What I Built
Meet Loopcall, powered by Sonali, an AI voice agent built to solve a very real problem for small e-commerce businesses.
Incomplete delivery details mean more manual calls, delayed fulfilment, and wasted operational time. But there's another cost that often goes unnoticed: fake COD (Cash On Delivery) orders. When an illegitimate order gets processed, sellers can lose money on shipping, packaging, and failed deliveries.
I wanted to build a solution that tackles both problems without spending a dime on an expensive automation stack.
Sonali reads order details from a local Excel workbook, identifies the flagged concern, and calls the customer to resolve it through a natural, conversational voice interaction. Whether it's a missing pincode or confirming that a COD order is genuine, she focuses on asking the right questions and capturing the response.
But I didn't want to build another voice bot that talks endlessly and leaves the actual work to someone else.
Loopcall closes the loop. Sonali analyses the outcome, marks an order as verified only when the necessary checks succeed, updates the relevant fields in the original workbook, and flags complicated or ambiguous cases for human review.
The system is designed with practical guardrails: concise conversations, controlled processing, minimal unnecessary computation, and human intervention when the agent cannot confidently resolve an issue.
The other important decision was keeping the order data local. Instead of depending on Google Sheets authentication, spreadsheet APIs, or a cloud-hosted database, Loopcall uses an offline Excel workbook as its source of truth.
The goal is to combine zero-cost tooling, local-first data handling, open-source AI, and real-world task completion into something a small business can actually use.
Sonali doesn't just make the call. She helps determine whether the order is legitimate, resolves missing information where possible, and ensures the outcome is recorded.
That's Loopcall: fewer manual calls, fewer avoidable failed deliveries, and a verification process that doesn't stop at the conversation.
Demo
Link : Loopcall - Demo
The demo shows Sonali handling an order verification from start to finish:
Pick an order: Loopcall reads an incomplete or flagged order from the local Excel workbook.
Understand the issue: Sonali identifies the customer's details and the concern that needs to be resolved, such as a missing pincode and an unconfirmed/ unverified order.
Make the call: Sonali initiates an outbound call and guides the customer through a short, contextual conversation.
Evaluate the response: Based on the conversation, she determines whether the required checks are complete or the case needs human review.
Close the loop: She then updates the relevant fields in the original Excel workbook, preserving the order record and saving the outcome.
The important part isn't just hearing an AI agent speak. It's seeing the result reflected in the actual order data. From a flagged concern to a recorded outcome, Loopcall connects the conversation to the action that follows.
Code
GitHub repository: Loopcall
Loopcall is built around a complete order-verification workflow, connecting order selection, concern-specific dialogue, outbound calling, response analysis, and persistent status updates.
The local Excel workbook serves as the source of truth for the demo. Only the fields required for verification are updated, while unrelated order data remains unchanged.
How I Built It
Three open-weight models on one laptop:
Hearing — Whisper-Hindi2Hinglish-Swift, because Indian customers mix languages.
Thinking — Gemma 4 E4B via Ollama, for messy replies the rules cannot place.
Speaking — Kokoro-82M, Hindi voice, same person in every language
Plain rules own the script and the address checks. Gemma only steps in when the customer hedges, asks a question, or goes off-script. That keeps the call short and the bill at zero.
One of my key decisions was to use a local Excel workbook instead of Google Sheets, since that is exactly how the business (Sustainable Universe) also actually maintains their order inventory.
This also removes the need for Google sign-in, spreadsheet API credentials, and a cloud-hosted spreadsheet database. Sonali reads the relevant order details locally, while Loopcall writes the verification outcome back to the same workbook.
The workflow looks like this:
Excel Workbook → Order Selection → Sonali → Outbound Call → Response Analysis → Excel Update
The flagged concern determines what Sonali asks. A missing pincode, for example, should trigger a request to confirm the pincode rather than a generic verification script.
The system also distinguishes a successful verification from a failed or unanswered call. An attempted call alone is not enough to mark an order as verified.
Why Does Open Innovation Matter?
A hosted voice API would charge per minute and upload names, phones, and addresses. A small business making dozens of confirmation calls a day cannot afford that, and should not ship that data out.
Open models meant I could run the whole agent offline, swap Whisper when it failed on Hinglish, and keep Gemma as a helper instead of the whole product. That is what makes Loopcall a side hustle instead of a demo that dies on the invoice.
For Loopcall, open innovation matters in three ways.
Data ownership: Keeping order records in a local workbook gives the business direct control over its primary order data.
Flexibility: Open-weight models and open-source tools can offer more flexibility to customize, evaluate, and adapt AI systems to specific business workflows, subject to their licenses and capabilities.
Practical automation: The objective is not to build an impressive chatbot that stops at generating a response. It is to build an agent that performs a useful task, records the outcome, and knows when a human needs to intervene.
There is an important distinction between local-first and fully offline. Actual phone calls may require an external telephony provider, and hosted AI services may process conversation data outside the machine. Loopcall's architecture should make these boundaries explicit.
For me, open innovation is about having the freedom to choose how these components work together, rather than being locked into one closed ecosystem.
My First User
Sustainable Universe. We ran it on a real flagged order -> missing house number and pincode. Sonali opened in Hindi and asked about the rosemary conditioner. They are willing to pay after the next step which is putting the same conversation on a real phone line.
Prize Categories
- Side Hustle - Challenge week - 2.
- Best Use of Gemma — Gemma 4 E4B is the local brain for intent and off-script replies.
What's Next?
I want to make Loopcall more reliable beyond the demo by improving retry handling, preventing duplicate calls, strengthening response analysis, and routing unresolved cases to human operators.
The broader vision is to make practical AI automation accessible to small businesses, starting with one everyday problem: incomplete orders.
Loopcall brings the conversation and the operational outcome together. Sonali makes the call. Loopcall closes the loop.
Made by - Amrita :)
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