u/Hot_Country_2177

Tired of paying CAs ₹3,000–15,000 for a straightforward salaried return, I spent the last few months building a tool.

How it works: you chat with it like WhatsApp. It asks plain, English questions, no forms, no jargon. Upload your Form 16 and it pre fills most of it. Takes about 20 minutes. At the end it generates your ITR XML ready to upload to the income tax portal.

Covers: salaried income, HRA, home loan, 80C/80D, capital gains on MF/stocks, NRI returns.

Price: ₹499 one time per filing year.

Genuinely want to know:

1. Would you use this or do you prefer a CA/ClearTax?

2. Does ₹499 feel right, too high, or too low?

3. Would you trust an AI with your PAN and salary details?

Not selling anything yet, still in beta. Just want honest feedback before I launch.

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy. Thanks!

https://preview.redd.it/q16r1svpfwyg1.png?width=2816&format=png&auto=webp&s=538312a9c4314011e9280a9fb96bc5450eca9430

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy. Thanks!

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy the MVP this weekend. Thanks!

PS: Post is written by AI so don't eat me for it in the comments.

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy the MVP this weekend. Thanks!

PS: Post is written by AI so don't eat me for it in the comments.