REMI
REMI
The Intelligence Layer
for Real Estate Capital
The Intelligence Layer
for Real Estate Capital
My Journey Here
My Journey Here
Fairview Capital Partners
Supported the onboarding of the firm's largest commitment (PA-SERS) at this $3.3B PE fund-of-funds — first exposure to institutional capital workflows.
Fairview Capital Partners
Supported the onboarding of the firm's largest commitment (PA-SERS) at this $3.3B PE fund-of-funds — first exposure to institutional capital workflows.
IBM Watson AI + Global Financing
Led 0-1 integration of Watson AI into IBM’s largest deal-making platform.
IBM Watson AI + Global Financing
Led 0-1 integration of Watson AI into IBM’s largest deal-making platform.
Point72 Asset Management + New York Mets
Built multiple data platforms from 0-1 ranging from crypto to baseball. Featured in WSJ, NY Times, & more.
Point72 Asset Management + New York Mets
Built multiple data platforms from 0-1 ranging from crypto to baseball. Featured in WSJ, NY Times, & more.
SMBC / Jenius Bank
Helped SMBC build & scale data platform for their first Digital Banking Unit.
SMBC / Jenius Bank
Helped SMBC build & scale data platform for their first Digital Banking Unit.
REMI
Building the solution I wished existed — now with AI capable enough to make it real.
REMI
Building the solution I wished existed — now with AI capable enough to make it real.
Nadi Haque — Founder
Nadi Haque — Founder
The Problem
The Problem
AI Lives in Spreadsheets.
But Firms Live in Context.
AI Lives in Spreadsheets.
But Firms Live in Context.
Today's AI tools are powerful, but nerfed. They operate at the cell level, not the firm level. Investment Firms have ripple effects — deals tied to asset(s) in a portfolio in a fund.
Today's AI tools are powerful, but nerfed. They operate at the cell level, not the firm level. Investment Firms have ripple effects — deals tied to asset(s) in a portfolio in a fund.
Just a Faster Calculator
AI's working 40/168 hours
Dependent on human input, firms are not capitalizing on the sheer volume of work AI can do.
Just a Faster Calculator
AI's working 40/168 hours
Dependent on human input, firms are not capitalizing on the sheer volume of work AI can do.
Zero Continuity
2.3 year avg analyst tenure
Every session starts from scratch. Firm history, IC preferences — gone.
Zero Continuity
2.3 year avg analyst tenure
Every session starts from scratch. Firm history, IC preferences — gone.
Still Manual
4-8 hrs per lease
63% of RE teams say unstructured files are their #1 bottleneck.
Still Manual
4-8 hrs per lease
63% of RE teams say unstructured files are their #1 bottleneck.
🧠 The Analyst
is still the integration layer
🧠 The Analyst
is still the integration layer
Funds hedge Market Risk but remain fully exposed to Key-Person Risk.
Bad hiring decisions are often made & key employees leave regularly. These drop your Fund's IQ significantly.
Funds hedge Market Risk but remain fully exposed to Key-Person Risk.
Bad hiring decisions are often made & key employees leave regularly. These drop your Fund's IQ significantly.
Current Solutions
Current Solutions
Two Approaches. Same Dead End.
Two Approaches. Same Dead End.
Headcount Approach
Scaling headcount is expensive & takes >1Y for positive ROI.
In the AI-age, this approach is outdated.
Mid-market RE is 3× less efficient than PE/Hedge Funds.
Headcount Approach
Scaling headcount is expensive & takes >1Y for positive ROI.
In the AI-age, this approach is outdated.
Mid-market RE is 3× less efficient than PE/Hedge Funds.
Generalist AI Approach
GPT & Claude are great, but lack context. Once everyone has Claude running, the novelty will wear off & real alpha will be chased.
Works 40 hours/week
Copilot & Claude are great - but they only work when the analyst does
Understands Excel, not your assets
Faster DCFs don't equate to better deals & higher IRR
Starts from 0 every session
No compounding knowledge, no firm memory
Generalist AI Approach
GPT & Claude are great, but lack context. Once everyone has Claude running, the novelty will wear off & real alpha will be chased.
Works 40 hours/week
Copilot & Claude are great - but they only work when the analyst does
Understands Excel, not your assets
Faster DCFs don't equate to better deals & higher IRR
Starts from 0 every session
No compounding knowledge, no firm memory
The memory of decisions made is stuck in human heads. An individual gets wiser, but the firm remains the same.
The memory of decisions made is stuck in human heads. An individual gets wiser, but the firm remains the same.
What if instead…
What if instead…
Every morning started at the finish line?
Every morning started at the finish line?
Let AI do what you were already going to tell it to do, preemptively. In humans, we call that taking initiative. All you have to do is review.
Let AI do what you were already going to tell it to do, preemptively. In humans, we call that taking initiative. All you have to do is review.
AI Agents That Know
Your Entire Firm.
AI Agents That Know
Your Entire Firm.
REMI maps historical and incoming data to build a living Context Graph for your entire firm, then deploys autonomous agents that work 24/7 across new and existing deals.
REMI maps historical and incoming data to build a living Context Graph for your entire firm, then deploys autonomous agents that work 24/7 across new and existing deals.
Email Arrives
Deal flow from broker
PDFs Extracted
OM, financials, rent rolls
Context Graph
Link to asset & fund history
Compare Past Deals
Match against your firm's deal history
Apply Fund Rules
IC criteria, thesis filters
Draft Generated
IC memo + model ready
Human Review
Your decision, your control
Market Opportunity (Initial Wedge)
Market Opportunity (Initial Wedge)
$25B+ Total Addressable Market
$25B+ Total Addressable Market
Most databases only track ~10K institutional firms. The real opportunity is the invisible majority — 90K+ boutiques, syndicators, and family offices desperate for institutional-grade tools.
Most databases only track ~10K institutional firms. The real opportunity is the invisible majority — 90K+ boutiques, syndicators, and family offices desperate for institutional-grade tools.
Visible (Preqin Tracked)
~10K Firms
~15K Firms
~35K Firms
30K+ Reg D/Yr
The Invisible Market
Visible (Preqin Tracked)
~10K Firms
~15K Firms
~35K Firms
30K+ Reg D/Yr
The Invisible Market
Segment
Firms
TAM
Enterprise & Global Giants
~500
$125M+
Core: Mid-Market Institutions
~8,000
$800M
Active Capital Raisers (Reg D)
~35,000+
$1.05B+
Adjacent: Private Credit
~4,000
$480M
Adjacent: Infrastructure & PE
~6,000
$900M+
Real Estate TAM
~53K+
$3.4B+
All Private Capital
$25B+
Same Product, Two Motivations
Institutions buy for amplification (multiply Analyst output without increasing hiring).
The Invisible Market buys for capability (leverage data in a way that would normally take an entire data team).
Same Product, Two Motivations
Institutions buy for amplification (multiply Analyst output without increasing hiring).
The Invisible Market buys for capability (leverage data in a way that would normally take an entire data team).
Sources: Preqin 2025, SEC Reg D Filings (Annual), AppFolio 10-K, IRS Partnership Data
Sources: Preqin 2025, SEC Reg D Filings (Annual), AppFolio 10-K, IRS Partnership Data
Segment
Firms
TAM
Enterprise & Global Giants
~500
$125M+
Core: Mid-Market Institutions
~8,000
$800M
Active Capital Raisers (Reg D)
~35,000+
$1.05B+
Adjacent: Private Credit
~4,000
$480M
Adjacent: Infrastructure & PE
~6,000
$900M+
Real Estate TAM
~53K+
$3.4B+
All Private Capital
$25B+
Same Product, Two Motivations
Institutions buy for amplification (multiply Analyst output without increasing hiring).
The Invisible Market buys for capability (leverage data in a way that would normally take an entire data team).
Same Product, Two Motivations
Institutions buy for amplification (multiply Analyst output without increasing hiring).
The Invisible Market buys for capability (leverage data in a way that would normally take an entire data team).
Sources: Preqin 2025, SEC Reg D Filings (Annual), AppFolio 10-K, IRS Partnership Data
Sources: Preqin 2025, SEC Reg D Filings (Annual), AppFolio 10-K, IRS Partnership Data
Why Now
Why Now
The AI Wave is Here
The AI Wave is Here
Coding was the first knowledge-based skill to fall to AI. With AI Agents unlocked, operational knowledge-work & recurring workflows are next.
Coding was the first knowledge-based skill to fall to AI. With AI Agents unlocked, operational knowledge-work & recurring workflows are next.
Coding Assistant Adoption
Coding Assistant Adoption
AI Workers Projection
Benjamin Miller, CEO FundriseBy 2030: 35.4% of all US knowledge-work hours automated — equivalent to 20.4M FTE workers.
AI Workers Projection
Benjamin Miller, CEO FundriseBy 2030: 35.4% of all US knowledge-work hours automated — equivalent to 20.4M FTE workers.
Operations is next.
Sources: GitHub Octoverse Report (2024–2025), Sequoia Capital "State of AI" Reports, Anthropic Public Metrics
Operations is next.
Sources: GitHub Octoverse Report (2024–2025), Sequoia Capital "State of AI" Reports, Anthropic Public Metrics
Why Me
Why Me
Built for This Exact Problem
Built for This Exact Problem
The way to solve enterprise AI is at its root — which is data. I've spent my entire career navigating enterprise data platforms at financial institutions & managing stakeholders of downstream data usage. I know how they work, their problems, and the alpha that exists by building REMI.
The way to solve enterprise AI is at its root — which is data. I've spent my entire career navigating enterprise data platforms at financial institutions & managing stakeholders of downstream data usage. I know how they work, their problems, and the alpha that exists by building REMI.
Data-First AI Platforms
Led 0→1 Watson AI integration at IBM's largest deal-making platform. Built multiple data platforms from scratch at Point72. This is what REMI requires: complex, multi-source data expertise, with context graphs & AI on top.
Data-First AI Platforms
Led 0→1 Watson AI integration at IBM's largest deal-making platform. Built multiple data platforms from scratch at Point72. This is what REMI requires: complex, multi-source data expertise, with context graphs & AI on top.
Enterprise Financial Systems
Helped SMBC build & scale their first Digital Banking Unit serving key business members in Credit Risk, Fraud, & Operations. Know the compliance, security, and procurement cycles.
Enterprise Financial Systems
Helped SMBC build & scale their first Digital Banking Unit serving key business members in Credit Risk, Fraud, & Operations. Know the compliance, security, and procurement cycles.
Data Security & Org Ops
I’ve handled sensitive financial data across institutions: encrypted, proprietary, and operationally critical. I know how teams actually operate, and how to layer AI into existing workflows without forcing organizations to flip overnight.
Data Security & Org Ops
I’ve handled sensitive financial data across institutions: encrypted, proprietary, and operationally critical. I know how teams actually operate, and how to layer AI into existing workflows without forcing organizations to flip overnight.
Builder DNA
Every role has been a 0→1 platform build inside complex organizations. Left corporate because I've seen enough progress with AI-accelerated development to bet on myself — building REMI full-time is the next step, not another middle management role.
Builder DNA
Every role has been a 0→1 platform build inside complex organizations. Left corporate because I've seen enough progress with AI-accelerated development to bet on myself — building REMI full-time is the next step, not another middle management role.
Why Not
Why Not
Risks I'm Thinking About
Risks I'm Thinking About
You're going to ask these questions. Let me address them head-on.
You're going to ask these questions. Let me address them head-on.
“AI is commoditizing fast. What's your moat?”
Context Graphs & Data Governance are our moat. Generic AI can't understand deal relationships without fund-specific knowledge graphs. We're building the data layer, not just the AI layer.
“AI is commoditizing fast. What's your moat?”
Context Graphs & Data Governance are our moat. Generic AI can't understand deal relationships without fund-specific knowledge graphs. We're building the data layer, not just the AI layer.
“What about data security and integration complexity?”
Deployment model is client-driven: VPC/on-prem when required, or managed cloud when preferred. Either way, enterprise-grade access controls, encryption, and audit trails are standard.
“What about data security and integration complexity?”
Deployment model is client-driven: VPC/on-prem when required, or managed cloud when preferred. Either way, enterprise-grade access controls, encryption, and audit trails are standard.
“What about the incumbents (Yardi, MRI)?”
They may create their own AI, but that's no competition to us. We intertwine all the systems a fund uses — and that's fundamentally out of scope for incumbents. At worst, they'll create an MCP that helps REMI.
“What about the incumbents (Yardi, MRI)?”
They may create their own AI, but that's no competition to us. We intertwine all the systems a fund uses — and that's fundamentally out of scope for incumbents. At worst, they'll create an MCP that helps REMI.
“Single founder risk?”
There is too much leverage with tools like Clawdbot, Claude, and Cursor to rush this decision. I’m technical, and my door-to-door sales background gives early GTM coverage. I’m intentionally selective about a long-term co-founder fit.
“Single founder risk?”
There is too much leverage with tools like Clawdbot, Claude, and Cursor to rush this decision. I’m technical, and my door-to-door sales background gives early GTM coverage. I’m intentionally selective about a long-term co-founder fit.
“How will you access data from legacy systems?”
Many legacy platforms were built as systems of record, not systems of understanding. REMI sits above them — ingesting outputs, context, and decisions to power faster analysis without disrupting existing infrastructure.
“How will you access data from legacy systems?”
Many legacy platforms were built as systems of record, not systems of understanding. REMI sits above them — ingesting outputs, context, and decisions to power faster analysis without disrupting existing infrastructure.
“Why not just buy an enterprise AI license and build internally?”
Who maintains it when the best model changes every 90 days? Who migrates from Feb 2026 models to Dec 2026 models that are 10× better and cheaper? REMI abstracts that complexity — we handle model orchestration, versioning, and firm-specific tuning so your team never has to.
“Why not just buy an enterprise AI license and build internally?”
Who maintains it when the best model changes every 90 days? Who migrates from Feb 2026 models to Dec 2026 models that are 10× better and cheaper? REMI abstracts that complexity — we handle model orchestration, versioning, and firm-specific tuning so your team never has to.
Let's build the Intelligence Layer together.
Let's build the Intelligence Layer together.
Current Status — Phase 0 & 1
Core platform 70% done, QA testing heavily
- Evaluating AI Tagging/Labeling solutions
Current Status — Phase 0 & 1
Core platform 70% done, QA testing heavily
- Evaluating AI Tagging/Labeling solutions
Pre-Seed Round
$1.2M – $1.5M
Open to SAFE or priced round
Targeting 18-month runway
Pre-Seed Round
$1.2M – $1.5M
Open to SAFE or priced round
Targeting 18-month runway
Additional Materials
Additional Materials
Appendix
Appendix
Deep dives on business model, platform architecture, learning loops, and risk analysis.
Deep dives on business model, platform architecture, learning loops, and risk analysis.
VS Generalist AI
VS Generalist AI
Claude is Stateless. REMI Has Memory.
Claude is Stateless. REMI Has Memory.
General AI is powerful infrastructure. But institutional RE needs a memory layer that compounds with each deal, not a blank slate every session.
General AI is powerful infrastructure. But institutional RE needs a memory layer that compounds with each deal, not a blank slate every session.
Dimension
Generalist AI
REMI
Memory
Session ends, context resets.
Persistent Context Graph compounds each deal cycle.
Workflow
Prompt-by-prompt, human-driven.
Pre-wired RE pipeline with human checkpoints.
Output Quality
Generic answer quality.
Firm-specific outputs with citations and rationale.
Learning
No institutional feedback memory.
Recursive learning from decisions and outcomes.
Dimension
Generalist AI
REMI
Memory
Session ends, context resets.
Persistent Context Graph compounds each deal cycle.
Workflow
Prompt-by-prompt, human-driven.
Pre-wired RE pipeline with human checkpoints.
Output Quality
Generic answer quality.
Firm-specific outputs with citations and rationale.
Learning
No institutional feedback memory.
Recursive learning from decisions and outcomes.
REMI will use a variety of different LLM's as core model infrastructure. The Context Graph is the product moat that turns general models into institutional RE intelligence.
REMI will use a variety of different LLM's as core model infrastructure. The Context Graph is the product moat that turns general models into institutional RE intelligence.
REMI is not hands-off automation - it is decision support with full auditability. We prioritize transparency over opacity. Every output is cited, every action is logged, every decision is yours.
REMI is not hands-off automation - it is decision support with full auditability. We prioritize transparency over opacity. Every output is cited, every action is logged, every decision is yours.
Competitive Landscape
Competitive Landscape
The Market is Ripe for Disruption
The Market is Ripe for Disruption
Yardi makes $1.6B/year on 40-year-old architecture. Point solutions lack context. General AI lacks firm memory. REMI is built AI-first.
Yardi makes $1.6B/year on 40-year-old architecture. Point solutions lack context. General AI lacks firm memory. REMI is built AI-first.
Yardi / MRI
Legacy ERP
+ Strength
Market leader, $1.6B revenue, 20K customers
- Weakness
40-year-old architecture, AI bolted on
Yardi / MRI
Legacy ERP
+ Strength
Market leader, $1.6B revenue, 20K customers
- Weakness
40-year-old architecture, AI bolted on
Rogo AI / o11
Point Solution
+ Strength
IB-focused agents (Rogo, $75M Series C); M365-native modeling (o11)
- Weakness
Built for banking, not RE ops — no property-level context, no deal lifecycle memory, no autonomous workflows
Rogo AI / o11
Point Solution
+ Strength
IB-focused agents (Rogo, $75M Series C); M365-native modeling (o11)
- Weakness
Built for banking, not RE ops — no property-level context, no deal lifecycle memory, no autonomous workflows
ChatGPT / Claude
General AI
+ Strength
Powerful, accessible
- Weakness
No firm memory, no integration
ChatGPT / Claude
General AI
+ Strength
Powerful, accessible
- Weakness
No firm memory, no integration
REMI
AI-Native Platform
+ Strength
Context Graph, autonomous agents
- Weakness
Early stage, unproven at scale
REMI
AI-Native Platform
+ Strength
Context Graph, autonomous agents
- Weakness
Early stage, unproven at scale
Business Model
Business Model
Intelligence-as-a-Service Pricing
Intelligence-as-a-Service Pricing
Platform fee based on fund size + metered agent usage. Revenue scales with client success — more deals = more value = higher contracts.
Platform fee based on fund size + metered agent usage. Revenue scales with client success — more deals = more value = higher contracts.
Platform Fee (Annual)
Includes: Context Graph, integrations, firm knowledge base, security infrastructure
Usage Based Pricing
Platform Fee (Annual)
Includes: Context Graph, integrations, firm knowledge base, security infrastructure
Usage Based Pricing
Why This Model Works
Revenue scales with client success. More deals processed = more value delivered = higher contract value. Perfectly aligned incentives.
Why This Model Works
Revenue scales with client success. More deals processed = more value delivered = higher contract value. Perfectly aligned incentives.
REMI Platform Stack
REMI Platform Stack
A purpose-built operating system for institutional RE intelligence
A purpose-built operating system for institutional RE intelligence
REMI is not a single model wrapper. It is a layered platform that combines firm ontology, context graph memory, governed agent execution, and client-facing workflows.
REMI is not a single model wrapper. It is a layered platform that combines firm ontology, context graph memory, governed agent execution, and client-facing workflows.
Prebuilt AI Products
Deal Screening, IC Memos, Portfolio Monitoring, LP Reporting
Custom AI Products
Firm-specific workflows, rubrics, approvals, and reporting formats
Build with REMI
Prebuilt AI Products
Deal Screening, IC Memos, Portfolio Monitoring, LP Reporting
Custom AI Products
Firm-specific workflows, rubrics, approvals, and reporting formats
Build with REMI
Context Graph + Quick Memory
Deal → Asset → Portfolio → Fund relationships + decision memory
Context Graph + Quick Memory
Deal → Asset → Portfolio → Fund relationships + decision memory
Data Services
Ingestion, parsing, entity resolution, retrieval, and lineage
AI Services
Model orchestration, agent planning, evaluation, and recursive learning
Workflow Services
Deliverables, approvals, notifications, and operating actions
Data Services
Ingestion, parsing, entity resolution, retrieval, and lineage
AI Services
Model orchestration, agent planning, evaluation, and recursive learning
Workflow Services
Deliverables, approvals, notifications, and operating actions
Security + Governance Layer
Role-based access, audit trails, policy controls, and deployment optionality
Security + Governance Layer
Role-based access, audit trails, policy controls, and deployment optionality
Software Delivery Layer
Web app, APIs, integrations, and productized workflows for each client
Software Delivery Layer
Web app, APIs, integrations, and productized workflows for each client
Technical Architecture
Technical Architecture
Context Graph + Multi-Agent Intelligence
Context Graph + Multi-Agent Intelligence
REMI is built as an institutional intelligence system, not a chat wrapper. Data is ingested, normalized, linked in a firm-level graph, reasoned over by specialized agents, and returned with citations and controls.
REMI is built as an institutional intelligence system, not a chat wrapper. Data is ingested, normalized, linked in a firm-level graph, reasoned over by specialized agents, and returned with citations and controls.
Data Ingestion + Parsing
- →Email/API/webhook ingestion (deal flow + updates)
- →Document pipeline: OCR + table extraction + chunking
- →Entity normalization (asset, sponsor, market, tenant, covenant)
Data Ingestion + Parsing
- →Email/API/webhook ingestion (deal flow + updates)
- →Document pipeline: OCR + table extraction + chunking
- →Entity normalization (asset, sponsor, market, tenant, covenant)
Context + Knowledge Layer
- →Context Graph (deal -> asset -> portfolio -> fund)
- →Hybrid retrieval (graph traversal + vector search)
- →Firm ontology + IC rubric + thesis constraints
Context + Knowledge Layer
- →Context Graph (deal -> asset -> portfolio -> fund)
- →Hybrid retrieval (graph traversal + vector search)
- →Firm ontology + IC rubric + thesis constraints
Reasoning + Agent Layer
- →Multi-LLM routing by task (extract, reason, draft, verify)
- →Agent orchestration (screening, compare, memo, monitor)
- →Citations + confidence scoring + policy checks
Reasoning + Agent Layer
- →Multi-LLM routing by task (extract, reason, draft, verify)
- →Agent orchestration (screening, compare, memo, monitor)
- →Citations + confidence scoring + policy checks
Application + Workflow Layer
- →New Deal -> Draft workflow with human checkpoints
- →Review UI (approve, revise, reject, watchlist)
- →Feedback loop writes outcomes back to graph
Application + Workflow Layer
- →New Deal -> Draft workflow with human checkpoints
- →Review UI (approve, revise, reject, watchlist)
- →Feedback loop writes outcomes back to graph
Security + Governance Layer
- →RBAC + tenant isolation + encrypted storage
- →Audit logs for prompts, actions, and approvals
- →Deployment flexibility: cloud, VPC, or on-prem constraints
Security + Governance Layer
- →RBAC + tenant isolation + encrypted storage
- →Audit logs for prompts, actions, and approvals
- →Deployment flexibility: cloud, VPC, or on-prem constraints
Moat: The Context Graph compounds with every approved output, every exception, and every deal outcome. Features are copyable. Institutional memory with governance is not.
Moat: The Context Graph compounds with every approved output, every exception, and every deal outcome. Features are copyable. Institutional memory with governance is not.
REMI Learning Loop
REMI Learning Loop
Validating Centaur Systems Theory for real estate investment workflows
Validating Centaur Systems Theory for real estate investment workflows
Centaur systems combine human intuition, context, and ethics with algorithmic speed and scale. REMI is built to test that strong merge in live investment operations: AI proposes at scale, humans apply judgment, and each interaction compounds firm intelligence.
Centaur systems combine human intuition, context, and ethics with algorithmic speed and scale. REMI is built to test that strong merge in live investment operations: AI proposes at scale, humans apply judgment, and each interaction compounds firm intelligence.
How REMI compounds
1) Ingest Context
Deals, docs, market data, and prior firm decisions
2) Agent Drafts
REMI proposes recommendations with source citations
3) Human Judgment
Analyst/IC review, approve, reject, or override
4) Outcome Capture
Final decisions and downstream performance are recorded
5) Memory Update
Context Graph learns patterns to improve next-cycle quality
How REMI compounds
1) Ingest Context
Deals, docs, market data, and prior firm decisions
2) Agent Drafts
REMI proposes recommendations with source citations
3) Human Judgment
Analyst/IC review, approve, reject, or override
4) Outcome Capture
Final decisions and downstream performance are recorded
5) Memory Update
Context Graph learns patterns to improve next-cycle quality
Validation Objectives
Increase decision consistency across analysts and associates
Reduce cycle time from intake to committee-ready materials
Improve early risk signal detection from prior deal memory
Raise trust via citations, approvals, and auditability
References: human-AI collaborative decision-making research framing (Springer, MIT HDSR).
Validation Objectives
Increase decision consistency across analysts and associates
Reduce cycle time from intake to committee-ready materials
Improve early risk signal detection from prior deal memory
Raise trust via citations, approvals, and auditability
References: human-AI collaborative decision-making research framing (Springer, MIT HDSR).
Go-To-Market
Go-To-Market
Bottoms-Up, Then Enterprise
Bottoms-Up, Then Enterprise
REMI creates value whether users touch it or not. We prove value early through targeted, proactive notifications tied to live deals, then drive bottoms-up adoption with analysts and associates before broader organizational rollout.
REMI creates value whether users touch it or not. We prove value early through targeted, proactive notifications tied to live deals, then drive bottoms-up adoption with analysts and associates before broader organizational rollout.
Phase 1: Design Partners
Now → Month 6- 3-5 mid-market RE firms
- Free pilots, high-touch onboarding
- Overinvest in early partner success to create raving advocates in a network-driven niche
Target
Target: first 5 LOIs
Phase 1: Design Partners
Now → Month 6- 3-5 mid-market RE firms
- Free pilots, high-touch onboarding
- Overinvest in early partner success to create raving advocates in a network-driven niche
Target
Target: first 5 LOIs
Phase 2: Early Revenue
Month 6 → Month 12- Convert pilots to paid
- Content marketing + thought leadership
- Referral program launch
Target
15-25 clients onboarded
Phase 2: Early Revenue
Month 6 → Month 12- Convert pilots to paid
- Content marketing + thought leadership
- Referral program launch
Target
15-25 clients onboarded
Phase 3: Scale
Month 12 → Month 24- Multi-channel acquisition engine
- Repeatable onboarding and account expansion
- Expand into adjacent alternative asset workflows
Target
Target: 100+ clients across 2-3 Alt Asset Classes
Phase 3: Scale
Month 12 → Month 24- Multi-channel acquisition engine
- Repeatable onboarding and account expansion
- Expand into adjacent alternative asset workflows
Target
Target: 100+ clients across 2-3 Alt Asset Classes
35%
Shadow Mode Pilots
Zero-risk 90-day pilots — REMI runs parallel to existing ops, proves value before any workflow changes
25%
Founder-Led Outbound
Direct outreach to mid-market GPs/analysts via LinkedIn, warm intros, and prior network
20%
Content & Thought Leadership
Case studies from pilot partners, founder articles, RE-specific AI insights
10%
Partnerships & Referrals
Fund administrators, RE accountants, and consultants as channel partners
10%
Events & Conferences
NMHC, IMN, CRE Finance Council — targeted RE industry presence
35%
Shadow Mode Pilots
Zero-risk 90-day pilots — REMI runs parallel to existing ops, proves value before any workflow changes
25%
Founder-Led Outbound
Direct outreach to mid-market GPs/analysts via LinkedIn, warm intros, and prior network
20%
Content & Thought Leadership
Case studies from pilot partners, founder articles, RE-specific AI insights
10%
Partnerships & Referrals
Fund administrators, RE accountants, and consultants as channel partners
10%
Events & Conferences
NMHC, IMN, CRE Finance Council — targeted RE industry presence
Adoption Strategy
Adoption Strategy
Zero-Risk Adoption - Meet Firms Where They Are
Zero-Risk Adoption - Meet Firms Where They Are
For The Analyst
Career Protection
REMI catches what you might miss. You approve everything. You stay the hero, not the system.
For The Analyst
Career Protection
REMI catches what you might miss. You approve everything. You stay the hero, not the system.
For The Principal
Institutional Memory
Never lose firm knowledge when people leave. Preserve the why behind every decision.
For The Principal
Institutional Memory
Never lose firm knowledge when people leave. Preserve the why behind every decision.
For Operations
Zero-Risk Entry
REMI watches, learns, and waits. Nothing changes until your team is ready.
For Operations
Zero-Risk Entry
REMI watches, learns, and waits. Nothing changes until your team is ready.
Read-only integrations first - REMI observes, does not write
Read-only integrations first - REMI observes, does not write
Shadow mode - See what REMI would have caught, risk-free
Shadow mode - See what REMI would have caught, risk-free
Graceful exit - If REMI disappears, nothing breaks
Graceful exit - If REMI disappears, nothing breaks
Human approves everything - Always
Human approves everything - Always
The question is not "Will this replace my team?" It is "What am I missing right now that REMI would catch?"
The question is not "Will this replace my team?" It is "What am I missing right now that REMI would catch?"
Pilot Structure
Pilot Structure
90-Day Proof of Value - No Contract Required
90-Day Proof of Value - No Contract Required
Start risk-free, demonstrate value in production conditions, then scale with confidence.
Start risk-free, demonstrate value in production conditions, then scale with confidence.
Step 1
Connect
Read-only integrations for email, Yardi, and shared drives.
Outcome: No workflow disruption
Step 1
Connect
Read-only integrations for email, Yardi, and shared drives.
Outcome: No workflow disruption
Step 2
Shadow Mode
REMI runs silently and shows what it would have flagged earlier.
Outcome: Proof before trust
Step 2
Shadow Mode
REMI runs silently and shows what it would have flagged earlier.
Outcome: Proof before trust
Step 3
Activate
Analysts use REMI drafts for IC memos with full human approval.
Outcome: Before vs after gains
Step 3
Activate
Analysts use REMI drafts for IC memos with full human approval.
Outcome: Before vs after gains
Step 4
Prove
Review throughput, quality, and decision speed metrics together.
Outcome: Conversion decision
Step 4
Prove
Review throughput, quality, and decision speed metrics together.
Outcome: Conversion decision
If it works, they do not turn it off. If it does not, we learn why and improve the system. Either outcome compounds value.
If it works, they do not turn it off. If it does not, we learn why and improve the system. Either outcome compounds value.
Risk Analysis
Risk Analysis
Eyes Wide Open: Risks & Mitigations
Eyes Wide Open: Risks & Mitigations
Every startup has risks. Here's how I'm thinking about ours — and what I'm doing about them.
Every startup has risks. Here's how I'm thinking about ours — and what I'm doing about them.
AI Commoditization
LowLLMs become commodities; features get copied quickly.
Our moat is not AI — it's the Context Graph. No AI company is building firm-specific knowledge graphs for each organization. The graph compounds from approvals, overrides, and outcomes. Competitors can copy features, but not accumulated intelligence.
✓ Action: Building proprietary data flywheel from day one
AI Commoditization
LowLLMs become commodities; features get copied quickly.
Our moat is not AI — it's the Context Graph. No AI company is building firm-specific knowledge graphs for each organization. The graph compounds from approvals, overrides, and outcomes. Competitors can copy features, but not accumulated intelligence.
✓ Action: Building proprietary data flywheel from day one
Data Security Concerns
MediumRE firms are paranoid about data leaving their walls
SOC 2 readiness and certification program on roadmap. Enterprise deployment options (VPC, on-prem). Zero data sharing between firms. Enterprise-grade encryption.
✓ Action: Security audit Q3 2026; compliance certifications Q1 2027
Data Security Concerns
MediumRE firms are paranoid about data leaving their walls
SOC 2 readiness and certification program on roadmap. Enterprise deployment options (VPC, on-prem). Zero data sharing between firms. Enterprise-grade encryption.
✓ Action: Security audit Q3 2026; compliance certifications Q1 2027
Incumbent Response
LowYardi/MRI bolt on AI features
Incumbents are burdened by technical debt. Their AI is a feature; ours is the architecture. They optimize existing workflows; we reimagine them.
✓ Action: Move fast, lock in design partners before incumbents react
Incumbent Response
LowYardi/MRI bolt on AI features
Incumbents are burdened by technical debt. Their AI is a feature; ours is the architecture. They optimize existing workflows; we reimagine them.
✓ Action: Move fast, lock in design partners before incumbents react
The Trust Gap
The Trust Gap
You Wouldn't Let AI File Your Taxes.
Why Would You Let It Deploy $100M?
You Wouldn't Let AI File Your Taxes.
Why Would You Let It Deploy $100M?
Even TurboTax asks you to review before filing. Deal-making at scale requires the same rigor — every number cited, every decision approved, every action auditable.
Even TurboTax asks you to review before filing. Deal-making at scale requires the same rigor — every number cited, every decision approved, every action auditable.
What People Fear
AI makes a decision I don't understand
Numbers appear from nowhere — no audit trail
I lose control of my own process
"Trust the AI" isn't good enough for LPs
“I need to explain this to my IC. 'The AI said so' doesn't cut it.”
What People Fear
AI makes a decision I don't understand
Numbers appear from nowhere — no audit trail
I lose control of my own process
"Trust the AI" isn't good enough for LPs
“I need to explain this to my IC. 'The AI said so' doesn't cut it.”
How REMI Works
“Show me exactly where that 12% IRR came from.” → Instant citation.
How REMI Works
“Show me exactly where that 12% IRR came from.” → Instant citation.
REMI is built for the Reviewer, not just the Runner. AI handles the 80% grunt work. Your team does the 20% expert judgment that actually matters.
REMI is built for the Reviewer, not just the Runner. AI handles the 80% grunt work. Your team does the 20% expert judgment that actually matters.
Founder's Thoughts
Founder's Thoughts
AI speed is the novelty. Context is the moat.
AI speed is the novelty. Context is the moat.
February 2026 feels like peak wow-factor for AI. That phase will fade. AI in spreadsheets and workflows will become table stakes, and firms that do not adopt will look ancient to the next generation of operators.
February 2026 feels like peak wow-factor for AI. That phase will fade. AI in spreadsheets and workflows will become table stakes, and firms that do not adopt will look ancient to the next generation of operators.
Three lines to remember
1) Automation without citations kills trust.
2) Context is alpha: data + decision history + operator feedback.
3) The end state is better reviewed decisions, not zero human decisions.
Three lines to remember
1) Automation without citations kills trust.
2) Context is alpha: data + decision history + operator feedback.
3) The end state is better reviewed decisions, not zero human decisions.