Enterprise AI Systems Engineer (RAG, Agents, Automation) I build production AI systems with grounded retrieval, deterministic workflows, guardrails, and secure delivery for serious B2B teams.
System snapshot
Operational05/2025 - 03/2026
2024 - Present
Applied AI systems engineered for production: grounded retrieval, validation-first orchestration, security boundaries, and maintainable delivery for B2B and enterprise teams.
Architecture
Tenant-isolated RAG orchestration
Reliability
Grounded retrieval, citations, and access control
Business
Trusted internal knowledge answers
Problem: Internal knowledge was spread across files and inboxes, so teams lost hours searching for answers and still doubted source reliability.
Solution: FileGPT.dev is a private document Q&A vault with account-scoped access, grounded retrieval, citation-backed answers, and cost-aware orchestration for predictable operations.
Impact: Teams get faster answers without sacrificing trust, because outputs stay reviewable, source-backed, and aligned with internal access boundaries.
Architecture
Document-to-Excel RAG orchestration
Reliability
Validation-first output + formatting integrity
Business
Faster, reviewable security responses
Problem: Enterprise B2B deals stall on massive vendor security questionnaires—often 200-row Excel files—while teams spend weeks mapping SOC 2 reports and internal policies into spreadsheets that break and lose context.
Solution: TrustRespond.ai ingests compliance documents into pgvector, maps questions through an enterprise RAG pipeline, and returns reviewable Excel outputs while preserving workbook structure.
Impact: Typical runs finish in about 12 seconds instead of weeks, helping teams respond faster while preserving reviewability and B2B trust.
Architecture
Guarded URL ingestion + compliance scoring pipeline
Reliability
Policy-grounded reports with abuse-aware controls
Business
Faster AI Act/GDPR decision cycles
Problem: SMBs face rising AI Act, GDPR, and ePrivacy risk but cannot justify expensive enterprise compliance programs.
Solution: ComplianceRadar combines guarded URL scanning, policy-grounded AI analysis, and actionable reporting so teams can triage EU AI Act, GDPR, and ePrivacy risk without legal ops overhead.
Impact: Product and leadership teams move from uncertainty to prioritized remediation faster, while keeping launches aligned with compliance expectations.
Additional systems and case studies available on request.
Impact: Teams reduced coordination overhead, improved service reliability, and gained audit-ready documentation for regulated care delivery.
Impact: Users plan faster with less friction, discover more relevant options, and convert inspiration into completed travel decisions.
How I build systems
A lightweight view of how I structure production-ready AI systems: deterministic flow control around LLM intelligence, with validation and observability built in.
User input
Requests enter through typed interfaces with schema-safe parsing and context capture to avoid ambiguity at the edge.
Reliability cues
Traceable steps and outputs
Validation before delivery
Policy-aware orchestration
Test-drive the core RAG extraction logic that powers my systems. Select a target B2B application and query below to see the animated execution pipeline, guardrails, and citation-grounded output.
I help German industrial and B2B companies optimize time-intensive document workflows with local, GDPR-compliant AI systems—without data leaking into the public cloud. Let's start with a brief discovery call to identify which processes in your team have the most potential.
Inspect the portfolio codebase directly in this mock VS Code environment. Click the files in the workspace explorer to read how different architectural components are engineered.
I build enterprise-ready AI systems focused on RAG, agent orchestration, and automation under real production constraints.
My approach is validation-first: deterministic rules and system boundaries where needed, with LLM reasoning layered on top for speed and explainability. The objective is reliable outcomes, not demo output.
I work on systems where retrieval quality, citations, abuse controls, rate limits, privacy posture, and maintainability matter as much as model quality.
Most of my work sits at the intersection of backend architecture, data pipelines, and applied AI product engineering for B2B and enterprise use cases.
I came to tech through a non-traditional path, which shaped how I engineer systems: understand the real workflow first, then ship software that holds up in production.
Hiring for an AI Systems Engineer or Applied AI role, or need help shipping a production AI workflow? I work with recruiters, hiring managers, founders, and technical leads, and usually reply within 24 hours.