Case Studies
Work We Have
Shipped
AI prototypes turned into production systems. Cloud bills cut by 28%. Real projects with measurable outcomes.
AI SaaS Platform: POC to Production in 8 Weeks
A SaaS company had promising AI models stuck in Jupyter notebooks. We built the LLM, RAG, and MLOps pipeline that got them into production.
Cloud Cost Optimization: $200K+ Saved Annually
An enterprise SaaS company was overspending on AWS by six figures a year. We cut their bill by 28% through FinOps, right-sizing, and architecture changes.
Healthcare AI: Unifying Clinical Data for Real-Time Insights
A healthcare provider had patient data spread across five clinical systems. We built a FHIR-compliant integration layer and added AI analytics on top.
Retail AI: 22% Conversion Lift with Personalized Recommendations
A retail brand was showing the same product suggestions to every visitor. We built a real-time recommendation engine that lifted conversions by 22%.
AI Copilot for Enterprise Support: 60% Ticket Deflection
A B2B SaaS company's support team was drowning in repetitive tickets. We built an AI copilot that pulls answers from their knowledge base, resolves 60% of queries without a human, and cuts average resolution time by 3x.
Real-Time Fraud Detection: $2M Saved in Chargebacks
A fintech company's rule-based fraud system was catching less than half of fraudulent transactions. We built ML models that score transactions in under 50ms and brought detection rates above 92%.
Data Platform Modernization: 6-Hour Reports Now Run in 4 Minutes
A mid-size insurance company was running on 10-year-old on-prem SQL Server with manual ETL jobs that broke every week. We migrated them to a lakehouse on AWS, and reports that took 6 hours now finish in 4 minutes.
AI Document Processing: 80% Faster Claims Review
An insurance company's claims team was manually reviewing 500+ documents per day. We built an LLM-powered pipeline that extracts, classifies, and summarizes claim documents, cutting review time by 80%.
MLOps Pipeline: Model Deployment from 2 Weeks to 2 Hours
A product company's data science team deployed models by SSH-ing into servers and running scripts manually. We built an end-to-end MLOps pipeline that cut deployment time from 2 weeks to under 2 hours.
Internal AI Assistant: 45% Less Time Searching for Answers
Employees at a 2,000-person company spent over 2 hours per day searching Confluence, Slack, and SharePoint for answers. We built a conversational AI assistant that searches across all tools and gives direct answers with source links.
Predictive Maintenance: 35% Less Unplanned Downtime, $1.2M Saved
A mid-size auto parts manufacturer was losing $100K+ per unplanned equipment failure. We built a sensor data pipeline and predictive models that detect failures 48 hours before they happen.
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