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AI that moves the P&L.

We find where AI and software will change your business, then we build it. Led by a former private equity investor and consumer-brand CEO who still writes the code.

Book an intro callSee what we've built

Founder background

  • UBS
  • Oaktree Capital
  • Wharton MBA
  • MeUndies

Why us

Strategy firms don't build. Dev shops don't know your P&L. We've done both.

Investor

We know what moves enterprise value.

Private equity trained. We start from margins, cash and returns, not from the latest model release, and we size every idea before we build it.

Operator

We have run the business.

Six years as a consumer-brand CEO while revenue grew more than 100x. We know what it takes for a team to actually adopt a new tool, and what gets quietly ignored.

Engineer

We build it ourselves.

No hand-offs to a junior team. The person who scopes your problem builds the data, models and code, ships it into your stack and stays accountable for the result.

What we build

Organized by business problem, not by buzzword.

  • 01

    Pricing & forecasting

    Machine learning models for pricing, demand and inventory, with confidence ranges your team can trust.

  • 02

    Decision engines

    Underwriting, scoring and screening tools that turn a spreadsheet ritual into a repeatable, auditable process.

  • 03

    Workflow automation

    The repetitive back-office work: collections, reporting, renewals, approvals, vendor follow-ups.

  • 04

    AI for inbound

    Language models that read, classify, route and draft replies to leads, customers and vendors, with a human in the loop.

  • 05

    Data foundation

    One clean warehouse fed by your systems, with dashboards and KPIs leadership actually uses.

  • 06

    Internal & mobile apps

    Purpose-built tools for your field and office teams when off-the-shelf software does not fit.

Systems we've built and run

Built in our own businesses, under our own P&L.

Every system below runs, or ran, a real operating company. Details are anonymized; the numbers are real.

Property operator

AI leasing assistant

Automated outreach, follow-up and list management for rental leads, so every prospect hears back and vacant homes fill faster.

17% → 4.8%portfolio vacancy, three months after launch
Read

Real estate investor

Automated underwriting engine

A pipeline that ingests listings across metros, enriches every property, underwrites it and screens out bad deals before an analyst ever looks.

30,000+properties underwritten automatically
Read

Real estate investor

Machine learning valuation models

Rent and after-repair-value models trained on tens of thousands of transactions, served by API with confidence ranges.

6.2%average rent error on ~17K leases
Read

Property operator

Field inspection app with AI scoping

An offline-first mobile inspection app that drafts renovation scopes automatically and documents condition for turns and chargebacks.

Days → hoursto produce a renovation scope of work
Read
Parcel targeting map: parcels that pass the filters in green, disqualified parcels in red
Map © Mapbox © OpenStreetMap

Acquisitions company

Custom acquisitions CRM with multichannel campaigns

A CRM that turns public county records into scored seller leads, runs automated mail, SMS and email campaigns, and triages every reply with AI.

490K+county parcels ingested and scored into 127K prospects
Read

Property operator

One operating hub across AppFolio, Asana and the leasing stack

Twenty-five-plus automations that connect the property management system, leasing tools and phones to Asana, so the whole team works from one place with the right information.

25+automations feeding one Asana workspace
Read
All case studies

Data we build

When the data doesn't exist, we build it.

Six in-house data services behind our underwriting engine. Each one started as a question no vendor could answer well enough, and each now runs on its own.

  • National flood intelligence

    5.1Mflood-hazard zones nationwide

    The FEMA flood zone for any US address or parcel boundary, not just a point, and it tells "unmapped" apart from "low risk."

    FEMA, NOAA, county parcels

  • Market scoring and forecasting

    1,020metros and 26K ZIP codes forecast

    Rent and home-price forecasts plus explainable scores for growth, value, job-base resilience and market heat.

    Census, BLS, HUD, housing market data

  • Sound score

    85%within ±5 points of a commercial benchmark

    An in-house noise model scores any US location 0–100 from highways, rail and airports, and flags busy streets. 168K properties scored.

    Federal road, rail, traffic and noise data

  • Lot intelligence

    1 meterelevation resolution

    Slope and terrain, lot shape, corner and flag lots, and which way the home faces, scored 0–100 for every parcel.

    USGS elevation, county parcels, road network

  • Photo condition scoring

    <2¢per property

    AI vision sorts listing photos room by room and rates kitchen, bath, interior and exterior condition. Every model change is benchmarked before it ships.

    Listing and street-level photos

  • Renovation scope generator

    ~50¢per whole-house scope, vs. 20+ staff hours

    Rules encode the company playbook; AI vision handles only what the rules miss. Drafts a line-item scope from a 437-item catalog, work that took a coordinator and their manager 20+ hours, for a person to approve.

    Inspection reports and photos

Your stack

From a Google Sheet to a mobile app.

We build in whatever you already run, and we are not resellers, so we have no reason to push a vendor.

Spreadsheets & workflows
Google Sheets, Apps Script, Excel
Web apps
TypeScript, React, Next.js, Node, Ruby on Rails
Mobile
iOS and Android (React Native)
Data & ML
Python, PostgreSQL, MongoDB, machine learning models
Cloud
Google Cloud, Vercel, Docker
AI
Anthropic, OpenAI, Google Gemini
Integrations
Asana, AppFolio, Slack, phone systems. If it has an API, we connect it. If it doesn’t, we find another way.

How we work together

Start with a sprint. Keep what works.

2 weeks

AI Opportunity Sprint

Find where AI pays off in your business, and prove it.

  • Interviews and workflow mapping across your core teams
  • Opportunities ranked by dollar impact and effort
  • A 90-day roadmap
  • One working prototype on a real problem

Monthly

Most common

Build Partner

We ship production tools into your stack, every month.

  • One focused build at a time, with a defined finish line
  • Measured against a baseline before we start
  • Weekly check-ins and same-day answers
  • Documentation and training for your team

Monthly

AI Advisor

Senior AI leadership without the full-time hire.

  • Roadmap and prioritization with your leadership team
  • Vendor and tool selection
  • Coaching for the people building internally
  • A monthly readout for leadership or the board

Process

Three steps. No 80-page strategy deck.

01

Find the money

We map how work actually gets done, and where time, margin and cash are leaking. Then we rank opportunities by dollar impact.

02

Build in your stack

We build on the tools you already use, in your own accounts, and ship in weeks, not quarters.

03

Hand it over

Documentation, training and a measured before and after. Your team runs it, and you own all of it.

  • You own everything we build
  • It runs in your accounts
  • Every build has a baseline metric
  • Senior people only. The person you meet writes the code.
Portrait of Bryan Lalezarian

Founder

Bryan Lalezarian

Founder & Managing Partner · UBS · Oaktree Capital · Wharton MBA · MeUndies

Bryan began his career in M&A and leveraged finance at UBS Investment Bank, then joined Oaktree Capital’s Principal Opportunities Fund, making private equity and distressed debt-for-control investments. There he built LBO models and worked on Oaktree’s acquisition of Pierre Foods out of bankruptcy: closing underperforming plants, consolidating pricing and recapitalizing the business. The company became AdvancePierre Foods and was sold to Tyson in 2017, one of the standout investments in Oaktree’s history. Along the way he was an early investor in a fast-casual restaurant chain that is now publicly traded, building the financial model behind the raise for its second and third locations.

After a Wharton MBA, he spent six years as CEO of MeUndies, leading the venture-backed brand as revenue grew more than 100x.

Since 2020 he has run an SFR acquisitions and property management platform whose underwriting, valuation models and operations all run on systems he designed and coded, alongside advisory roles with other growth companies.

Bryan Lalezarian on LinkedIn

FAQ

Questions we hear.

What kinds of companies do you work with?

Founder- and CEO-led companies, typically $5M to $250M in revenue, with operations-heavy teams and a lot of work still running in spreadsheets and inboxes. We have built in consumer, real estate and financial services, and the patterns travel across industries.

Do we need a technical team?

No. Most of our clients do not have one. We build, document and train your existing team to run what we ship. If you do have engineers, we work alongside them.

Who owns the work?

You do. Everything runs in your accounts, under your control, and the code and data are yours.

How do you handle our data?

We work inside your systems with least-privilege access, under NDA. Your data does not leave your environment, and we never use it to train anything outside your business.

How fast will we see something real?

A Sprint ends with a working prototype in two weeks. Most first builds are in production within 30 days.

What would you build with one more great operator on the team?

A 30-minute call. We'll ask where the time goes and tell you honestly whether AI can help.

Book an intro callor email hello@hurdlerate.ai