Aloa LV Collective
Secure the Foundation
Before You Scale
Your team is building the right thing. But the infrastructure underneath it - personal laptops, broken OneDrive, zero access controls - isn't ready for a $1.5B portfolio. This is how you fix it.
01 - The Immediate Risk
Your AI workflows are running on
infrastructure that can't protect them.
You know your IT infrastructure is a mess. It needs to get cleaned up before you scale, and it needs to stay clean as you build on top of it. Here's where things stand:
All AI workflows - Claude Code sessions, Obsidian vaults, automation scripts - run on personal, unmanaged laptops. No backup. No encryption. No central control. If a laptop dies, that lieutenant's entire system is gone.
The company's designated file sharing system doesn't work reliably. Builders avoid it. There's no functioning central repository for documents, data, or code - so everything stays local.
Builders can't install dev tools on company machines, so they use personal devices. That means sensitive financial data from a $1.5B AUM portfolio - close numbers, tenant PII, investor comms - lives on hardware LV doesn't control.
No access controls, no audit trails, no API key management, no data flow mapping. If Harrison Street asks “who accessed investor data last quarter” or “how does your AI handle sensitive financials” - there's no documented answer.
This isn't about slowing anyone down. The team is building the right thing. But every automation they create - monthly close, cash management, invoice processing, lease analysis - touches sensitive financial data. The gap between where infrastructure is today and where it needs to be for a portfolio this size is real, and it's fixable.
This is what Phase 1 surfaces and Phase 2 addresses. The engagement is designed to move fast: assess the full landscape, prioritize the risks, and build the governance layer the team needs to keep building safely.
02 - What We Assess
Phase 1: Discovery
Before fixing anything, we need the full picture. Phase 1 is a 1–2 week diagnostic that maps every workflow, data flow, and access point - and surfaces every compliance gap across the organization.
What we do
- Interview key people across the building team and leadership
- Map every AI workflow, tool, data flow, and access point
- Audit infrastructure: where data lives, how it moves, who touches it
- Data classification across the portfolio
What we assess
We evaluate your current posture across every area that matters for institutional-grade AI operations:
- Data Classification - Categorize every data source AI workflows touch: what's sensitive, internal, or public
- PII Handling - Audit what personally identifiable information workflows ingest, store, cache, or output
- Access Controls (Application Level) - Who can access which workflows and which data within them
- Access Controls (Infrastructure Level) - Who has access to environments, servers, and cloud resources
- Audit Trails - Logging of who ran what, when, what data was accessed, what outputs were produced
- Encryption - Whether data is encrypted at rest and in transit across all systems and devices
- API Key & Secret Management - How keys are stored, shared, rotated, and scoped across the team
- Endpoint Security - Device-level security on personal laptops handling sensitive financial data
- Vendor & AI Provider Agreements - BAAs and DPAs with every AI provider touching sensitive data
- Network Security - Where data travels between systems, open ports, exposed services
- Data Retention & Disposal - How long data is kept, how it's securely deleted when no longer needed
- Backup & Disaster Recovery - Version control, backup infrastructure, tested recovery procedures
- Secure Development Standards - How agents are built, reviewed, deployed, and changes tracked
- Monitoring & Alerting - Ongoing visibility into workflow behavior and anomalous access patterns
- Compliance Framework Mapping - Mapping all controls to recognized frameworks like SOC2
Deliverable: Executive assessment report covering every AI workflow, data flow, access point, and compliance gap - with prioritized recommendations. This is the document that answers an institutional partner's questions about AI security posture.
03 - What We Build
Phase 2: Framework Design
& Implementation
Scope is determined by Phase 1 findings. Based on similar engagements, this phase typically runs 1–3 weeks in the range of 40–60 hours. The goal is for the team to implement as much as possible in-house, with Aloa providing the architecture, standards, and guidance.
Typical scope
- Security policy and data governance framework - The policies and standards that define how AI touches sensitive data
- Access control architecture - Role-based, mapped to your org structure and lieutenant/enterprise model
- Development standards and collaboration playbook - How agents are built, reviewed, and deployed across the team
- Audit logging design and implementation guidance - What gets logged, how it's stored, how it's queried
- Infrastructure architecture and migration plan - Moving off personal devices into a governed, backed-up environment
- Data pipeline design for existing systems - Connecting OneDrive, personal vaults, and current data sources into a coherent architecture
Hours on this phase are flexible. Where the team's technical resources can build, they should. We cover the gaps they can't fill themselves. If the engagement surfaces things that need to be built beyond the team's capacity, that work gets scoped and estimated separately.
Deliverable: Implemented frameworks, architecture documentation, and team enablement. Everything the team needs to operate at enterprise grade and maintain it independently.
04 - Ongoing Advisory
Phase 3: Stay Current.
Scale Safely.
At the rate AI tooling is changing, you need someone keeping the architecture current and reviewing new agents as they come online - especially as you scale from 10 to 50 properties.
Monthly
Advisory
4–8 hrs/mo
Ongoing support as the team scales
- Monthly check-ins on new workflows and agents
- On-demand architecture review as the team builds new things
- Quarterly security and compliance assessments
- Evolving the frameworks as the portfolio scales
- Separate scoping for any build work the team can't handle internally
No lock-in. Monthly cadence with no long-term contracts. The ongoing advisory keeps the team's infrastructure current as AI tooling evolves and the portfolio grows - not to create dependency.
05 - Investment
Less than half what you almost spent on JLL.
Phase 1 + 2 Total
2–5 weeks · 60–90 hours · complete governance foundation
$18K–$27K
at $300/hr · senior engineers
Phase 1 alone gives leadership full visibility into the current state - every agent, every data flow, every risk - so you can make informed decisions before committing further.
Why Aloa
| Platform Vendors | Aloa |
| Approach | Rip out what you've built, adopt their ecosystem | Work with your existing stack - Claude Code, Obsidian, Supabase |
| AI Depth | Traditional RPA. Limited LLM and agent experience. | We build with Claude Code daily. LLM APIs, agent architectures, MCP - this is what we do. |
| Security | Platform-level. No custom AI governance. | Built governance for HIPAA-regulated healthcare AI. PII handling, audit trails, access controls. |
| Cost | Per-seat licensing. $60K+ year one. | Hourly consulting. $18K–$27K total. Everything belongs to you. |
| Builder Fit | Replace your builders' work with vendor workflows. | Level up your lieutenants. They keep building. |
| Element | Details |
| Rate | $300/hr flat for senior engineers who've built and secured AI systems across healthcare (HIPAA), fintech, and enterprise SaaS (SOC2). |
| Billing | Hourly, with monthly estimates agreed in advance. No surprises. |
| Cadence | Weekly during active phases. Monthly once the foundation is in place. |
| Ownership | Everything we produce - frameworks, policies, audit tools, architecture docs - belongs to LV Collective. Walk away anytime with everything. |