Contact person: Business Client

Phone:Show

Email:Show

Location: Magura, Bangladesh

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"Platinum Commercial currently manages operations using multiple separate tools — Reonomy,
CoStar, DocuSign, and cloud drives — and relies heavily on manual processes for property
research, valuation, communication, and reporting.
This project will build a fully custom CRM platform designed specifically for commercial
real-estate brokerage workflows, featuring:
● Direct integration with Reonomy and CoStar for property and ownership data.
● A built-in AI automation layer for summarization, outreach generation, and KPI insights
(cheaper and faster than external LLMs).
● Integrated DocuSign for digital contract management.
● Secure cloud storage for files and signed agreements.
Problem Statement
Agents currently waste hours each week switching between apps, re-entering data, and
manually calculating metrics.
This fragmentation leads to:
● Data inconsistency between systems
● Slow deal turnaround times
● Lost insights due to unstructured communication
● Limited visibility for managers
A unified, AI-enhanced CRM will eliminate manual work, standardize data, and accelerate
transactions.
Current Workflow (As-Is)
1. Agent meets client (e.g., investor seeking 50 + unit building in Midtown).
2. Uses Reonomy to identify qualifying properties.
3. Uses CoStar to gather detailed tenant and lease information.
4. Manually emails summarized information to client.
5. Requests landlord financials and calculates cap rate, pric


8. Uploads signed documents manually to a shared drive.
Proposed Integrated Workflow (To-Be)
[Reonomy API] + [CoStar API]
↓
[Integration Layer / Data Engine]
↓
[Custom CRM Platform (React + Node.js)]
├── Property Intelligence Module
├── Deal Pipeline (Lead → Valuation → Negotiation → PSA → Closed)
├── KPI Engine (auto-calculated metrics)
├── AI Automation Layer (summaries, outreach, insights)
├── Document Center (DocuSign + Cloud)
└── Dashboards & Reports
Project Objectives
● Build a centralized CRM to manage every stage of a commercial deal.
● Eliminate manual data entry by pulling data directly from Reonomy / CoStar.
● Use AI automation (on-prem or API-free) to:
○ Summarize property data and highlight key facts.
○ Generate draft outreach messages using templates + stored context.
○ Provide basic market or valuation insights.
● Automate KPI calculation and display results on live dashboards.
● Manage contracts through DocuSign integration.
● Store all records and documents in secure, searchable cloud storage.
Functional Requirements (by Module)
Module Core Functions
Property
Intelligence
Fetch and sync property, ownership, and lease data from Reonomy / CoStar;
searchable database with filters.

Deal
Pipeline
End-to-end tracking: Lead → Valuation → Negotiation → PSA → Closed;
auto-updates KPIs and deal status.
AI
Automation
Layer
Lightweight internal AI services (e.g., Python + ML models, rule-based NLP)
to summarize data, suggest outreach text, and surface key valuation insights.
KPI Engine Auto-calculates Cap Rate = Net Income / Purchase Price, Price per Foot,
and GMB from landlord inputs and stored data.
Document
Center
Integrates DocuSign API for contracts; tracks status; archives signed files in
cloud storage.
Dashboard
& Reports
Real-time visualization of pipeline volume, KPI trends, and agent
performance.
AI Automation Use Cases (No External LLMs)
Function Example
Property Summary
Generator
Extracts core info (owner, tenants, location, valuation metrics) and
produces a short paragraph summary.
Outreach Email
Helper
Uses predefined templates + stored property data to fill in details for
quick client communication.
Valuation Insights Highlights anomalies (e.g., unusually high cap rate) using simple
statistical models.
Follow-Up
Scheduler
Suggests reminders or next steps based on inactivity or deal stage
transitions.
Tech:
● Python-based NLP and rule-driven templates
● Pre-trained lightweight models (spaCy / Hugging Face mini models / scikit-learn
classifiers)
● Runs on the platform’s own backend for cost efficiency


System Architecture
[Reonomy API] [CoStar API]
│ │
└────→ [Integration Layer]
│
[CRM Backend (Node.js / Python)]
│
[Database (PostgreSQL)]
│
┌───────────┼───────────┐
│ │ │
[Web App (React)] │ [AI Engine] │ [DocuSign API]
│ │ │
Users Auto Summaries Contracts
│
[Cloud Storage / AWS S3]
User Roles & Permission Matrix
Role Description Permissions
Agent/Broker Manages property research, client
communication, and deals
Create/edit own deals, generate
summaries/outreach, send contracts
Manager Oversees team pipelines and KPIs View all pipelines, approve deals,
manage users, access dashboards
Admin Handles integrations, system
setup, and data security
Full system access, manage APIs,
backups, and audit logs
External
Legal (limited
guest access)
Optional one-time access to review
or upload executed contracts
For third party counsels


Technical Considerations & Constraints
● Data Licensing: Enterprise agreements required with Reonomy / CoStar for property
data ingestion.
● AI Implementation:
○ Use low-cost, open-source ML/NLP models or fine-tuned local models.
○ Avoid large external APIs (no ChatGPT dependency).
● Database: PostgreSQL with encryption and audit trail.
● Backend: Node.js for API orchestration; Python micro-services for AI tasks.
● Hosting: AWS or GCP cloud; auto-scaling containers (Docker / Kubernetes).
● Security: JWT auth, role-based access control, HTTPS, data encryption at rest + in
transit.
● Compliance: GDPR / CCPA alignment for any personal data.
Success Metrics & Scalability Plan
Metric Target
Manual data entry reduction 80 %
Average deal-closing time improvement 30 % faster
AI automation adoption ≥ 90 % of agents using
summaries/outreach
Data accuracy ≥ 98 % correctness
Platform uptime ≥ 99.9 %
Scalability Handle 100 + agents / 1 M property
records
Scalability:
● Add new integrations (LoopNet, MLS feeds) as modules.
● Support multi-office, multi-broker deployments.
● Optional white-label version for other brokerages.
Risks & Mitigation

Risk Description Mitigation
Data licensing
limits
Reonomy / CoStar usage
restrictions
Obtain enterprise data contracts; cache
results legally
AI accuracy Automated text generation may
misstate details
Rule-based validation and human
approval steps
User adoption Agents hesitant to switch tools Pilot with core team, provide training
and feedback loop
Integration
downtime
Third-party API outages Implement retry logic and monitoring
dashboards
Cost overrun Infrastructure and licensing fees Optimize cloud spend; use open-source
AI stack
Summary
This custom platform will unify Platinum Commercial’s entire deal lifecycle — from property
research to closing — into one AI-assisted, data-integrated CRM.
By using affordable, in-house AI automation instead of costly external APIs, the firm gains
efficiency and insight without ongoing LLM expenses.
Outcome:
A scalable, secure, and cost-effective real-estate CRM purpose-built for commercial brokers —
ready for internal deployment and future commercialization." (client-provided description)