01
AI workflow automation
Turn repetitive expert workflows into assisted, auditable, production-grade systems.
Output: Workflow map + automation plan01AI execution
AI systems / product strategy / technical architecture / operational execution
From AI pilots to operational systems.
Product strategy, technical architecture, and execution support for teams bringing AI into complex software, enterprise, and industrial workflows.
We help teams define the workflow, design the AI system, architect the integration path, and move from promising prototype to deployed product.
Deployment modelAI Execution System
ERPMESCRMDocumentsSupport ticketsSensor dataField notesProduction dataCustomer data
01Data Sources
02Human Workflow
03Model Layer
04Decision Logic
05System Integration
06Operating Dashboard
Review loopEvaluationPermissionsDeployment
$35M+Capital raised by products and companies supported
6Product lines launched from zero to market
100+AI, product, growth, and engineering teams advised
100M+Users reached by platforms and media systems
02AI systems
Where AI creates operating leverage.
The valuable work isn't the model demo. It's the workflow, the decision, the integration, and the adoption path around it.
PrototypeWorkflow ArchitectureData + Model LayerEnterprise IntegrationOperational Deployment
02
AI decision support
Help teams triage, prioritize, forecast, inspect, and act faster with human-in-the-loop intelligence.
Output: Decision model + review loop03
Industrial intelligence
Apply AI to manufacturing, quality control, maintenance, field operations, supply chain, and workforce workflows.
Output: Operational use-case map04
AI product platforms
Build AI-native SaaS, copilots, agent-assisted internal tools, and data products with real deployment paths.
Output: Product architecture + MVP plan05
Enterprise integration
Connect AI systems to existing data, permissions, business rules, tools, and operating constraints.
Output: Integration path + risk map06
Operational support & sustenance
Keep deployed AI and product systems running — monitoring, model drift, incident response, on-call, and continuous improvement after launch.
Output: Operating playbook + support model03Principle
We work inside the business, not around it.
We don't show up with a playbook. We start with the workflow, the team, the customer, and the real constraints — then make the product, engineering, and operating decisions that fit how the work actually gets done.
What we work within
Existing systemsTeam capacityCustomer realityCompliance boundariesBudget & runwayAdoption & changeOperational reliabilityHiring & handover
04Who we help
Who we work with.
Teams with a real product bet — and an unclear technical architecture, execution plan, or AI deployment path.
01
Founders without a full leadership team
Shape the product thesis, technical architecture, roadmap, hiring plan, and delivery sequence before a permanent CTO is in place.
02
Companies adding AI to existing systems
Move past pilots and demos. Land AI inside real workflows, data, permissions, and the systems your business already runs on.
03
Industrial and operations teams
Modernise quality, maintenance, planning, field service, inspection, and plant-level workflows with the right mix of software and AI.
04
Teams hiring product or technical leadership
Get senior product and engineering leverage before a full-time executive is in place — and help define the role you eventually hire for.
05
Venture studios and funds
Pressure-test product ideas, evaluate technical feasibility, define wedges, and turn concepts into fundable companies.
05Outputs
01
AI opportunity map
Where AI can create measurable leverage across workflows, users, data, and operations.
02
Workflow architecture
Human-in-the-loop process design, model touchpoints, decision rules, and escalation paths.
03
AI product thesis
Clear user, wedge, model role, business value, and deployment strategy.
04
System architecture
Data sources, integrations, model layer, permissions, observability, and production constraints.
05
Prototype-to-production plan
MVP scope, technical risks, rollout path, team plan, and delivery checkpoints.
06
Operating dashboard
Metrics, review loops, adoption signals, quality controls, and iteration cadence.
How we work
AI that holds up in production.
Demos are the easy part. Production is where the work begins — real users, real data, and the systems already running the business.
You're left with a system your team can run, measure, and trust.
Real workflowReal dataReal usersHuman reviewIntegration pathMeasurement loopOperating owner
06Experience
Where we've been brought in.
Across early-stage startups, public companies, industrial operations, developer platforms, and consumer products.
Gaming, AI
Two way video streaming for artists.
Enterprise IoT
Product platforms
Mobile platforms
Video, media, and AI data07Real work
A few representative engagements.
Erth.AI
Led product, engineering, and fundraising for AI gaming and web-native entertainment, scaling teams, shipping multiplayer experiences, and supporting more than $20M in fundraising.
View Inc.
Led hardware-device engineering, smart-building platform work, and engineering scale for a smart-window manufacturer, supporting growth to 200+ global sites and 99.999% uptime.
Microsoft
Led client-side engineering and founding product work across Skype mobile, Skype for Business, early Teams, VS Code, notification infrastructure, Qik, and HoloLens prototypes.
Qualcomm
Architected mobile platform, SDK, communications, and app ecosystem work across Snapdragon-era device software, spanning Android video calling, VoIP, IMS, AMSS, and Plaza Retail.
Doppler Labs
Led software and hardware for smart earbuds, companion apps, adaptive listening, accessibility workflows, and manufacturing for a Time-recognized hearables company. HereOnes were awarded the innovation of the year 2012 in Audio category.
08Operating model
From ambiguity to deployed system.
- 01Diagnose
Clarify the workflow, user, business value, data reality, technical constraints, and operating gaps.
- 02Design
Define the AI role, system architecture, integration path, roadmap, and human-in-the-loop model.
- 03Build
Prototype, validate, staff, and sequence delivery with measurable checkpoints and technical discipline.
- 04Deploy
Install review loops, adoption metrics, quality controls, operating cadence, and support systems for repeated delivery.
Get in touch
Have an AI product or operational workflow that needs to move beyond prototype?
Bring in senior product and engineering judgment before strategy, architecture, data, or deployment gaps slow the business down.
Best fit for founders, product leaders, AI transformation teams, and operators with urgent execution gaps.