> forward_deployed
Architecting the AI Solution
Forward deployed architecture starts inside the customer's constraints, not on a clean whiteboard. My pattern: understand the tenant's cloud, identity, and security posture first, then design the system so one codebase survives all of them.
> what this looks like in practice
▸Architected the AI-Infused QE Platform end to end and deployed it into customer-managed AWS, Azure, and GCP tenants: one codebase, no forks, with architecture, compute sizing, and identity (Azure AD, IAM, SSO) adapted per cloud.
▸The deployment stack is boring on purpose: Docker images behind a load balancer, orchestrated on Kubernetes, with OpenSearch for retrieval indexes and S3-compatible object storage per tenant, so any enterprise infra team can operate it without new primitives.
▸Designed the D365 integration for Loop Copilot inside the customer's tenant trust model: Power Automate flows authenticated by internal tenant identity, with direct Dataverse REST via MSAL held in reserve for environments that grant broader permissions.
▸Chose structured event sourcing over vector RAG for Loop Copilot's memory, which killed hallucinated recall while keeping cross-session context. Failure modes drive these calls, not whatever is trending.
> where it shows up
The AI-Infused QE Platform (production, 17 enterprise QA teams at peak), Loop Copilot (Fortune 500 pilot), and WealthOS (in development, 15 frozen architectural invariants and 11 ADRs before the first feature).