Strata — AI-native Action Systemfor Enterprises
An AI platform for the furniture industry that coordinates work among manufacturers, dealers, and installers. I designed the operating layer that unifies execution.
My Role
Leading discovery, workflow and system modeling, interaction design, design systems, and AI-assisted delivery in a live production environment.
This case study covers my work at Agentic Dream, where I was embedded with Avanto on a product that later evolved from Orderbahn to Strata — two iterations of the same AI-powered operations platform for the commercial furniture / commercial interiors industry.
Context
Strata is an AI-native operating system for the commercial furniture industry, coordinating execution between manufacturers, dealers, reps, and installers. I designed the operating layer that defines workflows, decisions, roles, and permissions for safe, scalable execution. (Earlier iterations of this same platform were branded as Orderbahn for the commercial interiors industry; Strata inherits and extends that operations layer.)
What I delivered
AI-Native Role & Permissions Layer
An AI-native layer that models roles and permissions for safe orchestration across teams.Action-First Action Center
An Action Center that replaces dashboards with action-first workflows.Design System for Code Delivery
A Figma design system aligned with code to support incremental delivery.Human-Agent Workflow Readiness
AI-ready workflows and UX patterns that prepare the platform for future human-agent guidance without disrupting current operations.Before
–Teams relied on disconnected workflows, manual coordination, and unclear ownership.
–Data was visible, but not actionable.
–Users struggled with exceptions, permissions, and next-step ambiguity.
After
✓Teams work through a unified, guided system of action.
✓Ownership, urgency, and next steps are surfaced clearly.
✓Managers and operators act faster with fewer errors and less coordination overhead.
Product Foresight & Constraints
Because Strata was already live and embedded in operations, I focused on evolving the system incrementally, reusing components, tightening roles and states, and designing changes engineering could ship without disrupting daily work. I also partnered with Product Managers and Product Owners to translate prioritized workflow states into Jira tickets, keeping execution aligned with the state model and supporting the ~35% delivery speed gain.
Selected Impact
✓Increased visibility and SLA compliance by ~40%.
✓Improved traceability and throughput by ~40% with execution-driven dashboards.
✓Reduced task ambiguity and delays by ~30% using state-based models.
✓Improved delivery speed by ~35% with a multi-tier Figma design system.
📌 Three core problems
Lack of operational clarity
Static dashboards and disconnected tools led to manual coordination, missed context, and inefficiencies at scale.
Data, not decisions
Dashboards showed metrics but didn't guide action or next steps.
Complexity without guidance
Overlapping workflows, exceptions, and permission layers led to errors and slow execution.
🗺️ Process — AI-augmented from research to delivery
Discovery & Research
Stakeholder interviews, workflow mapping, information architecture audits, decision-point analysis, and operational shadowing, anchored in real workflows rather than UI assumptions.
Synthesis & Validation
Extracted behavioral patterns, bottlenecks, ownership gaps, and exception scenarios; validated flows through stakeholder reviews, role-based simulations, and scenario walkthroughs.
Prototyping with AI
Used Claude, Claude Code, and Figma AI to generate early interaction models and wireframes, iterate workflows quickly, and validate feasibility faster.
Design System & Deliverables
Consolidated UI and system logic in Figma with a scalable component library, workflow-adaptive modules, role-aware patterns, and engineering-ready specs.
⚙️ Constraints & Design Principles
Strata was already deployed in production across multiple dealer networks. Every design change had to be backward-compatible, shippable by a lean engineering team, and non-disruptive to active workflows. I established four principles that filtered every decision throughout the project.
Action-first
Surface the next action, not just data. Every screen guides the user toward a specific step.
Role-aware
Each role sees only what moves their work forward — operators, managers, and admins see different surfaces.
State-driven
UI reflects real-time workflow state. A task in-progress looks different from one blocked or pending.
Incrementally shippable
Every change ships independently and doesn't break the system currently in use.
🤖 AI-native workflow & design-to-code
AI embedded in the design process
I used Claude and Claude Code, orchestrated with n8n and MCP, to generate initial workflow state models from operator interviews, compress research synthesis cycles, and draft edge-case scenarios for exception flows. This cut discovery-to-definition time and surfaced states that traditional user research would have missed.
AI didn't replace judgment — it compressed the distance between raw data and structured decisions.
Design-to-code handoff
The Figma design system was structured for direct engineering consumption: semantic variables for color and spacing, annotated component states, and named interaction patterns. Every component had a documented state model aligned with the backend workflow states.
This meant engineering could implement directly from the design file without translation — reducing back-and-forth and keeping design intent intact through shipping.
💡 Key design decisions
Unified state model
One source of truth for all workflow states. Every UI element maps to a named state, eliminating ambiguity between design, product, and engineering.
Role-based dashboards
Operators see task queues. Managers see exceptions and bottlenecks. Admins see configuration. No role navigates through another's context.
Action surfaces over data views
Screens are structured around what the user does next, not what data is available. Passive monitoring screens were replaced with guided action surfaces.
Progressive disclosure
Complexity is revealed on demand. Default views show essentials; details, history, and advanced options are one tap away — not in the way.
B2B2C Expansion for Strata
📡 B2B2CStrata was originally designed for enterprise back-office operations — a B2B system. As the platform matured, the opportunity emerged to extend the same operational logic outward: from dealer networks (B2B) through to the end customer (B2C), creating a B2B2C loop.
B2B — Enterprise layer
Fleet operators, procurement managers, and admins manage the operational backbone: inventory, SLAs, task routing, and exceptions. Strata's core surface.
B2B — Dealer network
Dealers receive, process, and close service requests. They operate within Strata's workflow but with scoped visibility — only their assignments and queues.
B2C — End customer
Customers receive real-time updates, can raise exceptions, and track service progress through a simplified mobile-first surface built on the same state model.
How I validated the decisions
I validated these choices through workflow mapping, stakeholder reviews, and scenario walkthroughs, focusing on how people actually coordinated work in a live environment.
That process helped confirm which interactions needed to be standardized, which needed role-specific treatment, and where AI could safely support execution without taking control.
✨ Solution
Strata is an action-first operating model that gives humans and AI agents shared context to coordinate work safely and efficiently.
Core components include:
Users land in a workspace that shows only relevant work, guides decisions step by step, and adapts to role and permissions.
📈 Outcome
💡 Learnings & positioning
AI Automation Maturity Framework
I evaluate AI automation across three levels to decide where automation should stop and agentic judgment should begin:
Workflow Automation
Point A → B automation, fixed rules, no reasoning.
Agentic Integration
LLM agents interpret context and make bounded decisions within a step.
Adaptive Agent Loops
Agents evaluate options in real time against historical context, SLA data, and permission scopes, closing the loop with minimal human intervention on low-risk decisions.
This staged approach kept Strata's live operations stable: L2 shipped where the decision surface was narrow enough to trust, while L3 was designed but deliberately held back until audit trails and permission scopes were mature enough to remove human review safely.
Closing
Strata shows that enterprise platforms should execute work, not just store it. It positions my work as designing AI-native systems of action that align human and agent workflows with real-world constraints and delivery.