Engineering that produces measurable results
A selection of representative engagements. Client names are illustrative for this preview, but the challenges, approaches, and outcomes reflect the work we do.
Rebuilding a Logistics Scheduling Platform for Scale
We rearchitected a legacy scheduling platform into a real-time, cloud-native system — unlocking enterprise sales and cutting infrastructure costs.
Challenge
Northwind’s scheduling platform struggled under growing load, with page loads exceeding eight seconds and frequent downtime during peak hours. The monolithic architecture made every change risky, and enterprise prospects required reliability the system could not guarantee.
Solution
We migrated the platform to a modular, event-driven architecture on AWS, introduced real-time updates via WebSockets, and rebuilt the frontend with a focus on performance and accessibility. A comprehensive test suite and CI/CD pipeline made deploys safe and frequent.
An AI Document-Review Copilot for a Legal Firm
We built a retrieval-augmented generative AI copilot that reviews contracts and surfaces risky clauses — saving attorneys hours every week.
Challenge
Meridian’s attorneys spent significant time on first-pass contract review — repetitive, high-volume work that delayed higher-value analysis. They needed an AI assistant that was accurate, auditable, and respected strict confidentiality requirements.
Solution
We designed a RAG-based copilot grounded in Meridian’s clause library, with human-in-the-loop review, full citation trails, and on-premise deployment to satisfy data-residency constraints. An evaluation harness continuously measures accuracy against labeled examples.
Cloud-Native Modernization for a Retail Chain
We migrated a decade-old monolith to a cloud-native architecture with zero downtime — lowering costs and enabling omnichannel commerce.
Challenge
Atlas operated a monolithic point-of-sale and inventory system that was costly to run, difficult to change, and unable to support new omnichannel experiences. Leadership wanted modernization without disrupting 200+ store locations.
Solution
We executed a strangler-fig migration, incrementally extracting services into containerized workloads on Kubernetes, with an API gateway mediating old and new. Infrastructure as Code and observability gave the team confidence throughout the transition.
From Idea to Seed Round: A Healthcare SaaS MVP
We helped a founder ship a HIPAA-compliant patient-engagement MVP that secured seed funding within months of launch.
Challenge
Cadence’s founder had a compelling vision for a patient-engagement platform but needed a working, HIPAA-compliant product to validate demand and raise a seed round — with a limited budget and an aggressive timeline.
Solution
We ran a focused discovery sprint, then built a production-grade MVP with secure authentication, appointment reminders, and a clinician dashboard — all on a HIPAA-aligned architecture. We shipped in 12 weeks and supported the founder through investor demos.
Intelligent Automation for a Manufacturing Operation
We automated high-volume data entry and reporting workflows — freeing the operations team for analysis and strategic work.
Challenge
Vertex’s operations team spent days each week manually consolidating spreadsheets from factory systems, leaving little time for the analysis leadership needed. Errors and delays were common.
Solution
We deployed an automation layer combining API integrations, intelligent document processing, and scheduled workflows — feeding a unified dashboard. Exception handling routes edge cases to humans with full context.
Real-Time Fraud Detection for a Fintech Platform
We built a machine-learning fraud-detection system that scores transactions in real time — reducing losses without adding friction.
Challenge
Boreal’s rule-based fraud system was catching too many legitimate transactions and missing sophisticated attacks. They needed a real-time ML scoring layer that improved precision without slowing checkout.
Solution
We engineered a streaming fraud-scoring pipeline with sub-100ms latency, trained on labeled transaction history, with explainability features for analyst review and a feedback loop that retrains the model weekly.
Detailed, named case studies are shared privately with prospective clients under NDA. The summaries above are representative of engagement types and outcomes.
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