Taking a Failed Technical Prototype to $2M MRR in 18 Months
Challenge
Momentum AI had a high-performing ML model but no production system. The solution could not scale beyond a demo, had no API, no infrastructure, and no monitoring. Investors required traction within 6 months.
Solution
We rebuilt the system from the ground up: production-grade backend architecture, scalable infrastructure, API layer, automated deployment pipelines, monitoring, and data systems.
Results
Scaled from 0 to $2M MRR in 18 months, with 500+ customers, Series A funding, and a production-ready platform supporting enterprise growth.
Momentum AI had solved the hard problem. Their machine learning model predicted customer churn with 92% accuracy, significantly outperforming the industry baseline of ~65%. But it existed as a 2,000-line Python script inside a Jupyter notebook.
It could not scale beyond a demo environment. It had no API, no deployment pipeline, and no infrastructure. At peak, it handled fewer than 10 concurrent users.
Investors were interested. But without a working product, there was no path to revenue. The company had 6 months to prove traction.
The Real Challenge
Turning an ML model into a product is not an extension of research. It is a complete system redesign. The requirements were clear:
- A scalable inference service
- A customer-facing API
- Reliable data storage and retrieval
- Automated deployment and rollback
- Monitoring, logging, and alerting
- Infrastructure that could scale from tens to thousands of users
Wrapping the script was not an option. It would fail under real-world load immediately.
Building the Production System
We rebuilt the platform in phases, focusing on speed without sacrificing long-term scalability.
Architecture and Foundation (Weeks 1–6)
- Refactored the ML model into a containerised service
- Designed REST API (v1) and gRPC interface (v2 for performance)
- Built a production database schema
- Defined AWS-based infrastructure architecture
Backend and Infrastructure (Weeks 3–10)
- Python ML service served via FastAPI
- Node.js API layer for customer integration
- PostgreSQL for transactional data
- Redis for low-latency prediction caching
- Load balancing for horizontal scaling
Deployment and Operations (Weeks 7–14)
- CI/CD pipelines via GitHub Actions
- Infrastructure as Code using Terraform
- Monitoring with Datadog
- Centralised logging and alerting
- Auto-scaling policies configured
Integration and Hardening (Weeks 11–18)
- Customer onboarding workflows
- Webhook-based prediction delivery
- Retry logic and rate limiting
- Admin dashboard for operational control
- Load testing to 1,000 concurrent users
By week 18, the platform was production-ready.
Technical Stack
ML Layer
- Python (TensorFlow / scikit-learn)
- FastAPI for inference serving
- Docker for containerisation
API Layer
- Node.js (Express)
- REST (v1) and gRPC (v2)
- OpenAPI documentation
Data Layer
- PostgreSQL (primary database)
- Redis (caching layer)
- S3 (model artifacts and storage)
Infrastructure
- AWS ECS (container orchestration)
- RDS (managed database)
- Application Load Balancer
- CloudWatch (monitoring)
- CloudFront (distribution layer)
Deployment
- GitHub Actions (CI/CD)
- Terraform (IaC)
- Automated test pipelines
Scaling the Business
With a stable system in place, growth accelerated.
Growth Metrics
Customers
- Start: 0
- Month 6: 50
- Month 12: 200
- Month 18: 500+
MRR
- Start: $0
- Month 6: $25K
- Month 12: $300K
- Month 18: $2M
Infrastructure Cost
- Start: $0
- Month 6: $2K/mo
- Month 12: $15K/mo
- Month 18: $60K/mo
Uptime
- Start: N/A
- Month 6: 99%
- Month 12: 99.9%
- Month 18: 99.95%
The system scaled without requiring fundamental rewrites.
Business Impact
The transformation was immediate.
- Customers could integrate via API instead of manual workflows
- Onboarding became automated and repeatable
- Predictions became billable services
- The company moved from prototype to revenue-generating SaaS
Within 18 months:
- $2M MRR achieved
- 500+ paying customers
- $8M Series A raised
- Acquired in 2025 for $50M+
The engineering platform was cited as a key factor during due diligence.
The Inflection Point
Growth accelerated between Month 6 and Month 12. Not because of new features, but because the system could support scale:
- Infrastructure handled increased load without degradation
- Feature development sped up on a stable foundation
- Team scaled from 3 to 10 engineers without operational breakdown
- No major outages or system failures slowed growth
The platform removed engineering as a bottleneck.
What Made It Work
- Separation of concerns: ML and platform engineering were treated as distinct systems
- Infrastructure built early: Avoided costly rewrites under pressure
- Full automation: Deployment, scaling, and monitoring required no manual intervention
- Observability from day one: Issues were detected before customers experienced them
- Designed for 10× growth: The system scaled without architectural change
Final Thought
A strong model is not a product. The companies that succeed are not the ones with the best algorithms. They are the ones that can deliver those algorithms reliably, at scale, to real customers. Momentum AI already had the intelligence.What they needed was a system that could carry it.
Building AI Products That Scale?
Intagleo Systems helps companies turn ML prototypes into production-grade platforms, design scalable architectures, and build systems that support rapid growth.