AI Operating System Proposal
Sansone Group is a 68-year-old firm with $9.8 billion in development value, 150 properties across 19 states, and partnerships with Goldman Sachs, Invesco, and Fortress. The relationships, reputation, and judgment that built this company are irreplaceable. The repetitive, analytical, and coordination work that slows it down is not.
This proposal outlines a six-month engagement to build a custom AI Operating System for Sansone Group. Not training your people to use chatbots — building a fleet of specialized AI agents that do the work alongside your team, within a single platform that becomes your company's nervous system.
This engagement ends that cycle. When we're done, Sansone Group will have the capacity of a team twice its size — without hiring a single additional person. Every director gains the equivalent of three analysts. Every department has agents handling the work that used to eat nights and weekends. And your internal team will know how to maintain and extend the system long after the engagement concludes.
This is not a consulting report. Not a training program. Not a one-time optimization. It's a fractional CTO engagement that builds a permanent AI capability inside Sansone Group.
Lior Weinstein serves as your technology leader for the duration of the engagement. This means:
You don't just get a system — you get the ability to run and grow it yourself. By month 6:
A single platform where every Sansone employee logs in and knows exactly what to do. AI agents handle the analytical, repetitive, and coordination work. Humans handle relationships, judgment, and negotiation — the things that built this company.
These are the agents we know you need based on our analysis of your org chart, job descriptions, and conversations with your team. But this is just the beginning — our proprietary discovery technology will uncover dozens more based on what your people actually do every day.
The highest-value department. Where AI compresses deal cycles from months to weeks.
Highest density of automatable tasks. Where P&L savings show up directly.
The agents listed above are the ones we already know to build. But the biggest opportunities are hiding in your team's daily work — the emails, the Teams chats, the repetitive tasks nobody thinks to mention in an interview. That's where DejaDo comes in.
During Phase 0, your employees connect their email and Microsoft Teams to our proprietary discovery engine. DejaDo scans their actual work activity — not surveillance, but pattern recognition — and identifies every opportunity where an agent could save real time.
No SOPs needed. DejaDo discovers your real processes from real data, not from documentation that may not exist or may be outdated.
A complete inventory of every agentic opportunity across your organization, prioritized by impact:
This runs continuously throughout the engagement. As new patterns emerge, new agents get proposed, reviewed, and deployed into the operating system.
Detected 47 email threads over the past 30 days where principals or investors requested portfolio stats, aggregate returns, deal status, or capital account balances. Requests involved 4 team members across Development and Accounting, with an average response time of 2.4 business days. Multiple threads show the same data being assembled manually from Yardi, Argus, and Excel each time.
Monitors incoming requests for investor data (email + Teams). Automatically assembles the requested stats from portfolio data — aggregate IRR, per-deal returns, capital account balances, distribution history. Drafts a response with the data formatted per Sansone's investor communication standards. Routes to a director for review before sending.
Runs continuously. Scans email every 15 minutes. Drafts ready for review within 30 minutes of request detection.
Simulated example — this is the level of detail DejaDo produces for every discovered opportunity across your organization.
Expect 40-80+ agent opportunities discovered per department when scanning real employee activity.
First agents producing value within 6-8 weeks. Full operating system live by month 6. Your team trained to extend it independently.
A six-month engagement with milestone-based payments. You pay as value is delivered, not upfront. AI platform licensing and usage costs are separate and ongoing.
Payments are tied to milestones. You don't pay for the next phase until the current one delivers.
The project fee covers the build. These are the ongoing costs to keep the system running and evolving.
| Item | Cost | What It Covers |
|---|---|---|
| Annual Platform License | $60,000/year ($5,000/month) |
AI Operating System platform, all deployed agents, The Brain, Agent Lab, performance monitoring, platform updates, security patches. Keeps the entire system running. |
| AI Token Usage | $3,000 – $8,000/month (usage-based, pass-through) |
The actual AI model costs (Claude, GPT, etc.) consumed by your agents. Passed through at cost — no markup. Scales with usage. Estimated range for your agent fleet size. |
| Managed Support (Optional) | $10,000 – $15,000/month (if desired) |
Ongoing agent optimization, new agent development, priority support, strategic advisory. Not required if your internal team manages independently. |
Conservative estimates based on your current team size, salary ranges, and deal volume. The engagement pays for itself within the first year — everything after that is pure upside.
| Position | Annual Savings |
|---|---|
| Financial Analyst (open position — don't fill) | $75,000 – $90,000 |
| Residential AP positions (1-2 via attrition) | $60,000 – $110,000 |
| Future analyst/coordinator hires avoided (2-3) | $150,000 – $270,000 |
| Contract/consultant reductions | $40,000 – $80,000 |
| Total Headcount Savings | $325,000 – $550,000 / year |
On $900M+ in deployed capital, even a 15-20% improvement in deal velocity represents $135–$180 million in additional deployment capacity over a cycle. Each director gains the equivalent capacity of three additional analysts. Deals that take "a couple weeks for an in-depth proforma" become same-day. Partner negotiations that "can take months" compress to weeks when you can present pre-modeled scenarios instantly.
Once the operating system is live, overlapping tools can be consolidated. Estimated $50,000–$200,000/year in subscription reduction (determined during Phase 0 audit).
You're already working with DivvyUp on AI training. That's good — and different from what this engagement does. Here's how they compare.
| AI Training (Current engagement) |
Big Consultants (Accenture, McKinsey) |
This Engagement | |
|---|---|---|---|
| Approach | Train people to use AI tools | Identify opportunities, deliver recommendations | Build the actual system |
| Output | AI-savvy employees | Reports and roadmaps | Working agents doing real work |
| Scale | Individual productivity | 10-30 processes optimized | Entire organization, all departments |
| Sustainability | Depends on individual adoption | One-time project, degrades over time | Self-extending operating system |
| P&L Impact | Indirect, hard to measure | Moderate if implemented | Direct — headcount, deal velocity, software |
| After Engagement | People remember (or forget) | Binder on a shelf | System runs and grows forever |
| Typical Cost | ~$10K | $500K–$2M+ | $750K (with IP transfer) |
Sansone Group would be featured as a case study in The Agent Advantage by Tony Robbins and Lior Weinstein.
Technology entrepreneur, fractional CTO, and AI transformation specialist. Five patents in machine learning. Built his first neural network at 14, first software company at 17. Has led technology for companies from startup to nine-figure revenue. Currently fractional CTO for Tony Robbins' Robbins Research International ($180M revenue, 200+ employees) and Redirect Health (scaled from $50M to $100M in 2.5 years).
Co-author of The Agent Advantage with Tony Robbins (publishing September 2026). Creator of the AAA Intelligence Architecture methodology. Founder of multiple AI companies including a lease abstraction platform serving Goldman Sachs and Avison Young.
Relevant experience: Six years in commercial real estate technology. Deep familiarity with Argus, Yardi, RealPage, AppFolio, Timberline, CoStar, and the full CRE software stack. Has built operating systems for companies ranging from 3-person firms to 200+ employee organizations.
Marissa Brassfield — AI adoption and change management specialist. Former right-hand to Peter Diamandis at Abundance360 for eight years. Leads all employee training and cultural transformation. Specializes in turning AI skeptics into AI champions.
Joe Brottmen — OS UX Architect. Designs the operating system interface, employee experience, and department dashboards. Ensures the platform is intuitive for every team member from day one.
From signed agreement to first agents live — six to eight weeks. Here's what happens next.
Confidential — Prepared exclusively for Sansone Group
Lior Weinstein • lior@cto.com • CTO.com