Services
I help B2B SaaS and enterprise teams define the right problem, design AI-native workflows with human control, and ship systems that work in real operations.
Define, redesign, and structure critical workflows for B2B SaaS tools
I help teams working under pressure define and restructure execution workflows, especially in operations, finance, logistics, and support.
• B2B SaaS platforms and internal tools.
• Workflows across operations, finance, logistics, and support.
• Dashboards, information architecture, exception handling, and escalation paths.
• Audits to identify friction, failures, manual work, and misaligned workflows.
• Connecting current workflow problems to a realistic product roadmap.
✓ End-to-end workflows teams can actually follow.
✓ Fewer errors and less coordination overhead.
✓ Faster execution and better visibility into next actions.
✓ Clearer direction, stronger alignment, and a realistic path.
✓ A roadmap that balances quick wins with medium-term evolution.
Define and design AI-native human+agent systems of action (from discovery to delivery)
I help teams define human+agent workflows that support judgment, add guardrails, and keep critical decisions under human control—from early discovery to implementation.
• Human-in-the-loop systems and AI decision interfaces.
• AI-driven patterns for routing, summarizing, and proposing actions.
• Risk-aware workflows where mistakes are costly (finance, compliance, ops).
• Governance rules for when AI should act, suggest, or wait for approval.
• Reusable rules and learnings, not just prompts.
• Translating AI opportunities into roadmap items and phased delivery.
• Workflow routing and escalation in n8n.
• RAG-based workflows that deliver timely, relevant information into the right step.
• API orchestration and webhook integrations across tools.
• Managing tools like Perplexity as digital coworkers for research and automation.
✓ AI that improves decisions and execution instead of creating noise.
✓ Human accountability over automation.
✓ Product teams that treat AI as part of the system, not as a feature.
✓ A maturity framework to sequence AI adoption safely.
✓ Durable human+agent collaboration patterns that evolve over time.
Define, build, and scale design systems for complex platforms
I view design systems as a visual API between design and engineering. They help teams ship faster, stay consistent, and improve products through safe, incremental changes.
• Figma systems built with primitives, tokens, and component variables.
• Tokens and components mapped to implementation primitives (Tailwind, ShadCN, Radix, etc.).
• API-first UI patterns for configurable enterprise products.
• Clear documentation for appearance and behavior.
• Prioritizing improvements using the design system.
• Systems as Figma libraries and v0/Cursor structures.
• Example flows for faster code integration.
• Figma as a machine-readable source for future AI and automation.
• Design systems that stay aligned with engineering constraints and roadmap changes.
• Scoping legacy CRM/ERP and cloud-based platforms down to the workflows, roles, and underlying data model that actually drive adoption.
✓ Faster development and less inconsistency across teams.
✓ Shared mental models across product, design, and engineering.
✓ A design system that evolves with the product.
✓ Clearer handoffs and fewer gaps between design intent and shipped code.