Weave
Weave AI
Overview
Weave is the leading software company for dental and veterinary clinics, offering tools for communication, payment processing, scheduling, document management, and marketing.
Defined and designed an extensible AI onboarding assistant and long-term platform AI vision aligned across product and engineering.
Process
The Problem
New customers were struggling to complete required setup tasks during onboarding. This slowed time to value and increased reliance on onboarding specialists and account executives.
At the same time, AI experiments were emerging in isolated product areas. These efforts were scoped to individual features and lacked a unifying platform vision.
I saw two related problems:
Onboarding friction
Fragmented AI strategy
The Insight
Early in exploration, I assumed AI work in Messaging would evolve into a platform-level assistant. Feedback revealed that this was not the long-term plan.
That changed the direction of the project.
Instead of designing an onboarding chatbot, I reframed the opportunity:
If AI is going to exist across the product it needs to be designed as infrastructure, not a feature. We have to establish a North Star to guide early designs.
Onboarding became the entry point, not the end state.
Defining the Platform Vision
I created a long-term AI vision that included:
High-value use cases across onboarding, messaging, payments, and settings
A unified interaction model
A clean evolution path from onboarding assistant to global AI worker
Design principles for embedding AI naturally within workflows
This vision clarified:
What we were building now
What we were enabling later
What infrastructure would be required
Driving Alignment
To move beyond concept, I led conversations across:
Product leadership
Engineering leadership
ML team
Integrations team
These discussions focused on:
Data access requirements
Model ownership
Integration architecture
Escalation patterns to human support
State persistence
The goal was alignment before execution.
Designing the Onboarding Assistant (MVP)
With alignment in place, I designed the AI Onboarding Assistant as Phase 1 of the broader system.
Key decisions:
Contextual entry points within onboarding tasks
A persistent but non-intrusive assistant
Clear escalation to human support
Reusable interaction patterns
State-based visual system
I named and branded the assistant, designing an icon that could communicate:
Idle
Listening
Processing
Responding
Error states
I also created motion studies to define how the assistant would behave.
The visual system was intentionally extensible so it could scale into a global assistant without redesign.
Outcome
Product Outcome
Engineering built the foundational AI onboarding assistant based on the defined system architecture.
The assistant was introduced to a limited user cohort, establishing:
AI infrastructure foundations
Reusable interaction patterns
Brand presence for future AI expansion
Alignment around long-term AI strategy
Modeled Impact
While the assistant was early-stage at the time of my departure, projected impact was modeled based on onboarding baselines and industry benchmarks.
Assumptions:
40–60% average SaaS onboarding completion rate
10–25% improvement from guided assistance
15–30% reduction in human-assisted onboarding requests
Even conservative estimates suggested:
Faster time to value
Reduced onboarding specialist load
Lower support costs
Increased activation rates
[Embed Visual #4 here — inside Work Outcome rich text]
Organizational Outcome
Beyond the onboarding feature itself, this project:
Unified fragmented AI efforts
Established a platform-level AI direction
Created cross-team alignment
Reduced risk of future rework
Positioned AI as product infrastructure rather than experimentation
The onboarding assistant became the foundation for a broader AI strategy.
*Copyrights for these designs belong to
Weave Communications, Inc
