Engineering From Problem to production.

Take AI capabilities from defined business outcomes to evaluated production systems. Prepare approved data, compare models and prompts, and run parallel quality, fairness, security, privacy, and performance evaluations before controlled releases and ongoing monitoring.

• Use-case discovery, risk assessment, and privacy-aware data preparation
• Tracked model experiments and parallel evaluations against held-out datasets
• Approved releases, canary rollouts, and feedback-driven improvements

Build knowledge systems that plan each question, select retrieval tools, and assemble evidence for grounded answers. Combine vector search, web sources, structured data, and calculations in parallel, then rerank context, generate a response, and verify it before returning the answer.

• Query analysis, orchestration, and parallel retrieval across approved sources
• Context aggregation, relevance reranking, and grounded answer generation
• Answer verification, source traceability, and iterative query refinement

Turn business events into durable workflows with validated inputs, clear permissions, and approval checkpoints. Coordinate parallel actions across business systems, documents, notifications, and human tasks, then reconcile outcomes and recover safely from partial failures.

• Authenticated triggers, idempotency controls, and durable workflow state
• Scoped connectors, parallel actions, and auditable human approvals
• Outcome reconciliation, safe retries, compensation, and exception handling

Connect applications and AI agents through versioned APIs and narrowly scoped MCP tools. Authenticate callers, enforce tenant and operation permissions, and validate requests before routing them to business services, events, and data connectors with reliable execution and auditable outcomes.

• Trusted discovery, OAuth authorization, and explicit API and tool contracts
• Schema validation, scoped connectors, and approval for consequential actions
• Timeouts, idempotent retries, contract tests, and correlated audit trails

Coordinate AI delivery agents through scoped MCP tools from reviewed code to verified production releases. Build and test containers, run security scans in parallel, and enforce policy gates before signing, registry publishing, approved deployments, and continuous monitoring.

• Reviewed code and infrastructure, isolated builds, tests, and SBOMs
• Parallel security scans, policy gates, signed images, and registry publishing
• Staging verification, approved canary rollouts, rollback, and AI incident triage

Design layered AI applications on AWS with protected edge access, scoped identity, and private application and AI services. Route model requests by evaluated capability, quality, cost, latency, and data residency, with approved fallbacks and bounded retries. Use load balancing and autoscaling across availability zones, with explicit public, private, and isolated subnets, controlled egress, and a second Region ready for tested recovery.

• Route 53, CloudFront, WAF, identity, API and AI gateways, and policy-based model routing
• Multi-AZ VPCs, ALBs, ECS autoscaling, NAT gateways, and private endpoints
• Cross-Region replication, tested failover, encryption, backups, and telemetry

Keep production AI systems measurable and accountable through correlated telemetry, privacy-controlled evidence, and incident ownership. Evaluate quality, fairness, safety, reliability, and cost in parallel, then turn findings into reviewed changes, controlled rollouts, and verified improvements.

• Production telemetry, redacted evidence, drift alerts, and scoped triage tools
• Parallel quality, safety, privacy, reliability, and cost evaluations
• Regression gates, approved changes, canary releases, and operational learning