AI Systems & Enterprise Automation
I build robust AI systems that scale in real operations.
Forward-deployed AI consultant working at the intersection of operations, product, and enterprise AI execution. I build automation systems for real workflows — reducing manual effort, improving operational efficiency, and helping lean teams scale without adding unnecessary complexity. Currently building AI Automation Infrastructure at Ergomotion, Inc. and Humanity-Centered Design AI tools at the Don Norman Design Award.
Systems I've Built
Case Studies
Automated Sales Order Processing (SAP + AI Pipeline)
Designed and deployed a multi-model AI system that extracts, validates, and pushes sales orders into SAP — replacing manual workflows across formats and regions.
Manual order entry nearly eliminated for the operations team
Logistics Automation: AI vs. Deterministic Tradeoffs
Built the same pallet calculation capability two ways — discovered where AI breaks down on precision tasks and architected a hybrid system that uses each where it excels.
Deterministic precision + AI orchestration in production
Design Intelligence System
Built a BM25-powered knowledge base (67 styles, 96 palettes, 99 UX guidelines) that replaces expensive AI calls for design decisions — used across client and personal projects.
Deterministic search over curated knowledge — no API costs
Cost-Optimized Content Pipeline
Architected a 4-layer content system where human checkpoints gate expensive API calls — UX as a cost control mechanism.
90% of work validated before any expensive model runs
Decision Frameworks
How I Think About Systems
AI ≠ Always the Answer
Use deterministic systems where precision matters. Use AI where flexibility is required. The Pallet Calculator taught me this through failure.
Build for Real Workflows
Systems must match how people actually work — not how processes are documented. I start every project by observing operations, not reading specs.
Human-in-the-Loop is a Feature
Especially in enterprise systems where compliance and trust matter. The review step in my SAP pipeline is what made adoption possible.
Cost is a Design Constraint
Every API call, model choice, and workflow step is optimized for cost vs. performance. My content pipeline costs $0.33 because architecture controls spend.
Right Model for Each Step
OCR doesn't need GPT-4. Address matching doesn't need Claude. I break pipelines into discrete steps and match each to its minimum viable model.
Ship Fast, Then Stabilize
Deploy early versions in production environments to learn from real usage. My SAP integration shipped in weeks, not months, because I prioritized learning.
About
Nayana Arya
I build and deploy AI systems inside real businesses — not prototypes, not demos. Systems that handle production workflows, integrate with enterprise platforms like SAP, and are used by operations teams every day.
Master in Global Management from Thunderbird/ASU (Distinction). Currently: Enterprise AI Automation at Ergomotion + Research Systems at the Don Norman Design Award. Previously: scaled communities to 10,000+ members, shipped AI tools used by customs brokers and warehouse teams.
Full background