NotifyParse — Turn WhatsApp, Telegram, and Email Notifications Into Structured Excel Rows Automatically

Q: Who can build NotifyParse — Turn WhatsApp, Telegram, and Email Notifications Into Structured Excel Rows Automatically?

This is a intermediate level project. SMB owners in e-commerce, logistics, and field services who manage operations via WhatsApp groups — estimated 8M SMBs globally using WhatsApp for business ops.

Your ops team is manually copy-pasting WhatsApp order confirmations into a spreadsheet at 11pm. That is a crime against productivity. NotifyParse reads incoming notifications from WhatsApp, Telegram, and Gmail, extracts structured fields using NLP, and pushes clean rows into Google Sheets or Excel in real time — no Zapier tax, no regex nightmare.

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Difficulty

intermediate

What is it?

SMBs in e-commerce, logistics, and field services receive critical business data via WhatsApp groups and Telegram channels — order numbers, lead names, delivery confirmations, invoice amounts — and someone has to manually transcribe it into a spreadsheet. NotifyParse connects to WhatsApp Business API, Telegram Bot API, and Gmail, runs each incoming message through a Claude-powered NLP extraction prompt to pull structured fields, and appends the row to a Google Sheet or Excel Online file. Users define their own field schema once ('extract: order_id, customer_name, amount, address') and it works on every subsequent message. The NLP extraction handles messy natural language and emoji-filled messages reliably — this is exactly what Claude excels at.

Why now?

Claude's structured JSON output mode (released in late 2024) made free-text extraction reliable enough to ship as a product without post-processing cleanup — this was technically painful before and is now a 5-line prompt.

▸Schema builder where user defines fields to extract in plain English once (Implementation: stored as a prompt template in Supabase, injected into each Claude extraction call)
▸Webhook listeners for Telegram Bot, Twilio WhatsApp, and Gmail push notifications appending rows to Google Sheets on each message
▸Extraction confidence score shown per row so user can spot Claude misreads at a glance
▸Message history log with raw input vs extracted output for debugging

Target Audience

SMB owners in e-commerce, logistics, and field services who manage operations via WhatsApp groups — estimated 8M SMBs globally using WhatsApp for business ops.

Example Use Case

Priya runs a 12-person delivery company. Order confirmations arrive in a WhatsApp group as free-text messages. NotifyParse extracts order_id, address, and time_window from each message and appends to her dispatch sheet — eliminating 2 hours of daily manual entry and 8 transcription errors per week.

User Stories

▸As a delivery company owner, I want WhatsApp order messages automatically extracted into my dispatch spreadsheet, so that my team stops spending 2 hours per day on manual data entry.
▸As an e-commerce reseller, I want supplier price quotes arriving in Telegram to auto-populate my pricing sheet, so that I can respond to customers faster than competitors.
▸As a field service manager, I want job completion messages from technicians in a WhatsApp group to log automatically in my tracking sheet, so that I have real-time job status without calling anyone.

Done When

✓Schema builder: done when user types 'extract: name, phone, order_id, amount' and the schema saves and shows as active with a green status indicator.
✓Telegram extraction: done when a test message is sent to the connected bot and a new row appears in the linked Google Sheet within 15 seconds.
✓Confidence score: done when each extracted row in the log shows a percentage score and highlights any field below 70% in yellow.
✓Stripe trial: done when user completes onboarding, a Stripe trial subscription is created, and a welcome email arrives from Resend within 2 minutes.

Is it worth building?

$29/month x 150 SMB users = $4,350 MRR at month 3. $49/month x 100 power users adds $4,900. Total $9k MRR at month 4 — requires 250 paying users via WhatsApp group cold DMs.

Unit Economics

CAC: $20 via Telegram group cold DM outreach. LTV: $340 (avg 10-month retention at $34 blended price). Payback: 1 month. Gross margin: 78%.

Business Model

$29/month for 1 source + 1 sheet. $49/month for 3 sources + unlimited sheets.

Monetization Path

7-day free trial, 50 messages included. Stripe auto-charge at trial end. Upgrade prompt triggers at 40-message trial limit.

Revenue Timeline

First dollar: week 3 via trial conversion. $1k MRR: month 2. $5k MRR: month 5. $10k MRR: month 9.

Estimated Monthly Cost

Claude API: $60 (10k extraction calls at $0.006 each), Twilio: $25, Supabase: $25, Vercel: $20, Stripe fees: ~$30. Total: ~$160/month at launch.

Profit Potential

Full-time viable at $8k–$20k MRR with low churn (ops-critical workflow = sticky).

Scalability

High — add Slack, Discord, and SMS sources, industry-specific extraction templates, and team workspaces.

Success Metrics

100 trial activations in week 1. 30% trial-to-paid conversion. Less than 10% monthly churn.

Launch & Validation Plan

DM 20 Telegram group admins in logistics and e-commerce niches offering free setup in exchange for weekly feedback before writing any code.

Customer Acquisition Strategy

First customer: join 5 active Telegram business groups and offer free 30-day setup to the group admin — target 3 activations from 5 conversations. Ongoing: Facebook ads to WhatsApp business owners, r/entrepreneur posts with demo GIF, cold email to Shopify store owners using Outscraper.

What's the competition?

Competition Level

Low

What's the roadmap?

Feature Roadmap

V1 (launch): Telegram and Gmail sources, schema builder, Google Sheets output, extraction log. V2 (month 2-3): WhatsApp source, extraction templates by industry. V3 (month 4+): Slack and Discord sources, team workspaces, Excel Online output.

Milestone Plan

Phase 1 (Week 1-2): schema builder, Telegram webhook, Claude extraction, Sheets append ships. Phase 2 (Week 3-4): Gmail source, extraction log UI, Stripe billing ships. Phase 3 (Month 2): 30 paying customers via Telegram group outreach.

How do you build it?

Tech Stack

Next.js, Claude API for extraction, Twilio WhatsApp API, Telegram Bot API, Gmail API via Google OAuth, Google Sheets API, Supabase — build in Cursor, UI with v0.

Suggested Frameworks

Anthropic Claude SDK, Google APIs Node.js client, Telegraf for Telegram bot

Time to Ship

3 weeks

Required Skills

Claude API prompting, Telegram Bot API, Google Sheets API, Next.js webhooks.

Resources

Anthropic API docs, Telegraf docs, Google Sheets API quickstart, Twilio WhatsApp docs.

MVP Scope

app/page.tsx (landing), app/dashboard/page.tsx (schema builder + log), app/api/webhook/telegram/route.ts, app/api/webhook/whatsapp/route.ts, app/api/webhook/gmail/route.ts, lib/extract.ts (Claude NLP extraction), lib/sheets.ts (Google Sheets append), lib/db/schema.ts (schemas, messages, extractions tables), components/SchemaBuilder.tsx, seed.ts, .env.example.

Core User Journey

Sign up -> connect Telegram bot -> define extraction schema -> receive first message -> see extracted row in Google Sheet within 10 seconds.

Architecture Pattern

Inbound message hits webhook route -> stored raw in Supabase messages table -> Claude API called with user schema template -> extracted JSON validated -> Google Sheets API appends row -> extraction log updated with confidence score -> Resend sends daily digest email.

Data Model

User has many Schemas. Schema has many SourceConnections. SourceConnection has many RawMessages. RawMessage has one Extraction. Extraction has fields as JSON and confidence score.

Integration Points

Claude API for NLP extraction, Telegraf for Telegram bot webhooks, Twilio for WhatsApp, Google Sheets API for row appending, Gmail API for email parsing, Supabase for storage and auth, Stripe for billing.

V1 Scope Boundaries

V1 excludes: Slack, Discord, SMS sources, team workspaces, custom AI model training, Excel Online (Google Sheets only), mobile app.

Success Definition

An SMB owner with no coding skills connects their Telegram group, defines a 4-field extraction schema, and wakes up to a fully populated Google Sheet with zero manual entry.

Challenges

WhatsApp Business API approval is the real acquisition blocker — users need a Meta-approved number, which takes 48-72 hours and scares non-technical SMB owners. Build a Telegram-first onboarding flow and add WhatsApp as upgrade.

Avoid These Pitfalls

Do not start with WhatsApp as the primary onboarding channel — Meta approval delays will kill your first week momentum. Do not store raw message content indefinitely without a clear retention policy or GDPR will bite you. Finding the first 10 paying SMB customers requires showing up in their Telegram groups, not just posting on ProductHunt.

Security Requirements

Supabase Auth with Google OAuth. RLS on all user-scoped tables. HMAC webhook signature validation on all inbound webhooks. Message content encrypted at rest in Supabase. GDPR deletion endpoint required.

Infrastructure Plan

Vercel for Next.js API routes. Supabase for Postgres and auth. Google Cloud for Gmail push notifications. Sentry for error tracking. GitHub Actions for CI deploy.

Performance Targets

Extraction per message under 3 seconds end-to-end. Google Sheets append under 1 second. Support 500 messages per day per user without rate limiting issues.

Go-Live Checklist

☐Security audit complete.
☐Payment flow tested end-to-end.
☐Error tracking (Sentry) live.
☐Monitoring dashboard configured.
☐Custom domain set up with SSL.
☐Privacy policy and terms published.
☐5+ beta users signed off.
☐Rollback plan documented.
☐Launch post drafted for ProductHunt and Reddit.

First Run Experience

On first run: dashboard shows a pre-built demo schema ('order_id, customer_name, amount, address') and a sample extraction log with 5 demo messages already parsed. User can immediately: send a test message to the demo Telegram bot and watch a new row appear in a shared demo Google Sheet. No manual config required: demo bot and sheet are pre-connected, no API keys needed until user connects their own source.

How to build it, step by step

1. Define Supabase schema for users, schemas, source_connections, raw_messages, and extractions tables with field JSON column. 2. Run npx create-next-app and install Anthropic SDK, Telegraf, Google APIs client, Supabase JS. 3. Build schema builder UI in components/SchemaBuilder.tsx with v0 — plain English field definitions stored to Supabase. 4. Set up Telegraf bot webhook handler at /api/webhook/telegram that stores incoming messages. 5. Implement lib/extract.ts that builds a Claude prompt using the user's schema and returns structured JSON. 6. Implement lib/sheets.ts that appends extracted row to the user's connected Google Sheet via OAuth. 7. Build extraction log UI showing raw message, extracted fields, and confidence score side by side. 8. Add Stripe billing with 7-day trial and auto-charge using Stripe Checkout. 9. Add Gmail push notification webhook for email source as second connector. 10. Verify: send a free-text Telegram message with order details, confirm extracted row appears in Google Sheet within 10 seconds without any manual action.

Generated

May 12, 2026

Model

claude-sonnet-4-6

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Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.