The Fintech Scalability Challenge
- ✓LatencyReduced from 400ms to 35ms (91% improvement).
- ✓ThroughputSystem successfully stress-tested at 50 million events per day.
- ✓CostReduced AWS bill by 20% by using Spot Instances for stateless worker nodes.
The Challenge
The client had built a successful MVP for algorithmic trading. However, as they onboarded institutional clients, their transaction volume spiked from 10,000 to 2 million events per day. Their existing monolithic architecture (built on a single Django instance) was collapsing under the load. Latency had crept up to 400ms, which meant losing money in the trading world. They needed sub-50ms latency and 99.999% uptime.
Our Solution
We deployed a "Strangler Fig" migration strategy to move from the monolith to an event-driven microservices architecture.
1. Infrastructure Re-Architecture:
We migrated their compute layer from EC2 auto-scaling groups to Kubernetes (EKS). This allowed for millisecond-level scaling during market opening hours. We implemented "Cluster Autoscaler" to aggressively provision nodes when the queue depth increased. We used Prometheus to monitor custom metrics like "Order Queue Depth" and "Processing Lag" to trigger scaling events before the CPU even spiked.
2. Database Optimization:
The single PostgreSQL instance was the bottleneck. We implemented "Read Replicas" for all reporting queries, offloading 80% of the traffic from the primary writer. We also introduced Redis as a caching layer for frequent pricing lookups, reducing database hits by 95%. We optimized slow queries by adding compound indexes and partitioning the largest tables by date.
3. Asynchronous Processing:
We replaced their synchronous HTTP calls with an event bus using AWS Kinesis. When a trade order came in, it was immediately acknowledged (latency < 10ms) and then processed asynchronously by background workers. This decoupled the ingestion speed from the processing speed, ensuring the API never timed out during a market crash.
"PrimarTech didn't just fix the bugs; they re-engineered the engine while the car was driving 200mph. We haven't had a minute of downtime since."
HIPAA-Compliant Data Warehouse
- ✓Reporting TimeReduced from 2 weeks to Real-Time.
- ✓CompliancePassed a rigorous third-party HIPAA audit with zero findings.
- ✓Revenue ImpactIdentified $4M in unbilled claims through better data reconciliation.
The Challenge
The client had data scattered across 15 different silos: EMR (Electronic Medical Records), Salesforce, Zendesk, and archaic on-premise billing systems. Reporting was manual and legally risky. They needed a Single Source of Truth that was fully HIPAA-compliant to analyze patient outcomes and operational efficiency without violating patient privacy laws.
Our Solution
We implemented a Modern Data Stack focused on security and governance.
1. Secure Ingestion:
We used Fivetran to build encrypted pipelines from their cloud tools. For the on-premise billing system, we built a secure VPN tunnel and a custom Dockerized extraction agent that pushed data to an S3 staging bucket. This ensured data never traveled over the public internet.
2. Warehouse Architecture:
We selected Snowflake for its powerful role-based access control (RBAC). We implemented "Dynamic Data Masking" so that PII (Personally Identifiable Information) like Social Security Numbers was automatically masked for analysts but visible to authorized doctors. We set up separate environments for Dev, Stage, and Prod to ensure tested code never touched real patient data.
3. The "dbt" Transformation Layer:
We used dbt to model their business logic. We wrote over 400 models to standardize definitions of "Active Patient" and "Revenue per Visit." We established a "Gold Layer" of data that was certified for executive reporting. We implemented automated tests that would stop the pipeline if data quality dropped below 99%.
4. Audit Logging:
We configured comprehensive logging. Every query run by every user was logged to an immutable audit trail. We built a "Compliance Dashboard" that allowed their Chief Compliance Officer to see exactly who accessed what data and when.
"For the first time in ten years, I trust the numbers I see on my dashboard. We are finally running a data-driven hospital."
Global SaaS Next.js Migration
- ✓PerformanceLighthouse score increased from 32 to 100.
- ✓Conversion RateOrganic traffic conversion increased by 45%.
- ✓Global ReachSuccessfully launched localized sites in 5 regions.
The Challenge
The client's marketing website was a 7-year-old WordPress monolith. It was slow (Lighthouse score of 32), insecure, and impossible for the marketing team to update without developer help. As they expanded into the US and Japanese markets, the slow load times were killing their conversion rates. They needed a global, multi-language site that loaded instantly.
Our Solution
We rebuilt the frontend using Next.js and headless architecture.
1. Headless CMS Integration:
We migrated their content to Contentful. This decoupled the content from the code. Marketing could now launch new landing pages in minutes using a drag-and-drop interface, without writing a single line of HTML. We created custom "Content Models" that enforced brand consistency.
2. Global Performance Strategy:
We deployed the site on Vercel's edge network. We used Next.js Middleware to detect the user's location and serve the correct language (English, German, or Japanese) instantly from the edge node closest to them. This reduced the Time to First Byte (TTFB) to under 50ms worldwide.
3. Core Web Vitals Optimization:
We optimized images using next/image to serve WebP formats automatically. We used code-splitting to ensure that users only downloaded the JavaScript needed for the page they were viewing. We achieved a perfect 100/100 Lighthouse score.
4. Incremental Static Regeneration (ISR):
With thousands of blog posts, a full build took 40 minutes. We implemented ISR to allow content editors to publish changes instantly without rebuilding the entire site. This gave them the speed of a static site with the agility of a dynamic CMS.
"Our marketing team is finally unblocked. We are launching campaigns 5x faster, and the site feels instant in Tokyo, London, and New York."
Logistics Optimization with AI
- ✓Fuel SavingsReduced fuel consumption by 12% in the first quarter ($1.2M annualized savings).
- ✓On-Time DeliveryImproved from 82% to 94%.
- ✓Dispatcher EfficiencyReduced route planning time from 4 hours to 15 minutes.
The Challenge
The client managed a fleet of 500 trucks across Europe. Their routing was done manually by dispatchers using Excel, leading to inefficient routes, high fuel costs, and missed delivery windows. They wanted to use their historical data to automate and optimize route planning.
Our Solution
We built a custom Route Optimization Engine using Python and open-source solvers.
1. Data Consolidation in Snowflake:
We ingested GPS data from the trucks, traffic data from Google Maps API, and delivery manifests from their ERP into Snowflake. This gave us a complete picture of the network reality.
2. The Optimization Algorithm:
We wrote a microservice in Python using the VRP (Vehicle Routing Problem) algorithmic constraints. It took into account truck capacity, driver rest times (EU regulations), and traffic patterns. We deployed this model on AWS Lambda to run nightly.
3. The Dispatcher Dashboard:
We proactively pushed the "Recommended Routes" to a custom dashboard we built in React. Dispatchers could review the AI's suggestions and override them if necessary. The system "learned" from these overrides to improve future predictions.
"The first week we ran the new routes, our fuel costs dropped by 12%. The ROI on this project was less than 3 months."
Deep Dive: Our Engineering Methodology
We do not believe in "hope-driven development." We follow a rigorous engineering protocol derived from our experience at top tech firms.
The PrimarTech
Engineering Standard
A rigorous framework derived from scaling systems at the world's most demanding tech firms.
The 'Walking Skeleton'
Architecture Validation (Week 1)We build a vertical slice of the entire application (Frontend, API, DB, CI/CD) in the first week to expose unknown risks immediately.
Test-Driven Development
Stability by DefaultWe write failing tests before any code. This ensures 100% feature coverage and makes refactoring safe and trivial.
Infrastructure as Code
Versioned & RepeatableZero manual configuration. Servers, DNS, and Databases are defined in Terraform/Pulumi for absolute reliability.
Observability-First
Proactive MTTR FocusTracing and logging are built-in from day one. We detect performance regressions before users ever notice them.
Frequently Asked Questions
While every project has unique challenges, the *engineering principles* we apply are constant. The specific results (20% AWS savings, Sub-100ms latency) are typical for clients who fully adopt our architectural recommendations.
Verification & Transparency
We understand skepticism. The IT industry is full of vaporware. Here is how we prove our claims:
Git History
For every client, we maintain a clean, atomic commit history. You can see exactly who wrote what code and when.
Performance Logs
We archive Datadog/New Relic reports showing the "Before" and "After" latency and error rate metrics.
Reference Calls
We encourage you to talk to the CTOs we've served. We will facilitate the introduction once we determine mutual fit.