Silent Spend Tracker – A Non-Conversational Agent for Automatic Daily Expense Totals

Venkatesh Prasanna — Fri, 09 Jan 2026 04:20:42 +0000

This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Non-Conversational Experiences

What I Built

Silent Spend Tracker is a non-conversational AI agent that automatically calculates day-wise spending totals from transaction SMS messages.

In real life, users receive multiple SMS alerts for every transaction made through PhonePe, Google Pay, Paytm, UPI, or net banking. While these messages contain all the information, it is scattered, unorganized, and difficult to track daily spending without manual effort.

This agent removes the need for apps, typing, or conversation. It works silently in the background and presents clear daily totals automatically.

No prompts.

No chat.

No manual entry.

Demo

Demo / Prototype link:

( add your GitHub, mockup, or placeholder link here )

Example workflow:

Transaction SMS arrives (UPI / bank / wallet)
Agent detects sender and amount automatically
Transaction is parsed and indexed
Daily totals update instantly
User opens the dashboard and sees:
- Today’s total spend
- Yesterday’s spend
- Weekly overview

This entire flow happens without user interaction.

How I Used Algolia Agent Studio

Algolia Agent Studio is used to orchestrate the automatic ingestion and retrieval of transaction data.

Parsed SMS data such as:

Amount
Date
Payment source (UPI, bank, wallet)
Merchant reference

is indexed into Algolia.

Agent Studio triggers retrieval automatically whenever new data arrives. Algolia’s fast search and filtering capabilities allow transactions to be grouped by date and source, enabling instant calculation of daily spending totals without any user query.

Why Fast Retrieval Matters

This experience depends on immediacy and clarity. Users expect to open the app and instantly see their spending summary without delay or interaction.

Algolia’s fast, contextual retrieval ensures:

Instant aggregation of transactions by day
Smooth updates as new SMS messages arrive
A frictionless, non-intrusive user experience

Because retrieval is fast and automatic, the agent feels invisible yet reliable — exactly what a non-conversational assistant should be.

Thanks for reviewing my submission!

Silent Plumbing Assistant – A Non-Conversational Retail Intelligence Agent

Venkatesh Prasanna — Fri, 09 Jan 2026 03:40:14 +0000

This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Non-Conversational Experiences

What I Built

Silent Plumbing Assistant is a non-conversational, visual-first AI agent designed for small retail environments such as hardware and plumbing stores.

In many real-world retail situations, customers cannot describe what they need because they don’t know English or the technical name of a product. They often rely on gestures, partial words, or showing broken parts. Traditional solutions like search bars or chatbots fail because they require language.

This agent works silently and proactively. When a retailer opens a category like Plumbing, the agent automatically retrieves and narrows the most relevant products based on context such as material type (e.g., CPVC), common sizes (¾”, 1”), and typical retail demand. Large, clear product images are displayed so customers can simply point to the correct item.

No typing.

No chat.

No English required.

Demo

Demo / Prototype link:

( add your link here – GitHub, Figma, or simple hosted page )

Example demo flow:

Retailer opens the Plumbing category
The agent auto-retrieves valves and fittings
Retailer selects CPVC
The agent prioritizes common sizes like ¾” and 1”
Visual results appear instantly
Customer points to the needed item and completes the purchase

Even a basic mockup or static demo illustrates the core intelligence clearly.

How I Used Algolia Agent Studio

Algolia Agent Studio is used as the orchestration layer that decides when and what information should appear, without requiring explicit user queries.

Product data such as:

Category (plumbing, valves, fittings)
Material (CPVC, PVC, brass)
Size (½”, ¾”, 1”)
Product type (ball valve, handle, fitting)

is indexed in Algolia.

When contextual signals occur (for example, a category is opened or a material is selected), the agent triggers retrieval automatically. Algolia’s faceted search and ranking capabilities are used to narrow and prioritize results based on relevance and common demand, transforming a static catalogue into a proactive assistant.

Why Fast Retrieval Matters

This experience depends on instant response. In a retail environment, even small delays break the flow between the retailer and the customer.

Algolia’s fast, contextual retrieval ensures that:

Results appear immediately when context changes
Product narrowing feels natural and effortless
The agent enhances the workflow instead of interrupting it

Because retrieval is fast and precise, the agent feels invisible yet helpful — which is essential for a non-conversational experience.

Thanks for reviewing my submission!

Forem: Venkatesh Prasanna

Silent Spend Tracker – A Non-Conversational Agent for Automatic Daily Expense Totals

What I Built

Demo

How I Used Algolia Agent Studio

Why Fast Retrieval Matters

Silent Plumbing Assistant – A Non-Conversational Retail Intelligence Agent

What I Built

Demo

How I Used Algolia Agent Studio

Why Fast Retrieval Matters