APP IDEA #9 · FINTECH · SAAS

AngelMatch — the Google for finding investors doing $28K MRR

Built by Rashid Khasanov, AngelMatch is a searchable database of 125,000+ angels and VCs with verified emails, investment focus, and past deal history. Founders save hundreds of hours they would otherwise spend manually scouring LinkedIn and Crunchbase. $28K MRR, 358 active subscriptions, $854K all-time on TrustMRR.

$28K

MRR (TrustMRR verified)

125K+

Angels and VCs in database

$854K

All-time total revenue

358

Active subscriptions

01 / HOW IT WORKS

What the app actually does

AngelMatch's core product is saved time — not a novel idea, but executed around the one activity founders universally hate most: finding investors. It's a search engine built specifically for that one job.

Search by industry, stage, location, and investor type

Filter 125K+ investors by the specific criteria that matter — not a generic people search, but one that knows what "pre-seed B2B SaaS focused" means in investor terms.

View full investor profiles with contact details

Each profile includes email addresses, LinkedIn, Twitter/X, phone where available, past investments, and the companies they've invested in — all aggregated in one place.

Export leads as CSV or email directly from the platform

Download contacts for outreach or use the built-in email outreach feature to manage investor communications inside AngelMatch itself.

Track investor interactions in a built-in CRM

Log replies, track who has seen your deck, and manage the fundraising pipeline without switching to a separate tool.

02 / INDIA POTENTIAL

Does this work as an India play

India's startup ecosystem is growing fast but fundraising infrastructure is weak — most founders still rely on warm introductions and LinkedIn cold messages. A database tuned for Indian investors, angels, and family offices is a clear gap.

₹2,999/mo

Realistic India-priced entry tier vs AngelMatch's global $39+/month — opens Indian early-stage founders who can't pay in dollars.

India angels

A focused database of SEBI-registered angel investors, family offices, and India-focused VCs, not just US/global investors.

DPIIT

Startup India DPIIT-registered companies actively seeking angel investment — a built-in, addressable market.

Regional

Curated by tier-2 and tier-3 city investors specifically — most global tools are Delhi/Mumbai/Bengaluru only.

03 / THE WEEKEND BUILD

Friday to Sunday, hour by hour

Scoped to search and contact export only — skip the email outreach CRM for v1. The database is the product.

Friday

Evening · 3 hrs

7–8 PM Set up a Next.js project, Supabase for auth/database, and a basic investor profile data structure.

8–9 PM Seed the database with 500-1,000 India-focused investor profiles from public data (Crunchbase, LinkedIn, Startup India network).

9–10 PM Build the search form — filter by industry focus, investment stage, location, and investor type.

Saturday

Full day · 7 hrs

Morning Build the search results grid — investor cards with name, firm, focus areas, and stage.

Afternoon Build the full investor profile page with contact info and past investment history.

Evening Add CSV export and a spreadsheet-style list view for power users building outreach lists.

Sunday

5 hrs

Morning Add a free-tier cap (10 profile views/day, 5 CSV exports) and Razorpay paywall for unlimited access.

Afternoon Polish search UI for mobile — most founders will search from their phone.

Evening Test end-to-end with real investor names, record the demo for your first post.

04 / APP STACK

What you're actually building with

Next.js 14

Frontend + search UI

Search form, results grid, and profile pages in one framework.

Supabase

Auth + database

Investor database and user accounts with Postgres full-text search.

Postgres full-text search

Search engine

No external search infrastructure — Supabase handles filtering and keyword matching.

Razorpay

Payments

₹2,999/month subscription for unlimited search and CSV exports.

Cloudflare

CDN

Caches search results for fast filter response on large datasets.

Tailwind CSS

Styling

Fast enough to build the search filters and investor card grid in a weekend.

05 / WHERE & HOW TO DEPLOY

Going live

Where: Vercel for the app, Supabase for the database. The main ongoing work is database quality — keeping investor data accurate and growing is the real operational challenge.

Push your project to GitHub, import into Vercel — auto-detects Next.js, no config needed.

Add environment variables: SUPABASE_URL, SUPABASE_KEY, RAZORPAY_KEY.

Deploy — Vercel gives you a live .vercel.app URL in under a minute.

Seed the database with your initial India-focused investor dataset before going live.

Point a custom domain at it from Vercel domain settings.

06 / MARKETING & REVENUE

Getting paying users

How to market it

Post the "find your ideal investor in 30 seconds" demo as a reel — show the search filters in action against a real Indian startup category.
Run 10-20 reels/day across multiple accounts targeting different founder types: D2C founders, SaaS founders, edtech founders.
Get featured in Startup India, iSPIRT, and NASSCOM communities — this audience is directly looking for investor databases.
Offer a free plan (10 profile views/day) with no card required — the first search that finds a relevant investor sells the upgrade itself.
Partner with one startup accelerator or incubator for bulk access in exchange for featuring their portfolio companies.

Who pays, and why

Early-stage Indian founders raising pre-seed to Series A who need more investor introductions than their network provides.
First-time founders who do not have the network to know which investor to even approach.
Accelerator program managers needing to match cohort companies with relevant investors.

Scenario

Paying users/mo

Revenue/mo

Slow start

100 users x Rs2,999

Rs2,99,900

Startup community traction + reels

800 users x Rs2,999

Rs23,99,200

10-20 reels/day + accelerator partnerships

3,000 users x Rs2,999

Rs89,97,000

07 / START BUILDING

Paste this into Claude or GPT

This prompt sets up the full build context so the AI scopes, plans, and starts coding the project with you from message one.

BUILD_PROMPT.txt

I want to build an investor database and search tool for the Indian startup ecosystem, inspired by AngelMatch, scoped to ship a working version in a single weekend. Core flow: 1. A searchable database of India-focused angel investors, family offices, and VCs. 2. Each profile shows name, firm, focus areas, past investments, and contact info. 3. Free tier: 10 profile views/day, 5 CSV exports/month. 4. Paid tier (Rs2,999/month): unlimited search and CSV export. Stack: Next.js 14, Supabase (with Postgres full-text search), Razorpay. Deploy: Vercel. Help me step by step: 1. Scope the database schema for investor profiles in Supabase — fields, indexes, how to structure for fast filtered search. 2. Build the search form and results grid first. 3. Build the full investor profile page. 4. Add the CSV export functionality. 5. Implement the free-tier usage cap and Razorpay paywall. 6. Advise on realistic sources for seeding the initial India-focused investor dataset. Keep explanations short and India-context aware. Push me to ship the smallest working version first. If I get stuck, tell me to ask @buildwithkanhaa.