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R

REMI

The Intelligence Layer
for Real Estate Capital

Pre-SeedFebruary 2026|chatremi.com
STARTING WITH CREDIBILITY

My Journey Here

2018

Fairview Capital Partners

Private EquityCo-Op

Helped onboard largest deal in firm history, PA-SERS, for my Co-Op at the boutique firm.

2019

IBM Global Financing + Watson AI

Data AnalystBusiness AnalystAI

Led 0-1 integration of Watson AI into IBM’s largest deal-making platform.

2021

Point72 Asset Management + New York Mets

Data EngineeringData ProductAsset ManagementAI/ML

Built multiple data platforms from 0-1 ranging from crypto to baseball. Featured in WSJ, NY Times, & more.

2024

SMBC / Jenius Bank

Data EngineeringData ProductCredit RiskOperationsDecision EngineData EncryptionData Security

Helped SMBC build & scale data platform for their first Digital Banking Unit.

2026

REMI

Left corporate to focus full-time on building the solution I wish existed at all my prior employers. Building the solution I wished existed — now with AI capable enough to make it real.

Nadi Haque — Founder

The Problem

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 chain reactions — work on a deal, that's tied to an asset, that's in a portfolio. They don't know your assets, your thesis, your IC, or what happened on the last deal.

🧮

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 Memory

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.

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.

The Trust Gap

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.

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

C
CITE: Every number links to source (page 42, cell B7)
R
REVIEW: AI proposes, you dispose — nothing ships without approval
F
FEEL: Full visibility into what's running and why

“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.

Current Solutions

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.

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

Generic outputs that miss deal-specific nuance

Starts from 0 every session

No compounding knowledge, no firm memory

Data prompts the decision making, but the memory of those decisions are stuck in human heads. An individual gets wiser, but the firm remains the same.

What if instead…

You started every morning
at the finish line?

Drafts ready. Models updated. Insights waiting.AI that works while you sleep — and remembers everything.

Emails
PDFs
Feeds
IC Memos
Models
Insights
Captures everythingLinks to every dealWorks 24/7Never forgets
Meet REMI

AI Agents That Know
Your Entire Firm.

REMI builds a living Context Graph of your deals and deploys autonomous agents that work 24/7.

Context GraphEvery fact linked
Recursive LearningGets smarter
Autonomous Agents168 hrs/week
5 minutesfrom email to draft

Email Arrives

Deal flow from broker

PDFs Extracted

OM, financials, rent rolls

Agent

Context Graph

Link to asset & fund history

CG

Compare Past Deals

Match against 847 prior deals

RL

Apply Fund Rules

IC criteria, thesis filters

AgentFlag

Draft Generated

IC memo + model ready

Human Review

Your decision, your control

Pursue
Diligence Agent
Pass
Archive + Learn
Watchlist
Monitor Agent
Running 24/7 · 168 hours/week · Human in the loop

Market Opportunity

$25B+ Total Addressable Market — The Invisible Majority

$1.6B+ Core RE TAM is just the tip of the iceberg

Visible

Institutional

(Preqin Tracked)

Waterline

~8-10K Firms

Enterprise & Mid-Market

High ACV ($150K+)

~12-15K Firms

Mid-Market & Boutique Sponsors

Moderate ACV ($50K-100K)

~20-30K Firms

Active Capital Raisers

Accessible ACV ($20K-40K)

30,000+ Reg D Filers/Yr

Long Tail / Pro-Sumer Investors

Volume ACV ($5K-15K)

The Invisible

Market

(Boutiques,

Syndicators,

Family Offices)

Segment

Firms

Est. ACV

Market Size

Enterprise & Global Giants

~500

$250K+

$125M+

Core: Mid-Market Institutions

~8,000

$100K

$800M

Active Capital Raisers (Reg D)

~35,000+

$30K (Avg)

$1.05B+

Adjacent: Private Credit

~4,000

$120K

$480M

Adjacent: Infrastructure & PE

~6,000

$150K

$900M+

Total Addressable Market

~100K+ Firms

Blended ACV

$25B+

The Strategic Insight: Same Product, Two Motivations.

Institutions buy for EFFICIENCY (replace analysts, reduce cost). The Invisible Market buys for CAPABILITY (access institutional-grade tools they couldn't afford otherwise).

Sources: Preqin 2025, SEC Reg D Filings (Annual), AppFolio 10-K (Customer Count), IRS Partnership Data

Why Now

The AI Wave is Here

Coding was the first knowledge-based skill to fall to AI. With AI Agents unlocked, operational knowledge-work is next.

Coding Assistant Adoption

12× growth in under 3 years

AI Workers Projection

Benjamin Miller, CEO Fundrise
Year% AutomatedFTE Equiv.
20263%1.7M
20278.6%4.9M
202816.8%9.6M
202927.1%15.6M
203035.4%20.4M

By 2030: 35.4% of all US knowledge-work hours automated — equivalent to 20.4M FTE workers.

Operations is next.

Why Me

Built for This Exact Problem

I've built this exact type of system — enterprise AI data platforms inside complex financial institutions — four times. Understanding RE workflows is learnable. Building institutional-grade AI infrastructure that actually works? That takes years of scar tissue.

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 unification with AI on top.

Enterprise Financial Systems

Helped SMBC build their first Digital Banking Unit. Understand how institutional clients evaluate, buy, and trust new technology. 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.

Builder DNA

Every role has been 0→1 platform builds inside complex organizations. Left corporate to build the solution I wished existed at every prior employer — now with AI capable enough to make it real.

Why Not

Risks I'm Thinking About

You're going to ask these questions. Let me address them head-on.

AI is commoditizing fast. What's your moat?

Context Graphs 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.

Addressed

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.

Addressed

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.

Addressed

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.

Actively Managing

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.

Addressed

VS Generalist AI

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.

Dimension

Generalist AI

REMI

Memory

Session ends, context resets.

Persistent Context Graph compounds each deal cycle.

Workflow

Prompt-by-prompt, analyst-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.

We use Claude/GPT as core model infrastructure. The Context Graph is the product moat that turns general models into institutional RE intelligence.

REMI is not “Autopilot” — it's “Co-Pilot” with a black box recorder. We prioritize transparency over autonomy. Every output is cited, every action is logged, every decision is yours.

Let's build the Intelligence Layer together.

Current Status — Phase 0

Core platform in active development
Deal Screening agent functional
No LOIs yet; founder-led outreach now underway
Targeting 3-5 design partners for Q1 pilot

Pre-Seed Round

$1.2M – $1.5M

Open to SAFE or priced round

Targeting 18-month runway

Engineering40%
Data/ML16%
GTM12%
Founder$2.5K/mo

Additional Materials

Appendix

Deep dives on business model, technical architecture, competitive landscape, and risk analysis.

Competitive LandscapeBusiness ModelTech ArchitectureGTM StrategyProduct RoadmapRisk Deep DiveFounder’s Thoughts

Competitive Landscape

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 / MRI

Legacy ERP

+ Strength

Market leader, $1.6B revenue, 20K customers

- Weakness

40-year-old architecture, AI bolted on

AI: Bolted-on

VTS / Dealpath

Point Solution

+ Strength

Modern UX, strong in niche

- Weakness

Single workflow, no context

AI: Limited

ChatGPT / Claude

General AI

+ Strength

Powerful, accessible

- Weakness

No firm memory, no integration

AI: Generic

REMI

AI-Native Platform

+ Strength

Context Graph, autonomous agents

- Weakness

Early stage, unproven at scale

AI: Purpose-built
Dimension
Legacy ERP
Point Solutions
Generalist AI
REMI
Firm Context
Autonomous Agents
Cross-Deal Learning
24/7 Processing
Human in Loop
Enterprise Ready
🔨

Business Model

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 (Annual)

$100M–500M AUM$75K/yr
$500M–1B AUM$100K/yr
$1B–5B AUM$150K/yr

Includes: Context Graph, integrations, firm knowledge base, security infrastructure

Agent Usage (Per Deliverable)

Deal ScreeningMetered
IC Memo GenerationMetered
LP Report AssemblyMetered
Portfolio MonitoringIncluded

Unit Economics

Target ACV

$100K

Blended average

LTV:CAC

10:1

Industry avg: 3:1

NRR Target

130%

Usage expands

Payback

3 mo

vs 12mo industry

💡 Perfectly aligned incentives: more deals processed = more value delivered = higher contract value.

Technical Architecture

Context Graph + Multi-Agent Intelligence

Every piece of information connects to every other. Agents don't just process — they understand context and learn continuously.

Intelligence Layer

  • Context Graph (Neo4j)
  • Recursive Learning Engine
  • Multi-Agent Orchestration

Processing Layer

  • Document AI (Vision + OCR)
  • LLM Pipeline (Claude/GPT)
  • Vector Embeddings (Pinecone)

Data Layer

  • Time-Series Store
  • Document Lake (S3)
  • Firm Knowledge Base

Integration Layer

  • Email APIs
  • CRM Connectors
  • Market Data Feeds

🔮 Moat: The Context Graph becomes exponentially more valuable with each deal processed. Competitors can copy features — they can't copy your firm's accumulated intelligence.

Go-To-Market

Bottoms-Up, Then Enterprise

Start with analysts and associates. Once they can't live without REMI, partners will notice.

Phase 1: Design Partners

Now → Month 6
  • 3-5 mid-market RE firms
  • Free pilots, high-touch onboarding
  • Build case studies + testimonials

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

$300K ARR

Phase 3: Scale

Month 12 → Month 24
  • Inside sales team (2-3)
  • Enterprise tier launch
  • Channel partnerships

Target

$2M ARR

40%

Content & SEO

Thought leadership, case studies

30%

Direct Outbound

Targeted firm outreach

20%

Partnerships

Accountants, consultants

10%

Events & Conferences

RE industry events

Adoption Strategy

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 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.

Read-only integrations first - REMI observes, does not write

Shadow mode - See what REMI would have caught, risk-free

Graceful exit - If REMI disappears, nothing breaks

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?"

Pilot Structure

90-Day Proof of Value - No Contract Required

Start risk-free, demonstrate value in production conditions, then scale with confidence.

Week 1-2

Connect

Read-only integrations for email, Yardi, and shared drives.

Outcome: No workflow disruption

Week 3-4

Shadow

REMI runs silently and shows what it would have flagged earlier.

Outcome: Proof before trust

Week 5-8

Activate

Analysts use REMI drafts for IC memos with full human approval.

Outcome: Before vs after gains

Week 9-12

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.

Product Roadmap

18-Month Vision: From MVP to Market Leader

Laser-focused on deal sourcing for mid-market RE, then expand to full investment lifecycle and adjacent verticals.

Q1-Q2 2026Building

Foundation

  • Core Context Graph architecture
  • Email + PDF ingestion pipeline
  • Deal screening agent v1
  • Design partner pilots (3-5 firms)
Q3-Q4 2026Planned

Intelligence

  • Recursive learning engine
  • Multi-agent orchestration
  • IC memo generation
  • First paying customers
Q1-Q2 2027Planned

Scale

  • Asset management agents
  • Portfolio analytics dashboard
  • CRM/ERP integrations
  • Enterprise security (SOC 2 readiness)
Q3-Q4 2027Vision

Expansion

  • Private credit vertical
  • LP reporting automation
  • White-label platform
  • Series A readiness

Key Metric: $2M ARR by Month 24 with 20+ mid-market RE firms

Risk Analysis

Eyes Wide Open: Risks & Mitigations

Every startup has risks. Here's how I'm thinking about ours — and what I'm doing about them.

AI Commoditization

Medium

LLMs become commodity; features get copied quickly

Context Graph is the moat — it compounds from your firm's approvals, overrides, and outcomes. Competitors can copy features, not accumulated intelligence.

✓ Action: Building proprietary data flywheel from day one

Data Security Concerns

High

RE firms are paranoid about data leaving their walls

SOC 2 Type II certification 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

Medium

Yardi/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

Sales Cycle Length

Medium

Enterprise RE sales can take 6-12 months

Bottoms-up adoption: analysts love REMI, push it up. Free pilots reduce friction. Focus on pain that costs money TODAY.

✓ Action: Design partner model validates demand before scaling sales

Founder's Thoughts

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.

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.