Projects that deliver results

The Problem

A growing retail business was struggling with a legacy PHP monolith that couldn't handle traffic spikes during sales events. Page load times exceeded 8 seconds, mobile experience was poor, and the development team spent most of their time fighting technical debt.

Constraints

• •Zero downtime migration required—revenue couldn't stop
• •Existing inventory and order management systems had to be preserved
• •SEO rankings needed protection during migration
• •Timeline: 4 months to peak season

Our Approach

We implemented a strangler fig pattern, gradually replacing PHP pages with Next.js while maintaining the existing backend. Server-side rendering ensured SEO continuity. We set up a staging environment mirroring production traffic to validate performance before cutover.

Outcomes

• 70% reduction in page load time (8s → 2.4s)
• 45% improvement in mobile conversion rate
• 99.9% uptime maintained during 3-month migration
• Development velocity doubled post-migration

The Problem

A logistics company needed real-time visibility into their fleet operations. Their batch-processing system had 15-minute delays, making it impossible to respond to delivery exceptions in time. Drivers and dispatchers were making decisions with stale data.

Constraints

• •50K+ events/second from 2,000+ vehicles
• •Dashboard latency under 1 second
• •Data retention for compliance (7 years)
• •Cost-effective scaling during peak hours

Our Approach

We designed an event-driven architecture using Kafka for ingestion and Kubernetes for elastic compute. Real-time aggregations were handled in-memory with Redis, while historical data flowed to a data lake for long-term analytics. Auto-scaling ensured cost efficiency.

Outcomes

• Sub-second dashboard updates (from 15 minutes)
• 50K events/second sustained throughput
• 30% reduction in delivery exceptions
• 40% infrastructure cost reduction via auto-scaling

The Problem

A professional services firm processed thousands of documents monthly—contracts, invoices, correspondence. Manual review was slow, error-prone, and couldn't scale. Extracting key data points for their systems required hours of tedious work per document batch.

Constraints

• •High accuracy required for legal documents
• •Sensitive data handling (client confidentiality)
• •Integration with existing document management system
• •Must handle 50+ document formats

Our Approach

We built a multi-stage pipeline: OCR for document ingestion, LLM-based classification, and structured extraction using fine-tuned prompts. A human-in-the-loop review step handled edge cases. All processing happened in their private cloud for data security.

Outcomes

• 85% reduction in manual processing time
• 94% classification accuracy (up from 78% rule-based)
• Processing capacity increased 10x without additional staff
• ROI achieved in 4 months

The Problem

A B2B SaaS company's monolithic architecture was holding them back. Deployments were risky (3-hour windows), one component's issues affected the entire system, and teams couldn't ship features independently. Customer growth was straining the single database.

Constraints

• •Active user base couldn't experience disruptions
• •Multi-tenant data isolation requirements
• •Team needed to ship features during transition
• •Limited DevOps expertise in-house

Our Approach

We identified bounded contexts and extracted services incrementally, starting with the most painful bottlenecks. API gateway handled routing during transition. We established CI/CD pipelines, observability, and runbooks before each service went live.

Outcomes

• Deployment time reduced from 3 hours to 15 minutes
• Zero customer-facing outages during 6-month transition
• Teams shipping features 3x faster
• Infrastructure costs reduced 25% through right-sizing

The Problem

A healthcare network needed to modernize patient engagement. Patients called for basic tasks, staff was overwhelmed, and competitors offered online scheduling. Privacy requirements and existing EMR integrations made off-the-shelf solutions unsuitable.

Constraints

• •PIPEDA and provincial health privacy compliance
• •Integration with legacy EMR system
• •Accessibility requirements (WCAG 2.1 AA)
• •Multi-language support (English and French)

Our Approach

We built a Next.js application with a focus on security: end-to-end encryption, comprehensive audit logging, and strict access controls. The EMR integration used HL7 FHIR standards. Accessibility was tested with screen readers and verified by third-party audit.

Outcomes

• 40% reduction in phone call volume
• Patient satisfaction scores increased 25%
• Zero security incidents in first year
• WCAG 2.1 AA certification achieved

The Problem

A distributor with 10,000+ SKUs struggled with inventory management. Manual forecasting led to stockouts on popular items and overstock on others. Cash was tied up in slow-moving inventory while customers waited for popular products.

Constraints

• •Highly seasonal demand patterns
• •Long lead times from international suppliers
• •Limited historical data quality
• •Integration with legacy ERP

Our Approach

We combined traditional time-series forecasting with ML models that incorporated external factors (weather, economic indicators). An automated reordering system considered lead times, safety stock, and supplier constraints. Dashboards gave buyers visibility and override capability.

Outcomes

• 60% reduction in stockout events
• 18% reduction in average inventory levels
• Forecast accuracy improved from 65% to 88%
• Annual savings of $2M+ in carrying costs