AI platform

Safe Search AI

AI platform for consumer, family, specialist, and enterprise use

My role: Founder and owner — direction across the business model, financial structure, privacy and governance requirements, operations, market segmentation, workflow design, and AI-assisted product development
Current status: Pre-launch. No adoption figures are published.
Shown as case studies of my work; not offered as products or services.

The origin of the platform

Safe Search AI began as a separate environment for developing a more advanced, configurable AI capability for Deal Pros. Building it independently gave me room to work through grounding, permissions, oversight, and the different ways an organization might need to control an AI experience before bringing those ideas into the brokerage platform.

During that work, I began to see broader applications I hadn’t recognized at the start. The same underlying capability could serve consumers, families, specialists, and organizations if its governance, roles, and knowledge boundaries changed with the user. What began as one part of Deal Pros developed into a standalone platform concept.

The decisions and workstreams I directed

I set the direction across these business, operating, market, and product workstreams. AI-assisted development is one part of the work — not the whole of it.

Business and ownership structure

The entity, and the segmentation decision the whole business rests on: consumer, family, specialist, and organizational use are different products built on one capability.

Financial model and unit economics

Pricing per segment against usage cost, which in an AI product is the whole game — the model that works for an organization doesn’t work for a family.

Legal and compliance

Privacy obligations, data-retention and persistence boundaries, oversight and approval requirements, and what an organization can be accountable for when AI is in the loop.

Operations, vendors and support

Model and vendor selection, cost control, the support model, and the administrative surface an organization needs to actually run it.

Market and go-to-market

Which segment to lead with, how the offer differs across them, and why an organization would adopt this rather than allow general-purpose tools by default.

Product, workflow and AI-assisted development

Governance and approval design, role and permission model, curated source libraries, product direction, and the AI-assisted development behind it.

Why the system exists

What was broken

General-purpose AI tools assume a single competent adult with no oversight requirements. Families, specialists, and organizations need boundaries, review, and accountability that generic assistants don’t provide.

What became possible

Segment the audience rather than the model. The same underlying capability becomes a different product when governance, roles, and knowledge boundaries change with the user.

How I designed the response

Design for the governance requirement first — who may see what, what requires human approval, what persists, what doesn’t — then design the AI experience for each segment inside those boundaries.

What was designed

Specialist AI experiences per user segment
Family and organizational structures
Role-based access and permissions
Organizational knowledge and curated source libraries
Temporary and persistent workspaces
Human approval where oversight is required
Privacy boundaries and AI governance

Directing an AI business concept in which governance, privacy, legal requirements, economics, and market positioning shape the product — not only the model or interface.

AI product strategy
Segment pricing and usage economics
Privacy and data obligations
AI governance
Vendor and cost management
Market segmentation
Role-based access
Security thinking
Controlled deployment

How the System Works

Governed AI rather than an open chatbot: answers come from approved material, people approve where oversight matters, and each family or organization is kept separate.

Layer 1
User experience
What people see and use.
- Temporary and persistent workspaces (People)
- AI answers and actions (Output)
Layer 2
Knowledge
AI is grounded in material the organization controls.
- Curated, organization-controlled source libraries (Data)
- Answers grounded in approved material (AI)
Layer 3
Governance
Rules and people, built into the experience.
- Governance and approval controls (Security)
- Human approval where oversight is required (Human decision)
- Privacy boundaries for AI (Security)
Layer 4
Platform
Separation and support across every environment.
- Family, organization and workspace isolation (Security)
- Reusable AI support with escalation (AI)

People
Output
Data
AI
Security
Human decision

Features that are part of the product, not separate products

Governance and approval controls

Human review built into the experience where oversight matters, rather than added afterwards.

Source libraries

Curated, organization-controlled knowledge so answers are grounded in approved material.

Multi-tenant separation

Family, organization, and workspace isolation enforced structurally.

AI Support

A reusable AI support layer built into the platform: platform-specific knowledge grounding, AI chat support, escalation to a human, support-ticket handoff, and administrative controls.

AI Support and multi-tenant separation are architectural patterns reused across the platforms rather than rebuilt each time.

What I took from it

The value in applied AI usually comes from the constraints — access, grounding, approval — not from the model itself.

Ownership and operating experience is what makes this a governance product rather than a chat product. The questions that shaped it — who is accountable, what is defensible, what an organization can live with — are ones I’ve had to answer as an owner and as a COO.

Other case studies

Operating platform

Deal Pros

Business brokerage operating platform

Marketplace

Agent Lead Exchange

Real-estate referral and lead marketplace