The Revenue Intelligence Engine for Self-Storage

Better revenue decisions.
Every property. Every day.

Strevi combines operating data, machine-learning forecasting, predictive models, and revenue strategy to help automate the difficult analysis behind pricing decisions — turning complex signals into smarter action.

Request a Demo Explore the Platform

Market & operating signals
Strevi Intelligence
Strevi Recommendations

The problem

Revenue teams shouldn't have to manually assemble every decision.

Pricing decisions rarely depend on one metric. Occupancy, demand, move-in velocity, availability, market position, seasonality, forecasted conditions, unit characteristics, and business strategy all matter.

PMS reports
Spreadsheets
Competitor shops
BI dashboards
Market knowledge
Pricing systems
Institutional knowledge

Strevi brings those signals together so revenue teams can spend less time assembling the answer and more time managing strategy and exceptions.

Decisions Strevi is intended to support

Should this unit type increase or decrease in price?
Is a store incorrectly positioned against its market?
Is weakening occupancy temporary or structural?
Which properties need attention today?
Where are promotions deeper than necessary?
Which revenue opportunities have the greatest economic impact?
How should pricing strategy change as demand changes?

Revenue intelligence

From fragmented data to a revenue decision.

Strevi systematically evaluates the signals shaping property performance, applies predictive models to anticipate what comes next, and surfaces the actions most likely to support an operator's revenue objectives.

01

Smarter Decisions

Bring more relevant information into each pricing decision.

02

Greater Efficiency

Reduce the manual analysis required to identify where action is needed.

03

Better Business Outcomes

Help operators make more consistent, timely, and economically informed revenue decisions.

How Strevi works

From signals to a decision you can defend.

One path, five stages. Four of them are what Strevi does today; the last describes where the platform is headed.

01UnderstandOperating, customer, property, and market signals in one place.

02ForecastMachine-learning forecasts of demand and revenue conditions.

03RecommendPrioritized recommendations with the reasoning attached.

04DecideOperator review against strategy, rules, and constraints.

05ActApproved decisions moving toward execution. Platform direction.Direction

Strevi's current platform centers on intelligence, forecasting, recommendations, and operator review. Increasingly autonomous decisioning and execution represent the platform's direction rather than functionality available today, and will remain governed by operator-defined strategy, limits, and controls.

Explore the Platform

Strevi Intelligence

One intelligence layer for revenue performance.

Dynamic pricing is one capability inside a broader revenue-intelligence platform.

Dynamic Pricing

Available today

Combine property performance, demand signals, occupancy, market positioning, and pricing policy to support more informed asking-rate decisions.

Forecasting

Available today

Anticipate changes in demand and operating conditions so revenue teams can respond before performance appears in lagging reports.

Portfolio Intelligence

Available today

Identify where attention matters most by prioritizing properties, markets, and revenue opportunities according to performance signals and potential impact.

Competitive Intelligence

In development

Bring competitive rate, promotion, and market-positioning context into the same environment where rate decisions are reviewed.

Existing Customer Revenue

Planned

Extend revenue intelligence beyond asking rates by incorporating customer behavior and retention economics into future customer-rate decision support.

Labels state what Strevi can demonstrate today, what is actively in development, and what is planned. Strevi does not represent development or planned work as delivered functionality.

See how each capability works

Product philosophy

Powerful intelligence. Operator control.

Strevi is designed to help operators make better decisions—not remove them from the decision process. Recommendations can be evaluated within defined business rules, operational constraints, and revenue strategy.

Operator-defined strategy

Revenue strategy is set by the operator. Strevi is designed to work within it, not around it.

Configurable guardrails

Business rules and operational constraints are intended to shape what the platform can recommend.

Transparent recommendations

Recommendations show the contributing signals behind a suggested direction.

Review workflows

Recommendations are reviewed, adjusted, or declined by the operator before anything is acted on.

Auditability

The platform is being developed so revenue decisions and their review history can be traced.

Human oversight

Strevi is not an uncontrolled autonomous pricing system, and is not being built to become one.

Economic levers

Find the revenue opportunities hidden across the portfolio.

Revenue performance is the sum of several decisions that are usually managed separately. Strevi is designed to treat them as one problem.

Asking Rates

Improve new-rental pricing decisions with performance, demand, and market context in the same view.

Promotions

Identify opportunities to reduce unnecessary discounting where demand does not require it.

Occupancy

Respond earlier to changing demand instead of reacting after the month closes.

Existing Customers

Planned: extend decision support to customer-rate strategy, weighed against retention.

Portfolio Focus

Direct limited revenue-management resources toward the opportunities that matter most.

Illustrative revenue arithmetic

Set a portfolio size and an improvement assumption to see the arithmetic. Both inputs are yours, not ours.

Illustrative only

Portfolio annual revenue$100M

Illustrative improvement1.00%

Potential annual revenue impact

$1,000,000

Illustrative example only. Actual results will vary. This figure is the product of the two inputs above — it is arithmetic, not a statement of results produced by Strevi.

Purpose-built for self-storage

Built around how self-storage revenue actually works.

Storage revenue is decided at the unit type, inside a local market, under seasonal demand. Strevi is designed around those concepts rather than adapting a generic analytics tool to them.

Inventory

Unit types
Unit attributes
Occupancy
Availability

Demand

Move-in velocity
Move-out behavior
Seasonality

Price

Asking rates
Promotions
Competitor rates
Market position

Portfolio

Customer tenure
Property differences
Portfolio strategy

Responsible design

Designed for responsible revenue decision-making.

Revenue decisions carry commercial consequences. The platform is being built so those decisions stay reviewable, attributable, and inside operator-defined policy.

Role-based access

Access is scoped to the roles that participate in revenue decisions.

Multi-tenant architecture

Operator data is separated by tenant.

Recommendation review workflows

Recommendations move through operator review rather than straight to execution.

These describe how the platform is being designed. Strevi makes no claim of third-party security certification, audit status, or service-level commitment on this site. Security and compliance documentation is provided directly during evaluation.

Nicole Brannen, Founder and CEO of Strevi

Company

Built from firsthand experience building revenue management at scale.

Nicole Brannen

Founder & CEO

Nicole Brannen is the Founder & CEO of Strevi, with more than two decades of experience building teams, products, and business capabilities — and approximately a decade focused on revenue management within self-storage, across Extra Space Storage, Entrata, and National Storage Affiliates.

Prior employers are referenced as professional background only. They are not Strevi customers, partners, investors, or endorsers.

See Strevi against your portfolio.

Tell us a little about your portfolio and revenue-management priorities, and we will follow up to arrange a demo.

Request a Demo

Interested in becoming an early Strevi design partner? Start the conversation.

Request a Demo

Platform

The intelligence behind better revenue decisions.

Strevi combines operating performance, machine-learning forecasting, predictive models, market signals, and operator-defined strategy to evaluate revenue opportunities and support dynamic pricing decisions across the portfolio.

The revenue intelligence engine

One engine. Thousands of revenue decisions.

Every property creates a constant stream of pricing decisions. Strevi is designed to evaluate those decisions systematically — connecting property performance, demand, availability, market position, forecasts, and strategy inside a single revenue intelligence engine.

Inputs

Operating Performance
Demand Signals
Market Position
Availability
Predictive Models
Operator Strategy

Strevi Revenue Intelligence Engine

Outputs

Forecasts
Recommendations
Dynamic Pricing Decisions
Prioritized Opportunities

Strevi Intelligence

One intelligence layer for revenue performance.

Dynamic pricing is one capability inside a broader revenue-intelligence platform. Each area below is labeled with what Strevi can show today.

Dynamic Pricing

Available today

Combine property performance, demand signals, occupancy, market positioning, and pricing policy to support more informed asking-rate decisions.

Strevi evaluates asking rates at the unit-type level, where storage revenue decisions are actually made, and presents the reasoning behind each recommended direction rather than a single unexplained number.

Unit-type level rate evaluation
Occupancy and availability context
Market position inputs
Policy-aware recommendations

Forecasting

Available today

Anticipate changes in demand and operating conditions so revenue teams can respond before performance appears in lagging reports.

Strevi applies machine learning to historical performance and current conditions to produce a forward view of occupancy and demand at the property and unit-type level, so a developing trend is visible while there is still time to act on it.

Machine-learning demand forecasting
Property and unit-type granularity
Seasonality-aware baselines
Trend deterioration signals

Portfolio Intelligence

Available today

Identify where attention matters most by prioritizing properties, markets, and revenue opportunities according to performance signals and potential impact.

Revenue management capacity is finite. Strevi ranks where intervention is likely to matter rather than asking a team to review every property with equal effort.

Opportunity prioritization
Property and market ranking
Estimated economic impact
Portfolio-level visibility

Competitive Intelligence

In development

Bring competitive rate, promotion, and market-positioning context into the same environment where rate decisions are reviewed.

Market context belongs next to the decision rather than in a separate shopping spreadsheet. This capability is in development: Strevi does not currently offer an integrated live competitive data feed, and no competitive data source is claimed here.

Competitive rate and promotion context
Market-level comparison
Context carried into the decision
In development

Existing Customer Revenue

Planned

Extend revenue intelligence beyond asking rates by incorporating customer behavior and retention economics into future customer-rate decision support.

Existing-customer revenue is a planned direction for the Strevi platform. It is not available today and is not represented here as an available capability.

Customer tenure context
Retention economics
Rate-change decision support
Planned

Labels state what Strevi can demonstrate today, what is actively in development, and what is planned. Strevi does not represent development or planned work as delivered functionality.

01

Predict what matters before it appears in the results.

Sophisticated machine-learning and predictive models help Strevi anticipate changing demand and operating conditions — demand, occupancy pressure, move-in and move-out conditions, and the forward-looking context around a pricing decision.

Forecasts become an input into the revenue decision, not another report a team has to interpret manually.

02

Dynamic pricing powered by revenue intelligence.

Strevi evaluates property performance, demand signals, forecasts, availability, market position, pricing strategy, and operator-defined constraints to support more intelligent asking-rate decisions.

Rather than relying on a single metric or a static rule, pricing decisions can incorporate the broader economic context of the property and its market.

03

Turn analysis into action.

Strevi surfaces prioritized revenue recommendations with the contributing signals and estimated economic opportunity needed to evaluate each decision in context.

Revenue teams can focus first on the decisions most likely to matter, instead of searching across properties, reports, and dashboards for opportunities.

04

Automate the difficult parts of revenue management.

The goal of automation is not to remove strategy from the operator. It is to reduce the manual work required to repeatedly evaluate complex decisions.

Strevi is being built to automate more of the analysis, forecasting, evaluation, and recommendation process while keeping objectives, limits, policies, and approval authority with the operator.

The Strevi framework

The next generation of revenue management shouldn't stop at telling operators what happened.

It should understand what is happening, forecast what is likely to happen next, determine the best available action, and — where an operator chooses — execute approved decisions within clearly defined guardrails.

01

Understand

Connect the signals that matter.

Bring together operating, customer, property, and market signals in one decision environment.

02

Forecast

Anticipate what comes next.

Apply historical performance, current conditions, and machine learning to forecast likely demand and revenue conditions.

03

Recommend

Identify the best available action.

Evaluate signals, constraints, and expected outcomes to surface the most appropriate revenue decision, with its reasoning attached.

04

Decide

Apply strategy, economics, and guardrails.

Combine recommendations with operator-defined objectives, rules, and business constraints. Recommendations are reviewed, adjusted, or declined by the operator.

05

Act

Move toward controlled autonomous execution.

Enable approved decisions to progress toward execution within clearly defined operator controls.

Strevi's current platform centers on intelligence, forecasting, recommendations, and operator review. Increasingly autonomous decisioning and execution represent the platform's direction rather than functionality available today, and will remain governed by operator-defined strategy, limits, and controls.

The interface

Decision-ready views, not another dashboard.

The views below are illustrative product views rendered from synthetic data. They show how Strevi presents portfolio performance, property detail, and recommendation review.

Portfolio overview

Portfolio, market, property, and unit-level performance in one view, with prioritized opportunities ranked by estimated impact.

Portfolio
Properties
Recommendations
Markets

Illustrative product view · Synthetic data

Revenue opportunity

$1.42M

+$104K vs. last week

Portfolio occupancy

91.4%

-0.6 pts vs. prior month

Market position

+3.1%

12 markets above index

Properties flagged

18

6 new since Monday

Portfolio occupancy & Strevi Forecast

Trailing 6 mo · 3 mo view

Actual
Market
Strevi Forecast

Strevi RecommendationPrioritized · Est. impact

Cedar Hollow Storage
$74K
Reduce promotional depth on climate units
Northgate Self Storage
$61K
Increase asking rate toward market band
Harbor Point Storage
$48K
Shorten promotion length on 10x20

Properties requiring attention

18 of 142

Illustrative list of properties flagged for revenue attention, using fictional property names and values.
Property	Occupancy	Market position	Signal	Est. impact
Cedar Hollow StorageFictional Market A	94.2%	+6.4% vs. market	Move-in velocity slowing	$74K
Northgate Self StorageFictional Market B	88.1%	-4.2% vs. market	Priced below market band	$61K
Harbor Point StorageFictional Market C	96.7%	+1.8% vs. market	Promotion deeper than required	$48K
Willow Creek StorageFictional Market A	83.5%	+2.9% vs. market	Sustained occupancy decline	$39K
Stonebridge StorageFictional Market D	90.9%	-1.1% vs. market	Seasonal demand inflection	$27K

Illustrative product view rendered from synthetic data. Not a screenshot of a customer environment.

Property detail

Unit types, asking rates, occupancy, market position, and demand trend in one place — with the rationale behind a recommended direction stated alongside it.

Portfolio
Properties
Recommendations
Markets

Illustrative product view · Synthetic data

Fictional Market B

Northgate Self Storage

Occupancy: 88.1%
Market position: -4.2%
Move-in velocity: Stable

Illustrative unit-type detail for a fictional property, including asking rates, occupancy, market delta, and a recommended direction.
Unit type	Occupancy	Asking rate	vs. market	Strevi Recommendation
10x10 Climate100 sq ft	97.1%	$164	+2.4%	Increase
10x10 Drive-Up100 sq ft	92.6%	$138	-3.8%	Increase
10x20 Drive-Up200 sq ft	81.3%	$219	+7.1%	Reduce
5x10 Climate50 sq ft	94.8%	$96	+0.6%	Hold

Demand trend

Trailing 12 weeks

What Strevi observed

Asking rates sit below the market band for two unit types.
Occupancy is holding above target with steady move-in velocity.
10x20 drive-up is priced above market with softening demand.

Strevi Recommendation

Raise 10x10 drive-up toward the market band and reduce promotional depth on 10x20.

Illustrative product view rendered from synthetic data. Not a screenshot of a customer environment.

Recommendation review

Every recommendation arrives with its expected direction, its contributing signals, a review status, and an operator action — approve, adjust, or decline.

Portfolio
Properties
Recommendations
Markets

Illustrative product view · Synthetic data

Recommendation rec-4821

Northgate Self Storage · 10x10 Drive-Up

Awaiting review

Strevi Recommendation

Increase asking rate

Current: $138
Recommended: $146

Within the operator’s configured pricing policy for this market. Requires review before any change is applied.

ApproveAdjustDecline

Contributing Signals

Occupancy
92.6%, above target band
Market position
3.8% below market index
Move-in velocity
Stable over trailing 30 days
Availability
7 units available
Seasonality
Approaching shoulder season

Signal detail shown for illustration. Strevi does not publish its internal model logic or pricing thresholds.

Illustrative product view rendered from synthetic data. Not a screenshot of a customer environment.

Product philosophy

Powerful intelligence. Operator control.

Strevi is designed to help operators make better decisions—not remove them from the decision process. Recommendations can be evaluated within defined business rules, operational constraints, and revenue strategy.

Operator-defined strategy

Revenue strategy is set by the operator. Strevi is designed to work within it, not around it.

Configurable guardrails

Business rules and operational constraints are intended to shape what the platform can recommend.

Transparent recommendations

Recommendations show the contributing signals behind a suggested direction.

Concept · Future direction

Expressing revenue intent directly to the platform.

Strevi is exploring a natural-language operator experience that would allow revenue teams to communicate business intent to the platform. This is a design concept under exploration, not a live product feature.

Illustrative intent examples

“Prioritize occupancy in this market for the next 30 days.”
“Show me stores where asking rates are above market and move-in velocity is weakening.”
“Keep recommended rate changes within this market's approved pricing policy.”

Concept language shown for illustration only. It does not represent functionality available in the Strevi platform today.

Walk through the platform with us.

Tell us a little about your portfolio and revenue-management priorities, and we will follow up to arrange a demo.

Request a Demo

Interested in becoming an early Strevi design partner? Start the conversation.

Request a Demo

Solutions

Revenue intelligence for the people accountable for revenue.

Revenue performance is decided by different people asking different questions of the same portfolio. Strevi is designed to serve all three views without splintering into three products.

Revenue Management Teams

Spend less time assembling the decision.

Strevi helps revenue teams evaluate portfolio performance, forecasts, pricing conditions, and market signals so analysts can focus on strategy, exceptions, and the decisions requiring human judgment.

Less manual analysis

Faster opportunity identification

Prioritized recommendations

More consistent decisions

More predictive and less reactive revenue management

Executive Leadership

Turn revenue management into a more scalable operating capability.

Strevi gives leaders greater visibility into where revenue opportunity exists, how decisions are being made, and where strategy should change — while creating a more systematic decision process across the portfolio.

Scalability

Visibility

Governance

Consistency

Estimated opportunity

Business outcomes

Multi-Property Operators

Sophisticated revenue intelligence without building the entire capability internally.

Advanced revenue management has historically required specialized teams, custom analytics, forecasting expertise, data science, and significant technology investment. Strevi is being built to deliver more of that capability through software.

Bring your portfolio to the conversation.

Tell us a little about your portfolio and revenue-management priorities, and we will follow up to arrange a demo.

Request a Demo

Interested in becoming an early Strevi design partner? Start the conversation.

Request a Demo

Why Strevi

Revenue management doesn't need another dashboard.

It needs an intelligence engine capable of understanding performance, anticipating change, evaluating alternatives, and helping operators determine where to act.

01

Predictive, not just reactive

Predictive models anticipate changing conditions instead of waiting for lagging performance to reveal them.

02

Decisions, not dashboards

Turn data into prioritized actions rather than requiring teams to interpret another set of reports.

03

Sophisticated models, operationally usable

Translate machine learning, forecasting, and revenue science into decisions revenue teams can actually use.

04

Automation with control

Automate more of the analytical workload without surrendering operator-defined strategy or guardrails.

05

Built for self-storage

Model the economics, property differences, unit characteristics, demand patterns, and revenue workflows specific to storage.

Strevi describes its own approach rather than ranking itself against named competitors. Evaluate it against your portfolio and your revenue-management process.

Responsible design

Designed for responsible revenue decision-making.

Revenue decisions carry commercial consequences. The platform is being built so those decisions stay reviewable, attributable, and inside operator-defined policy.

Role-based access

Access is scoped to the roles that participate in revenue decisions.

Multi-tenant architecture

Operator data is separated by tenant.

Recommendation review workflows

Recommendations move through operator review rather than straight to execution.

Auditability and data lineage

Strevi is being developed so the inputs behind a recommendation can be traced.

Configurable policies

Pricing policy and operational constraints are intended to be operator-configured.

Controlled integrations

Data connections are designed to be explicit, scoped, and operator-approved.

These describe how the platform is being designed. Strevi makes no claim of third-party security certification, audit status, or service-level commitment on this site. Security and compliance documentation is provided directly during evaluation.

Put Strevi in front of your revenue team.

Tell us a little about your portfolio and revenue-management priorities, and we will follow up to arrange a demo.

Request a Demo

Interested in becoming an early Strevi design partner? Start the conversation.

Request a Demo

Company

We've lived the problems Strevi is being built to solve.

Strevi is being built by leaders who have run revenue management inside major self-storage operators, helped build the revenue-management and analytics software behind those decisions, and worked at the intersection of data, machine learning, and pricing strategy.

Why Strevi exists

Sophisticated revenue management shouldn't require every operator to build it from scratch.

The largest operators can invest in specialized revenue teams, custom analytics, forecasting models, data science, and proprietary technology. Many other operators face the same pricing and revenue decisions without the same infrastructure.

Strevi is being built to close that capability gap — putting sophisticated forecasting, machine learning, decision intelligence, and dynamic pricing into software more operators can use.

Founder

Nicole Brannen

Founder & CEO

Nicole has spent years both building sophisticated revenue-management capabilities inside operators and helping build the software, analytics, teams, and decision systems needed to support them.

Nicole Brannen is the Founder & CEO of Strevi, with more than two decades building and leading teams, organizations, products, and business capabilities — and approximately a decade focused on revenue management and performance within the self-storage industry.

Her career spans leadership roles at Extra Space Storage, Entrata, and National Storage Affiliates, working at the intersection of revenue management, pricing, forecasting, analytics, data science, machine learning, strategic investments, and portfolio performance.

Nicole has worked both sides of the problem: leading revenue strategy inside large self-storage operators, and helping build the revenue-management, analytics, and data products that support those decisions across self-storage and multifamily. That combination gave her firsthand perspective on how predictive models and pricing logic have to translate from analysis into software an operating team can actually run.

She has also advised organizations on how to build stronger revenue-management teams, systems, analytics, pricing strategies, and decision capabilities.

Across those experiences the same challenge appeared repeatedly: sophisticated revenue management creates significant value, but building it internally requires specialized expertise, connected data, predictive models, technology, and meaningful organizational investment.

Nicole founded Strevi to make more of that sophistication available through software — a revenue intelligence engine designed to automate the difficult analysis behind pricing decisions, improve decision quality, and support better business outcomes without requiring every operator to build the teams, models, and infrastructure internally.

Prior employers are referenced as professional background only. They are not Strevi customers, partners, investors, or endorsers.

20+ Years

Building and leading organizations, teams & products

A Decade in Self-Storage

Revenue management, pricing & portfolio performance

Operator + Software

Built revenue capabilities from both sides of the platform

Data + AI

Forecasting, analytics, machine learning & decision intelligence

The Strevi framework

The next generation of revenue management shouldn't stop at telling operators what happened.

It should understand what is happening, forecast what is likely to happen next, determine the best available action, and — where an operator chooses — execute approved decisions within clearly defined guardrails.

01

Understand

Connect the signals that matter.

Bring together operating, customer, property, and market signals in one decision environment.

02

Forecast

Anticipate what comes next.

Apply historical performance, current conditions, and machine learning to forecast likely demand and revenue conditions.

03

Recommend

Identify the best available action.

Evaluate signals, constraints, and expected outcomes to surface the most appropriate revenue decision, with its reasoning attached.

04

Decide

Apply strategy, economics, and guardrails.

Combine recommendations with operator-defined objectives, rules, and business constraints. Recommendations are reviewed, adjusted, or declined by the operator.

05

Act

Move toward controlled autonomous execution.

Enable approved decisions to progress toward execution within clearly defined operator controls.

Strevi's current platform centers on intelligence, forecasting, recommendations, and operator review. Increasingly autonomous decisioning and execution represent the platform's direction rather than functionality available today, and will remain governed by operator-defined strategy, limits, and controls.

Where AI fits

AI is valuable when it improves a decision — not when it simply produces an answer.

A revenue decision is not a text-generation problem. Strevi is being designed to bring several inputs together so a recommendation reflects the operator's business rather than a model's best guess — measured by better decisions and better business outcomes.

What a recommendation draws on

Operating data
Property performance
Market signals
Forecasting
Machine learning
Revenue-management logic
Operator strategy
AI-driven reasoning and interaction — in development

Our purpose

Better decisions are better for the whole business.

Better pricing decisions are not simply about charging more. They are about understanding demand, positioning inventory appropriately, responding to changing conditions, and making economically rational decisions.

Better-run revenue management can create healthier operators, more efficient businesses, and more consistent customer experiences.

See what Strevi could uncover across your portfolio.

Tell us a little about your portfolio and revenue-management priorities, and we will follow up to arrange a demo.

Request a Demo Explore the Platform

Interested in becoming an early Strevi design partner? Start the conversation.

Request a Demo

See what a revenue intelligence engine could change across your portfolio.

Tell us about your portfolio and revenue-management priorities. We’ll explore where forecasting, machine learning, dynamic pricing, and more automated decision support could help your team operate more intelligently.

What to expect

A working walkthrough of how Strevi is designed to support revenue decisions.
A conversation about your portfolio, markets, and current revenue-management process.
A candid view of what is built today and what is still being developed.

Interested in becoming an early Strevi design partner? Mention it in your message and we will cover it in the first conversation.

Request a Demo

Strevi LLC

Privacy

This page is a routing placeholder. A privacy policy has not been drafted or reviewed, and none is published here.

No analytics provider is installed on this site, and no visitor tracking data is transmitted to a third party.
The demo request form sends what you submit — your name, work email, company, role, portfolio size, and optional message — to Strevi by email, together with the page you submitted from and any campaign parameters in the link you followed. It is not used to subscribe you to marketing email.
No delivery service is configured by default. When none is configured, submissions are not transmitted and the form says so rather than confirming a request that was never sent.
A privacy policy must be prepared and reviewed by counsel before this site is published publicly.

Request a Demo

Strevi LLC

Terms

This page is a routing placeholder. No terms of use or service agreement is published here.

Nothing on this site constitutes a contract, offer, warranty, or service-level commitment.
Product descriptions on this site describe intended design and development direction rather than guaranteed functionality.
Terms of use must be prepared and reviewed by counsel before this site is published publicly.

Better revenue decisions. Every property. Every day.

Revenue teams shouldn't have to manually assemble every decision.

Decisions Strevi is intended to support

From fragmented data to a revenue decision.

Smarter Decisions

Greater Efficiency

Better Business Outcomes

From signals to a decision you can defend.

One intelligence layer for revenue performance.

Dynamic Pricing

Forecasting

Portfolio Intelligence

Competitive Intelligence

Existing Customer Revenue

Powerful intelligence. Operator control.

Operator-defined strategy

Configurable guardrails

Transparent recommendations

Review workflows

Auditability

Human oversight

Find the revenue opportunities hidden across the portfolio.

Asking Rates

Promotions

Occupancy

Existing Customers

Portfolio Focus

Illustrative revenue arithmetic

Built around how self-storage revenue actually works.

Inventory

Demand

Price

Portfolio

Designed for responsible revenue decision-making.

Role-based access

Multi-tenant architecture

Recommendation review workflows

Built from firsthand experience building revenue management at scale.

Nicole Brannen

See Strevi against your portfolio.

The intelligence behind better revenue decisions.

One engine. Thousands of revenue decisions.

One intelligence layer for revenue performance.

Dynamic Pricing

Forecasting

Portfolio Intelligence

Competitive Intelligence

Existing Customer Revenue

How the engine is applied

Predict what matters before it appears in the results.

Dynamic pricing powered by revenue intelligence.

Turn analysis into action.

Automate the difficult parts of revenue management.

The next generation of revenue management shouldn't stop at telling operators what happened.

Understand

Forecast

Recommend

Decide

Act

Decision-ready views, not another dashboard.

Portfolio overview

Property detail

Recommendation review

Powerful intelligence. Operator control.

Operator-defined strategy

Configurable guardrails

Transparent recommendations

Expressing revenue intent directly to the platform.

Walk through the platform with us.

Revenue intelligence for the people accountable for revenue.

Spend less time assembling the decision.

Turn revenue management into a more scalable operating capability.

Sophisticated revenue intelligence without building the entire capability internally.

Bring your portfolio to the conversation.

Revenue management doesn't need another dashboard.

What distinguishes Strevi

Predictive, not just reactive

Decisions, not dashboards

Sophisticated models, operationally usable

Automation with control

Better revenue decisions.
Every property. Every day.