40%
Architecture decisions. Specific KPIs. Verified outcomes.
Claims processing time cut — AI prediction platform, 12-week delivery.
60%
Fraud detection error rate reduction — rules engine replaced with gradient boosting in production.
$4.2M
Every engagement documented from problem definition through production delivery — not a results slide, but the decision record that got there.
Infrastructure cost eliminated — cloud migration with org-model change preceding lift-and-shift.
Problem. Architecture. Outcome.
AI Claims Prediction Platform
Enterprise Cloud Migration
Predictive Pricing Engine
Problem: on-prem sprawl with siloed teams blocking delivery. Architecture: Azure landing zone, org-model restructure before migration, Terraform IaC, 14-month phased cutover.
Problem: actuarial tables updated quarterly, missing real-time risk signals. Architecture: LightGBM on GCP Vertex AI, feature store in BigQuery, REST API consumed by underwriting UI.
Problem: manual triage created a 9-day average cycle. Architecture: XGBoost model on AWS SageMaker, event-driven pipeline via Kafka, integrated into legacy claims API.
$4.2M annual savings · 99.95% uptime
18% loss ratio improvement · 6-week pilot
40% faster cycle · $200K build cost
Fraud Detection Platform
Customer 360 Analytics
Workflow Automation Platform
Problem: rules-based system generating 34% false positives, flooding investigators. Architecture: gradient boosting ensemble, real-time scoring via Kafka streams, explainability layer for compliance.
Problem: 11 source systems, no unified customer record, 3-day reporting lag. Architecture: Snowflake data mesh, dbt transformation layer, Looker dashboards consumed by CX and underwriting.
Problem: 7 manual handoff steps per policy change, 4.5-day SLA breach rate at 22%. Architecture: BPMN engine on Camunda, RPA for legacy system writes, event-driven audit trail in PostgreSQL.
60% error reduction · $1.1M annual recovery
3-day lag to real-time · 11 sources unified
SLA breach rate to 2% · 7 steps to 1
17+
$6M+
6 weeks
0 pilots
Years of delivery. Every case study here maps to a production system still running.
Documented cost reduction and recovery across these six engagements combined.
Fastest pilot-to-production cycle — Predictive Pricing Engine, business-owned model at handoff.
Remaining as pilots. Each engagement reached production with the business team owning the outcome.
Evaluating an engagement? The full decision record is the conversation.
Bring a program challenge. The discussion starts with your architecture constraints, not a capabilities deck.