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Vikrama.
PropTech

Valuation engines, tenant analytics, and building intelligence. AI that scales across portfolios.

We build the systems that turn property data into investment and operational decisions.

Real estate decisions, from pricing and leasing to maintenance and investment, depend on data that's scattered across property managers, brokers, and building systems. We build AI-powered valuation models, tenant analytics platforms, and building management intelligence.

Why AI in Real Estate is different.

Real estate is a $280 trillion asset class still run largely on spreadsheets, gut feel, and manual comps. Commercial real estate firms spend an average of 2-3 weeks on due diligence per deal, pulling data from 10+ disconnected sources. In a market where speed determines who wins the bid, that delay is expensive.

AI-powered valuation models now achieve accuracy within 3-5% of appraised value on residential properties, updated daily instead of quarterly. Dynamic rent pricing algorithms are capturing 3-7% more revenue that fixed-rate approaches leave on the table. Predictive maintenance on building systems is cutting OPEX by double digits.

The opportunity is not just in transactions. It is in operations. Building management systems generate constant data from HVAC, elevators, energy meters, and access controls. Most of that data is ignored. The firms connecting it into a unified intelligence layer are running more profitable portfolios with less staff.

The Problems That Slow You Down

01

Valuations based on comps, not complete data

Appraisals rely on a handful of comparable sales. Market conditions, neighborhood trends, and building-level data aren't factored in.

02

Tenant management by spreadsheet

Lease dates, maintenance requests, communication history, tracked in Excel by property managers juggling 50+ units.

03

Building operations are reactive, not predictive

HVAC fails, then gets fixed. Elevators break, then get serviced. Energy costs spike, then someone investigates.

04

Investment analysis takes weeks

Due diligence on a new property means manual data gathering from 10+ sources. Deals move faster than your analysis.

These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.

What We Build in PropTech

AI Valuation & Rental Yield Engine

Head of Investments
Problem

Underwriting deals takes days; valuations miss micro-local drivers (schools, transit, noise).

System

Gradient-boosted AVM on transactions, property features, geospatial layers, macro indicators, with API to underwriting/BI.

More accurate valuations, superior pricing vs legacy approaches.

Dynamic Rent Pricing

VP Revenue Management
Problem

Manual comp checks; rents updated infrequently, 3-7% revenue left on table.

System

Time-series + gradient boosting on occupancy, lease expiries, comps, events, delivering real-time recs into PMS/Yardi.

Dynamic pricing outperforms competing platforms in accuracy.

Portfolio Underwriting Copilot

Head of Acquisitions
Problem

Analysts spend weeks pulling leases, rent rolls, OPEX, legal docs; speed decides competitive bids.

System

LLM+RAG ingests rent rolls, T-12s, leases, OMs, then extracts assumptions, flags risks, and populates templates with scenarios.

Underwriting from weeks to hours.

Lease Abstraction Agent

General Counsel
Problem

Thousands of leases; legal teams manually abstract terms. Slow and error-prone.

System

LLM parses PDFs, pulls structured fields (rent, step-ups, CAM, options), summarizes obligations, natural-language portfolio queries.

Instantly reads/categorizes hundreds of documents, rapid cross-portfolio queries.

Predictive Maintenance Engine

Head of Property Operations
Problem

Reactive HVAC/elevator maintenance; unplanned outages, higher OPEX, unhappy tenants.

System

Time-series on BMS/IoT sensors + work-order history, flagging likely failures and auto-generating work orders in CMMS/PMS.

Reduced downtime, lower OPEX, better tenant satisfaction.

Lead Qualification & Touring Agent

VP Leasing
Problem

Leasing teams flooded with low-intent leads; slow response, high CAC.

System

Multi-channel LLM agent scores intent, schedules tours via calendar, hands off warm leads to humans.

Scale operations without headcount increase, faster response.

Tenant Service Copilot

Head of Property Management
Problem

Tenants log issues via email/phone; manual triage, slow resolution for large portfolios.

System

LLM classifies requests, triggers work orders, answers FAQs, provides status updates, integrated with PMS/CMMS.

Hours saved weekly, improved service quality.

Energy Optimization

Head of Sustainability
Problem

Static BMS configs; rising energy costs, ESG reporting pressure.

System

ML on consumption, weather, occupancy, tariffs, producing dynamic setpoints, schedules, and retrofit recommendations.

Double-digit energy savings, better ESG reporting, improved NOI.

Building the system is half the job. Growing the business around it is the other half.

How We Grow PropTech Brands

AEO for Property Tool Queries

Growth Marketing Director
Problem

Proptech loses 47% of "best CRM for realtors" to ChatGPT; zero-clicks cut trials 31%.

Build

Schema tool comparisons, agent FAQs, Perplexity tracking to onboarding flows.

39% AI citation uplift, 24% lead growth in 6 months.

LinkedIn Ads for Broker Tools

Demand Gen Lead
Problem

LinkedIn CPL $70-110 for CRE software; under 10% demo.

Build

Broker targeting, webinars, HubSpot nurture/scoring.

CAC 34% down to $55, pipeline 2x.

HubSpot Lead Scoring for Listings

Marketing Ops Manager
Problem

2K agent leads/quarter; 64% unqualified.

Build

HubSpot scoring (property searches), sequences, MLS sync.

Listing pipeline 43% growth, cycle 26% shorter.

Frequently asked questions

AI moves fast. Stay ahead.

No spam. One actionable email per week on AI systems and growth.

Your portfolio generates data every second. We turn it into decisions.

Start with a portfolio data audit. We'll identify where AI and automation create the most value across your properties.