Experience / ChefKart
From late check-ins to more reliable visits.
Allocation, tracking and operational problem-solving across 300+ daily service visits.
- Role
- Operations Executive
- Period
- Jan 2026 - Present
- Location
- Gurugram
Context
The problem
ChefKart runs 300+ service visits a day across Delhi NCR, Gurgaon, Bengaluru and Mumbai, from one-time cooking to party services. In allocation, the goal is a clean, reliable experience for both customers and cooks.
One problem kept hurting that: chefs were going late on visits, which led to 4-5% cancellations, weaker customer retention and a poorer customer experience.
Contribution
What I personally did
Allocation and tracking
My day-to-day work sits at the point where demand has to become a delivered service. I allocate chefs through ChefKart's internal dashboard, working sector by sector within city-wise clusters. Alongside allocation, I track visits in real time and work within company SOPs and SLAs to keep service delivery on track.
Ground-level research and a report to the founders
To understand the late-visit problem, I went beyond the dashboard. I spoke to the entire chef pool about the issues they faced on the ground and analysed the peak hours when delays occurred. I brought those findings together in a report, paired the issues with feasible fixes and presented the recommendations to the founders.
Technical and operational proposals
The recommendations combined operational changes with technical ideas. I pitched geofencing-based check-in, location-based nudges to chefs and pre-approval strategies as ways to address the issues identified in the research.
Team training and an automation prototype
I have also trained junior team members in the work. Separately, I examined how automation could replace manual calling, wrote a report on the approach and built a working prototype myself in n8n. This let me take the idea beyond a written recommendation into something that could be demonstrated.
Proof
From data to decision
Late check-ins were driving 4-5% cancellations. I collected ground-level issues from the chef pool, mapped the peak hours when lateness happened, wrote up the issues with feasible fixes, and took geofenced check-in, location-based nudges and pre-approval proposals to the founders.
Outcome
The result
I improved check-in data by 15% within 30 days, inside the KPI, and brought service failures below 2%.