AI Automation
Case Studies
Discover how leading companies leverage our AI agents to transform operations, accelerate innovation, and achieve measurable business outcomes.
How We Built a Gong Competitor for $400 in 3 Days
Using AI-assisted development, we built Callzeno—a production conversation intelligence platform competing with $100M+ funded companies—in just 72 hours.
The Problem
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 brilliantly, but 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.
What We Built
Callzeno is a full-featured conversation intelligence platform:
This isn't a demo—it's a production SaaS platform competing with companies that have raised hundreds of millions of dollars.
The 72-Hour Timeline
- Database schema design
- API structure
- Job queue setup with Bull + Redis
- OpenAI API integration
- Transcription pipeline
- Speaker diarization
- Sentiment analysis
- Performance scoring & coaching
- User authentication
- Real-time dashboard interface
- Billing integration
- Deploy to production
For comparison: A traditional development approach would typically take 4-6 months and cost $150,000-$300,000.
The Secret: AI-Assisted Development
We didn't cut corners—we used AI-assisted development tools that fundamentally change the economics and speed of software development. Using Cursor, an AI-powered code editor, we could describe what we needed and get complete implementations in seconds.
"Create a Bull queue system with parallel processing, retries, and monitoring"
"Optimize PostgreSQL for high-volume concurrent inserts with full-text search"
"Implement adaptive rate limiting with retry logic and queue management"
The Architecture
Backend
Frontend + AI
The Hard Problems We Solved
Processing 1,000 Calls/Minute
OpenAI has rate limits. PostgreSQL can only handle so many concurrent writes. How do you process at scale?
- Parallel worker pools that scale based on load
- Intelligent batching of API requests
- Adaptive throttling based on real-time rate limits
API Timeout Handling
Long recordings (30+ min) would timeout. Customers paid for those calls—we couldn't just fail.
- Auto-chunking for recordings over 20 minutes
- Fallback to slower, more reliable processing
- 95%+ success rate on first attempt
Real-Time Dashboard Updates
Users want to see calls appear and get analyzed in real-time. Polling is inefficient.
- Server-sent events (SSE) for instant updates
- PostgreSQL LISTEN/NOTIFY for DB changes
- Reduced support tickets by ~70%
Cost Optimization
At $1/call pricing, every call needs to be profitable.
- Efficient prompting for fewer tokens
- Caching common analysis patterns
- Smart model selection (Instant vs Thinking)
The $400 Development Cost
What We Spent
- AI API costs during development~$400
- Cursor Pro subscription~$20/mo
- Infrastructure (dev + staging)Minimal
- Team size1 developer
Per-Call Economics
Compared to hiring a traditional dev team at $150-200/hour for months, the cost difference is staggering.
This took 3 days. Not 3 weeks. Not 3 months. 3 days.
What We Got Right
- 1Rock-solid queue system — Bull with Redis from day one saved countless debugging hours
- 2Separated transcription from analysis — Makes retries easier and allows independent improvements
- 3Comprehensive logging — When something goes wrong, logs tell us exactly what happened
- 4Real-time status updates — Reduced support tickets by ~70%
- 5Cost tracking from day one — Know exactly what margins are at all times
What We'd Do Differently
- 1Better error messages from the start — Early errors were cryptic and cost debugging time
- 2More aggressive caching — Added later; would have reduced API costs by 15-20%
- 3Automated testing earlier — Cursor can generate tests instantly; should have used TDD
- 4User feedback loops sooner — Should have shown to potential users immediately
The Bottom Line
The economics of software development have fundamentally changed. What used to require months and hundreds of thousands of dollars can now be done in days.
Traditional Development
- 4-6 months timeline
- Team of 5+ developers
- $150,000 - $300,000+ budget
- High risk, unclear outcomes
AI-Assisted Development
- 3 days to 2 weeks
- 1 developer with AI tools
- $5,000 - $30,000 budget
- Lower risk, rapid validation
Try Callzeno
See what we built. Callzeno is live and available for a free trial.
Want Us to Build Something for You?
We use the same stack and approach that built Callzeno in 3 days.
Renalytix
Renalytix needed to process and analyze complex biomarker data from thousands of kidney disease patients to generate accurate risk predictions. Manual data processing was slow, error-prone, and couldn't scale with their growing patient base.
Our AI Solution
We developed an AI agent system that automates biomarker analysis, integrates with their clinical data systems, and generates real-time risk stratification reports. The AI agents process patient data, identify patterns, and provide actionable insights to clinicians within minutes instead of days.
The AI agents have transformed how we process patient data. What used to take days now happens in real-time, allowing us to help more patients faster.
VericiDX
VericiDX required intelligent automation to manage their kidney transplant rejection prediction platform. They needed to automate patient data collection, test result analysis, and generate personalized risk assessments for transplant recipients.
Our AI Solution
We implemented a comprehensive AI agent platform that automates the entire workflow from patient data ingestion to risk prediction. The system includes intelligent data validation, automated report generation, and integration with hospital EHR systems for seamless clinical workflows.
Autonimate's AI agents have revolutionized our ability to deliver critical transplant insights. The automation has enabled us to scale our operations while maintaining the highest standards of accuracy.
Colonial Van Lines
Colonial Van Lines struggled with manual scheduling, route optimization, and customer communication across thousands of moves annually. Their team spent hours on phone calls, email coordination, and manual logistics planning.
Our AI Solution
We deployed AI agents that handle customer inquiries, optimize moving routes in real-time, automatically schedule crews and trucks, and provide proactive updates to customers. The system integrates with their CRM, dispatch software, and customer communication channels.
The AI agents handle everything from initial quotes to final delivery updates. Our team can focus on the actual moves while the AI manages the logistics. It's been a game-changer.
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