Customer Outcomes

Challenge: Navigate regulatory complexity and deploy foundation models responsibly across 12,000+ employees.

Approach: Built end-to-end governance framework: model inventory, risk assessments, bias testing, audit trails, EU AI Act alignment.

Outcome: 95% model oversight, zero compliance violations, 140+ models governed in 6 months.

Challenge: AI and ML workloads across AWS and Azure spiralling, no cost accountability or optimization levers.

Approach: Multi-cloud cost dashboards, unit economics, commitment planning, anomaly detection, showback system.

Outcome: 34% cost reduction, 22 ML models rightsized, clear cost-per-prediction metrics.

Challenge: Build intelligent document retrieval on unstructured policy and claims data. Speed to market: critical.

Approach: Serverless RAG pipeline on Bedrock, vector embeddings, knowledge base ingestion, LLMOps monitoring.

Outcome: Claims processing time cut 40%, 99.2% vector search accuracy, 3-month deployment.

Challenge: Equip 3,000+ indirect channel partners with AI-powered lead scoring and follow-up automation.

Approach: Claude API integration, agentic workflow, MCP-based custom actions, white-label dashboard.

Outcome: 52% lift in qualified leads, 25% reduction in sales cycle, live in 16 weeks.

Challenge: Manual month-end financial consolidation and variance analysis took 15 FTEs, 5 days per month.

Approach: AI-powered document extraction, variance detection, multi-currency consolidation, audit trail logging.

Outcome: 85% time saving, close cycle 48 hours, 2 FTEs redeployed to strategic work.

Challenge: Embed responsible AI into product; meet ISO 42001 and healthcare regulations; differentiate from competitors.

Approach: Policy framework, model evaluation, risk register, continuous monitoring, audit-ready documentation.

Outcome: ISO 42001 certified in 12 weeks, trusted by 40+ hospital networks, 3x faster enterprise sales cycle.