What we do
Four practices, each built on work we've done in our own lab and for some of the world's most demanding technology companies.
Build AI‑native teams
Structure engineering and technical teams so people and AI do their best work together.
- Team and org design for AI-augmented engineering
- Workflows that pair people with AI tools across product, design and engineering
- Hiring plans, roles and skills for an AI-native team
- Coaching for technical leaders through the transition
Who it's for: Companies scaling engineering, and leaders asking how AI changes the shape of their team.
Build AI products
Apps, services and devices that deliver AI experiences, from the cloud to the hardware in people's hands.
- Product strategy, experience design and roadmap
- AI apps and services: mobile, web and cloud
- AI-enabled devices: wearables, in-car systems, sensors and edge AI
- Model selection and architecture across on-device and cloud
- Hardware engineering, prototyping and manufacturing support
Who it's for: Teams turning an AI idea into a product people use every day, on a screen or in a device.
Monetize AI
Business models, pricing and unit economics that hold up as AI costs change.
- Business model and pricing design for AI products
- Cost-to-serve modeling for inference and data
- Go-to-market strategy and launch planning
- Metrics that show real value to customers
Who it's for: Startups finding their model, and companies adding AI to products they already sell.
Secure AI
Privacy, security and trust designed in, so people stay protected.
- Privacy-by-design architecture, on device and in the cloud
- Security reviews of AI systems, data flows and third-party models
- Safeguards against misuse, manipulation and data leakage
- Readiness for regulation and customer trust reviews
Who it's for: Anyone putting AI in front of customers, patients or citizens.