Snapshot
The Challenge
Our client operates a payment processing platform serving approximately 900 SMB merchants across the US and Canada. The platform handles payment acceptance, merchant onboarding, settlement, and reporting — processing roughly $14M in monthly transaction volume across 180,000+ individual transactions.
The entire platform ran on a monolithic Ruby on Rails application deployed on Heroku. This architecture had served the company well through product-market fit and early growth, but it had become the primary constraint on the business.
Our client was a solo founder — a 15-year veteran of freight logistics who had identified a gap in the market. Mid-size freight brokers (50–500 shipments/month) were managing operations through a combination of TMS software built in the 2000s, Excel spreadsheets, email chains, and phone calls. The existing TMS platforms were either enterprise-grade (6-figure annual contracts, 6-month implementations) or basic tools that couldn’t handle the complexity of multi-modal freight coordination. The founder had a clear product vision, $180K in pre-seed funding from angels, a LOI from three potential pilot customers, and an investor ready to lead a Series A — contingent on a working product with live customer traction within 4 months.
The constraints were unambiguous:
Budget: $50K maximum for MVP development. The remaining $130K was reserved for the first year of operations, sales, and runway to Series A.
Timeline: 12 weeks maximum to a deployable product. The pilot customers had committed to evaluate for 60 days starting no later than Week 14. The Series A investor had set a milestone: demonstrated product-market fit (defined as 3 paying customers and positive NPS) within 6 months.
Scope discipline required: The founder’s feature wishlist was 47 items long. We needed to cut that to the 12 features that would make pilot customers say “yes, I’ll switch from my spreadsheet.”
The founder had approached three US-based development shops. Quotes ranged from $120K to $220K with timelines of 16–24 weeks. He found Gigabit through a recommendation in a startup Slack community.
Our Approach
Infrastructure Assessment ($6,000)
We audited the existing architecture before proposing a migration plan.
Application analysis
We reviewed the Rails codebase (approximately 85,000 lines), mapped the database schema (47 tables, 12 with >1M rows), identified service boundaries, and documented every external integration (Stripe Connect for payment processing, Plaid for bank verification, SendGrid for notifications, Twilio for SMS, and a custom settlement engine).
Performance profiling
We instrumented the application with detailed request tracing. Key findings: the payment processing path was actually fast (p99: 340ms) but the reporting queries were consuming 60% of database CPU during month-end. The monolith’s shared database meant reporting load directly impacted payment processing latency.
Cost analysis. Current Heroku spend: $14,200/month. We modeled the equivalent workload on AWS using EKS with Fargate: projected $9,200/month at current volume, scaling more efficiently as transaction volume grows.
Migration strategy. We recommended the strangler fig pattern — extracting services from the monolith one at a time, running old and new in parallel, and gradually shifting traffic. This eliminates the “big bang” risk of a full rewrite.
Extraction order (risk-optimized):
- Notifications (lowest risk — if a notification is delayed 30 seconds, no merchant impact)
- Reporting and analytics (read-only workload, no transaction dependency)
- Merchant onboarding (medium risk — not on the payment processing path)
- Webhook delivery (medium risk — partner integrations, but retryable)
- Payment processing core (highest risk — the money pipeline, extracted last with maximum preparation)
Build Sprints
We ran four 2-week sprints with a consistent rhythm:
Monday
Sprint planning. Founder attends via video call. We walk through the tickets, clarify acceptance criteria, and commit to the sprint scope.
Daily
15-minute async standup via Slack (Dhaka team posts updates at end of their day, founder reads at start of his US morning). Sync video standup twice per week during overlap hours for blockers and decisions.
Friday (end of sprint)
Demo of working features deployed to a staging environment. Founder and one pilot customer test live. Feedback captured and prioritized for the next sprint.
Sprint 1 (Week 2–3)
Foundation — auth system (email + Google SSO), tenant management, user roles, database schema, CI/CD pipeline, staging environment. Deliverable: a logged-in user can create a workspace and invite team members.
Sprint 2 (Week 4–5)
Core workflow — shipment creation, multi-stop routing with Mapbox, carrier assignment interface, document upload. Deliverable: a broker can create a shipment, assign a carrier, and upload documents.
Sprint 3 (Week 6–7)
Intelligence layer — real-time tracking with map visualization, automated notifications, status event pipeline, dashboard with KPI widgets. Deliverable: live shipment tracking on a map with automated email notifications at each status change.
Sprint 4 (Week 8–9)
Monetization and polish — Stripe integration (per-shipment billing + monthly invoicing), customer portal, reporting, CSV import for legacy data, mobile responsive testing, performance optimization.
esting and Hardening
End-to-end testing
QA engineer executed 180 test cases covering every user flow, edge case, and error condition. We focused especially on multi-tenant isolation (can Broker A see Broker B’s data? — no), payment handling (what happens when a charge fails mid-invoice? — graceful retry with notification), and concurrent usage (what happens when 3 users edit the same shipment? — optimistic locking with conflict resolution).
Load testing
Simulated 50 concurrent users with 500 active shipments. Response times stayed under 200ms for all major operations. The architecture was validated to scale to 200+ concurrent users on the current infrastructure without modification.
Security review
Input validation, SQL injection prevention (parameterized queries throughout), XSS protection, CSRF tokens, rate limiting on auth endpoints, and proper CORS configuration. Document uploads restricted by type and scanned for malware.
Launch
Production deployment on a Friday morning Dhaka time (Thursday evening US). Zero-downtime deployment via ECS rolling update. DNS cutover, SSL certificate provisioning, and final production smoke tests.
The founder onboarded his first pilot customer that same afternoon.
The Results
Launch metrics (11 weeks, $48,000 total investment):
Business outcomes (first 6 months post-launch)
The Series A: Four months after launch, with 7 paying customers, positive NPS, and a clear growth trajectory, the founder closed a $2.1M Series A at a $6.2M pre-money valuation. The lead investor specifically cited the product quality, rapid development timeline, and capital efficiency as factors in their decision.
What the investor said (per the founder): “Most pre-seed companies show us mockups and a prototype. This founder showed us a production system with paying customers, clean architecture, and a development partner that could scale with them. That’s rare.”
Client Quote
“I talked to three US dev shops and the cheapest quote was $120K for 16 weeks. Gigabit delivered a better product for $48K in 11 weeks. And they didn’t just write code — they challenged my assumptions, cut the scope to what actually mattered, and built an architecture I could grow into. My Series A investors audited the codebase. They said it was cleaner than most Series B companies they’d seen.”
What’s Next
After the Series A closed, the founder transitioned from a fixed-scope MVP engagement to ongoing team augmentation. He retained 3 Gigabit engineers (2 full-stack + 1 QA) as his dedicated development team, operating within his sprint cadence alongside a newly hired US-based product manager.
Current development priorities include carrier API integrations (direct tracking from major carriers), advanced analytics and forecasting, a native mobile app for carrier drivers, and AI-powered shipment optimization that recommends optimal carrier-route combinations based on historical performance data.
Investment Summary
Comparable US agency quotes: $120K–$220K, 16–24 weeks Gigabit delivered: $48K, 11 weeks — 60–78% cost savings, 30–50% faster delivery
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