How we built a SaaS app for $400

How We Built a Gong Competitor for $400 in 3 Days Using AI-Assisted Development TL;DR: We built Callzeno, a conversation intelligence platform that competes with $100M+ funded companies like Gong and Chorus, for $400 in API costs in just 3 days using Cursor and modern AI development tools. Here's exactly how we did it and what we learned. The Problem We Set Out to Solve Sales teams need conversation intelligence. They need to know what's happening on calls without listening to hours of recordings. They need coaching insights, performance metrics, and visibility into what's actually being said to prospects. Solutions like Gong and Chorus solve this problem brilliantly. But they cost $100+ per user per month with annual commitments. For small and mid-sized sales teams, that's prohibitively expensive. We saw an opportunity: build similar capability using modern AI at a fraction of the cost. So we built Callzeno. In 3 days. What We Built Callzeno is a full-featured conversation intelligence platform: AI transcription with speaker diarization Sentiment analysis tracking emotional tone throughout calls Performance scoring from 0-100 for every call Coaching recommendations specific to each conversation Objection tracking with pattern analysis across teams Custom analysis using AI to answer any question about calls Real-time processing with 2-5 minute turnaround per call The platform processes 1,000+ call recordings per minute and serves paying customers analyzing sales conversations daily. This isn't a demo or proof of concept. It's a production SaaS platform competing with companies that have raised hundreds of millions of dollars. And we built it in 3 days. The Tech Stack Backend: Node.js with Express for the API Bull + Redis for job queue management PostgreSQL for data storage PM2 for process management AI: OpenAI's GPT-5.2 for transcription and analysis Custom prompting for speaker diarization, sentiment, and coaching Parallel processing to handle API rate limits Frontend: Next.js for the dashboard Tailwind CSS for styling Real-time updates using server-sent events The Development Cost: $400 Here's the breakdown: AI API costs during development: approximately $400 Testing transcription accuracy across various call types Fine-tuning analysis prompts Processing sample datasets Quality assurance testing Development tools: Cursor Pro subscription (approximately $20/month) Infrastructure during development: Minimal (local dev + staging server) Team size: One developer (me) Total out-of-pocket: approximately $400 Obviously, this doesn't include my time. But compared to hiring a development team at $150-200/hour for months, the cost difference is staggering. More importantly: this took 3 days. Not 3 weeks. Not 3 months. 3 days from idea to working product. The Timeline: 72 Hours Day 1: Core Architecture Database schema design API structure Job queue setup Basic upload handling OpenAI API integration Day 2: AI Features Transcription pipeline Speaker diarization Sentiment analysis Performance scoring Coaching recommendations Day 3: Dashboard and Polish User authentication Dashboard interface Real-time updates Basic billing integration Deploy to production Total time: 3 days from concept to production-ready platform For comparison, a traditional development approach with a team of developers would typically take 4-6 months and cost $150,000-$300,000. The Secret: AI-Assisted Development The reason we could build this in 3 days wasn't because we cut corners. It was because we used AI-assisted development tools that fundamentally change the economics and speed of software development. Specifically, we used Cursor - an AI-powered code editor built on VS Code. What Cursor Actually Does Cursor isn't just autocomplete on steroids. It's a pair programmer that: Writes entire functions based on natural language descriptions Refactors code across multiple files simultaneously Debugs issues by analyzing error messages and suggesting fixes Explains unfamiliar code in plain English Generates tests based on the code you've written Handles boilerplate so you focus on business logic Real Examples from Building Callzeno Example 1: The Job Queue System Instead of spending hours researching Bull queue best practices, I described what I needed: "Create a Bull queue system that processes call recordings in parallel, handles retries with exponential backoff, and monitors for stalled jobs." Cursor generated the complete implementation - queue setup, worker configuration, error handling, monitoring - in seconds. I reviewed it, tweaked a few parameters, and it worked. Time saved: 4-6 hours reduced to 20 minutes Example 2: Database Schema Optimization When planning for high-volume concurrent writes, I asked: "Optimize this PostgreSQL schema for high-volume concurrent inserts of call transcriptions with full-text search." Cursor suggested: Proper indexes for our query patterns Partitioning strategy for the transcripts table JSONB fields for flexible metadata Optimized full-text search configuration Time saved: 8+ hours of research and testing reduced to 1 hour Example 3: API Rate Limit Handling OpenAI's API has rate limits. At 1000 calls/minute, we'd hit them constantly. I described the problem: "Implement adaptive rate limiting for OpenAI API calls with retry logic, request queuing, and real-time monitoring of rate limit headers." Cursor built a complete rate limiter with: Exponential backoff Token bucket algorithm Queue management Real-time status tracking Time saved: Probably 12+ hours reduced to 45 minutes The Hard Problems We Solved 1. Processing 1,000 Calls Per Minute The challenge: OpenAI's API has rate limits. PostgreSQL can only handle so many concurrent writes. How do you process calls at scale? Our solution: Parallel worker pools that scale based on load Intelligent batching of API requests where possible Retry logic with exponential backoff Adaptive throttling based on real-time rate limit monitoring The workers can process over 1,000 recordings per minute sustained. We've tested it by clearing backlogs of 1,000+ calls and watching them disappear in under a minute. 2. API Timeout Handling Long recordings (30+ minutes) would occasionally timeout during transcription. We couldn't just fail the job - customers paid for those calls. Our solution: Automatic chunking for recordings over 20 minutes Retry with smaller chunks if initial request times out Fallback to slower, more reliable processing mode Real-time status updates so users know what's happening Now we process 95%+ of recordings successfully on first attempt, with the remaining 5% handled by fallback logic. 3. Cost Optimization At $1/call pricing, we needed every call to be profitable while including: OpenAI API costs Infrastructure Support Growth margin Our breakdown: OpenAI API: approximately $0.40/call (transcription + analysis) Infrastructure: approximately $0.10/call (database, servers, bandwidth) Margin: approximately $0.50/call for operations and growth We optimized API costs by: Efficient prompting (getting good results with fewer tokens) Caching common analysis patterns Batching requests where possible Using appropriate models (GPT-5.2 Instant vs Thinking) 4. Real-Time Dashboard Updates Users want to see their calls appear and get analyzed in real-time. Polling is inefficient and creates lag. Our solution: Server-sent events (SSE) for real-time updates PostgreSQL LISTEN/NOTIFY for database changes Optimistic UI updates with eventual consistency Background sync for reliability Users see their uploads immediately and watch the analysis appear in real-time as it completes. What We Got Right 1. Starting with the queue system Building a rock-solid job queue from the beginning saved us countless debugging hours. Bull with Redis has been incredibly reliable. 2. Separating transcription from analysis We process calls in stages: upload, transcribe, analyze, score. This makes retries easier and lets us improve analysis without re-transcribing. 3. Comprehensive logging We log everything. When something goes wrong, logs tell us exactly what happened. This has saved us dozens of times. 4. Real-time status updates Users see exactly what's happening with their calls. This reduced support tickets by approximately 70%. 5. Cost tracking from day one We tracked API costs per call from the first test. This let us optimize pricing and know exactly what our margins are. What We'd Do Differently 1. Better error messages from the start Early errors were cryptic. We spent time debugging issues that better error messages would have caught immediately. 2. More aggressive caching We added caching later. Should have built it in from the beginning - it would have reduced API costs by 15-20%. 3. Automated testing earlier We wrote tests after building features. Should have used TDD. Cursor can generate tests instantly - we should have used this from day one. 4. User feedback loops sooner We waited to get feedback until we thought it was ready. We should have shown it to potential users immediately and iterated based on their input. The Lessons: What This Means for Custom Development Building Callzeno in 3 days taught us that the economics of software development have fundamentally changed. Traditional Development Model: Hire developers at $100-200/hour 3-6 months for complex projects Total cost: $150,000-$300,000+ Risk: High (long timeline, unclear outcomes) AI-Assisted Development Model: One experienced developer with AI tools 3 days to 2 weeks for complex projects Total cost: $5,000-$30,000 Risk: Lower (rapid iteration, faster validation) The difference isn't developer skill. It's the leverage AI tools provide. What This Means for Businesses If you're considering custom development: Don't hire a traditional agency that staffs your project with 5 developers for 6 months. Find a skilled developer who uses AI-assisted development tools and can deliver 10x faster. The developer using Cursor will: Ship faster (days vs months) Cost less ($5,000-$30,000 vs $150,000+) Iterate quicker (changes take hours, not sprints) Maintain quality (AI catches bugs humans miss) What This Means for Developers If you're not using AI-assisted development tools yet, you're competing with one hand tied behind your back. Start with: Cursor for AI-powered coding GitHub Copilot as an alternative GPT-5.2 for architecture discussions and debugging Claude for code reviews and refactoring Learn to: Describe what you want clearly to AI Review and understand AI-generated code Use AI for boilerplate and let yourself focus on business logic Iterate rapidly based on AI suggestions The developers who master AI-assisted development will 10x their output and be able to charge premium rates while delivering faster. The developers who ignore it will be competing on price with everyone else doing manual development. The Bottom Line We built Callzeno - a production SaaS platform competing with $100M+ funded companies - for $400 in API costs in just 3 days using AI-assisted development. What used to take: 4-6 months A team of 5+ developers $150,000-$300,000+ budget Now takes: 3 days 1 developer with AI tools $400 (plus developer time) This isn't a one-time fluke. This is the new normal for software development. If you're building a software product or need custom development, the old rules don't apply anymore. You don't need a big team or a huge budget. You need one skilled developer with the right tools. Try Callzeno Want to see what we built? Callzeno is live and available for a free trial: 50 free call analyses No credit card required Full access to all features Setup in 2 minutes Start your free trial at callzeno.com Want Us to Build Something for You? If you need custom AI development, we use the same stack and approach that built Callzeno in 3 days: Fast: 3 days to 2 weeks for complex projects Affordable: $5,000-$30,000 vs $150,000+ traditional development Proven: Callzeno proves we can deliver production systems Modern: AI-assisted development with cutting-edge tools Schedule a free consultation at autonimate.com/contact Cory Coddington is the founder of Autonimate and creator of Callzeno. He's been building software since running a Wildcat! BBS at age 8 and now specializes in AI-assisted rapid development.

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How we built a SaaS app for $400

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