P1AI CONSULTANT·SYSTEMS ARCHITECT
AI Consultant & Systems Architect
Business Problems → Technology → Systems → Validation
I work between the business problem and the technology. I don’t start with “how can we add AI?” I start with: what is actually slowing the business down?
Prototyping is part of the process: I build enough to test whether the design holds.
Digital Growth & Performance Marketing
I work with existing digital products to improve distribution, user acquisition, content, performance, creator partnerships and monetization.
SaaSAppsGamesAI productsAI agentsDigital services
I start from the product and who it is for, then work out where distribution, acquisition and monetization are actually breaking.
AI strategyProblem discoveryAI opportunity assessmentProduct / system direction
AI architectureAgent systemsMulti-agent systemsWorkflow designAutomationAPIsMCPModel/provider architectureIntegrations
AI softwareInternal toolsAgentsAutomationPrototypingLocal/private AI
PermissionsAI governanceSecurity considerationsCost controlsProvider flexibility
Find where technology can remove repetitive work, reduce operational friction and improve how a business operates.
Product positioningAcquisition strategyDistribution strategyGrowth experiments
Paid acquisitionPerformance campaignsTestingOptimization
Organic contentCreator partnershipsBrand partnershipsProduct distribution
Revenue strategyProduct monetizationConversion improvementGrowth experiments
Help useful digital products reach the right users, improve distribution and create sustainable revenue.
| Step | Focus | What happens |
|---|---|---|
| 01 · Understand | Discovery | Understand the business, product, users and existing process. |
| 02 · Diagnose | Analysis | Find the actual bottleneck instead of automatically adding technology. |
| 03 · Architect | Design | Design the system, workflows, tools, integrations, models and responsibilities. |
| 04 · Prototype | Build | Build enough to test whether the idea actually works. |
| 05 · Measure | Review | Look at performance, cost, friction and user/business feedback. |
| 06 · Improve | Iteration | Iterate using what was learned. |
| Step | Focus | What happens |
|---|---|---|
| 01 · Objective | Goal | Decide what the product needs to achieve. |
| 02 · Strategy | Plan | Choose the audience, channel and approach. |
| 03 · Test | Experiment | Run a small, measurable experiment. |
| 04 · Result | Data | Record what actually happened. |
| 05 · Lesson | Learning | Keep what worked and change what didn’t. |
Problem → Thinking → Architecture → Validation → Current state
AI tools, agents, models and workflows are fragmenting. Businesses need a way to discover, use and manage them without losing control.
AI ecosystem & governance infrastructure
Can a personal AI companion keep memory, voice and personality while staying private and local?
Local-first AI companion
AI work is increasingly spread across different applications, models and agent environments.
AI workspace & coordination layer
Experiments, tests and strategy work, labelled as what they are. No client case studies or metrics until there is real evidence to show.
Repetitive workDisconnected systemsOperational bottlenecksInefficient processesUnsure where AI fits
RELEVANT WORKAI strategyarchitectureautomationinternal softwareintegrations
DistributionAcquisitionContentCreator partnershipsMonetizationGrowth strategy
RELEVANT WORKGrowth strategyperformance marketingdistributionmonetization
Have a problem but don’t know what should be built? That’s fine. Start with the problem.
How I approach problems, not just what technologies I know.
AI systemsAI architectureAI agentsAI governanceAutomationLocal AI
Growth strategyPerformance marketingDistributionCreator partnershipsMonetizationMarketing thinking
Ideas → Experiments → Results
| Skill | Level | What it is |
|---|---|---|
| AI Agents | ★★★☆☆ | Software that plans and acts with tools to reach a goal, with limits on what it can do. |
| AI Workflows | ★★★★★ | Multi-step flows that chain models, data and tools so repeat work runs on its own. |
| MCP | ★★★★☆ | Model Context Protocol: a standard way to connect AI models to tools and data. |
| Automation | ★★★★★ | Replacing manual steps with triggers, APIs and scripts. |
| AI Integrations | ★★★★★ | Wiring AI into existing products, APIs and business tools. |
| AI Governance | ★★★☆☆ | Permissions, policies and oversight for what AI systems may access and do. |
| Testing | ★★★★★ | Trying ideas, ads, funnels and builds in a controlled way to see what actually works. |
| Memory & Orchestration | ★★★★☆ | Giving AI long-term context and coordinating several models or agents. |
| Local-first | ★★★★☆ | Systems that keep data and processing on the user's device by default. |
| Apps | ★★★★☆ | Product development and full-stack work: interfaces, APIs and the logic behind them. |
| Games | ★★★★☆ | Interactive builds where the AI or logic is part of the play. |
| Social Media Marketing | ★★★★☆ | Building an audience and demand across social platforms with content and community. |
| Ads | ★★★★☆ | Paid campaigns on search and social, measured by leads, sales and cost per result. |
| SEO | ★★★★☆ | Making a site easy to find and rank in search engines. |
| AI GEO | ★★★★☆ | Generative Engine Optimization: getting a brand cited and recommended in AI-generated answers. |
| Performance Marketing | ★★★★☆ | Paid and measurable marketing, judged by results like leads and revenue. |
| Growth | ★★★★☆ | B2B marketing, positioning, lead generation, monetization and creator infrastructure. |
VERIFY
I prefer showing the work over making claims.
If you have a real problem and want to see if AI can solve it, let’s talk.
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