This dramatically shortened development timelines—from months to weeks. Second, TableSprint’s vector database capabilities enabled the storage and processing of image embeddings for real-time AI analysis. Finally, the integration of AgentSprint brought in LLM-driven workflows that supported intelligent processing and automated decision-making, especially across returns triage and quality decision points.

Empowering Teams

One of the key differentiators for Flipkart was the empowerment of its business teams. The business stakeholders used TableSprint to directly prototype, build, and iterate on their workflows. The intuitive, mobile-first UI required minimal training for delivery personnel and fast distribution of the app —critical for nationwide rollouts. With backend systems like Yaantra and E-Kart already plugged in, teams could roll out new features without touching core infrastructure.

Phased Rollout

The implementation process was structured across three phases. The first phase involved rapid prototyping using Vibe Code, where Flipkart’s team built the initial app-based interface and workflow for defect categorization. In phase two, advanced AI features were integrated—such as real-time analysis pipelines, vector column setup, and API-based integration with logistics and repair networks. The final phase focused on deployment and training, during which the app was rolled out to delivery teams with performance monitoring in place. Thanks to the app’s simplicity, the training was minimal, and adoption was quick.

Tangible Impact

The results were striking. Doorstep return accuracy improved by 7% compared to regular shipments, reducing the rate of defective items being incorrectly accepted. This improvement translated to a direct financial impact in per-unit exchange cost savings due to lower reprocessing and fewer customer service escalations. Operational efficiency also increased, with faster decision-making at the doorstep and reduced need for back-and-forth communication between delivery and warehouse teams.

Platform Strengths

TableSprint’s platform features played a central role in this success. The platform offered a robust combination of worksheet-based data tracking, multiple view types for different user roles, and form builders for seamless data capture. Its AI capabilities—like vector column storage and LLM-powered workflows—eliminated the need for separate AI teams or infrastructure. Furthermore, real-time integration with Flipkart’s and E-Kart systems ensured smooth deployment without major technical barriers.

Why It Worked

The success of this implementation highlighted why TableSprint succeeded where traditional platforms often fail. By allowing business teams to take charge, enabling rapid prototyping, and embedding AI deeply into the workflow, TableSprint removed the typical bottlenecks associated with enterprise software development. Its AI-first architecture, native mobile support, and integration-ready agents made it well-suited to Flipkart’s demanding scale, supporting thousands of transactions daily across diverse user roles.

Key Learnings

Several key lessons emerged from this engagement. First, involving business teams early and giving them the tools to build directly accelerated development and adoption. Second, leveraging TableSprint’s built-in AI tools, like Vibe Code and AgentSprint, removed the need for additional AI engineering resources. Third, designing a user-friendly, mobile-first interface helped with fast and wide adoption. Finally, seamless backend integration and continuous performance tracking were essential in proving ROI and optimizing over time.

Looking forward, the same framework used for returns management could be extended to other parts of the supply chain and customer operations. The AI platform could be leveraged for warehouse inspections, automated customer service interactions, inventory predictions, and end-to-end process automation. The Platform itself continues to evolve—adding deeper LLM integrations, enhanced analytics, and more powerful AI agents—making it a future-ready foundation for enterprise innovation.

"Flipkart’s collaboration with TableSprint serves as a blueprint for AI-powered transformation at scale. The partnership delivered immediate and measurable results, including a boost in accuracy and significant per-unit savings. It also showcased how modern AI platforms, when placed in the hands of business teams, can create lasting operational improvements. TableSprint’s blend of no-code development, enterprise-grade AI, real-time integrations, and decision systems enabled Flipkart not just to solve a pressing challenge—but also lay the groundwork for future innovation across its operations."

– Ashutosh Chandel, Business Unit Head - Recommerce

“As a tech-first company, Flipkart constantly focuses on innovation that enhances the customer experience. In addition to the FK Reset app that helps with the technical evaluation of phones, we are now using TableSprint's solution to check for cosmetic defects during doorstep exchanges. With TableSprint’s solution, we now capture real-time images at the customer’s doorstep, which are instantly assessed by our backend agents. The feedback is immediately relayed to the delivery personnel, enabling them to inform customers on the spot and offer the appropriate resolution, thus reducing leakages and improving customer experience.”

– Abhijeet Kumar, CEO & Co-Founder, TableSprint

The Flipkart implementation perfectly demonstrates Tablesprint's core vision  —

democratizing enterprise AI by empowering business teams to build sophisticated solutions themselves.

Using Tablesprint's Vibe Code AI assistant, the teams at Flipkart created a powerful AI-powered application without needing data scientists or lengthy development cycles. This is the future of enterprise software: combining enterprise-grade AI capabilities with Excel-like simplicity, so business teams can solve their problems and deliver measurable results in weeks, not months.