GITNUXSOFTWARE ADVICE
Gambling LotteriesTop 10 Best AI Betting Software of 2026
Ranking top ai betting software for 2026 with trading API notes and picks. Includes PredictZ, Sports Insights, RebelBetting.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
PredictZ is the best fit for a trading team that needs automated AI picks delivered through an integration-ready betting API, while OddsJam is the cheapest practical entry if you want real-time EV scans feeding staking rules, and Betegy works best when you run a desk and need EV ranking with controlled Kelly staking across many markets.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
PredictZ
Prediction output mapping that converts model signals into execution-ready odds-format inputs for automated bet logic.
Built for fits when a trading team needs automated AI picks delivered through an integration-ready betting API..
Sports Insights
Editor pickOngoing performance monitoring tied to forecast outputs helps teams review model drift and decision accuracy over time.
Built for fits when forecasting teams need monitored model iteration and market-aligned decision workflows..
RebelBetting
Editor pickThresholded execution logic that converts model confidence and line movement into governed bet placement rules.
Built for fits when teams need automated AI picks with controlled staking and consistent odds mapping..
Related reading
Comparison Table
PredictZ
vertical specialistAlgorithmic football prediction tool that generates match outcome forecasts using historical data and statistical modeling.
Prediction output mapping that converts model signals into execution-ready odds-format inputs for automated bet logic.
PredictZ is designed for operational use where model signals must turn into actionable bets with repeatable configuration and repeatable results across markets. The strongest fit signals are the model evaluation workflow and the execution-friendly integration pathway that can feed trading or bet placement layers. Automation depth matters here because prediction generation and downstream decision rules are meant to run without manual relabeling each cycle. Teams evaluating betting-model tooling should check that the odds mapping supports the formats they trade and that the update frequency matches odds scrape latency.
A tradeoff appears in governance and operations because tighter control over staking math and drawdown limits requires careful configuration discipline. PredictZ is most useful when an existing line shopping aggregator or odds feed pushes updates on a fixed cadence and the model must react with predictable throughput. It is also a stronger choice for setups that already have a defined market universe and want consistent closing-line regression style evaluation rather than ad hoc tuning.
- +API-first workflow for prediction-to-execution chaining
- +Backtesting and calibration loops for measurable model change
- +Configurable automation for pre-match and in-play decisioning
- +Model output formatting supports odds integration needs
- –Staking and risk limits require careful configuration discipline
- –Ops setup effort rises with high market coverage
- –In-play pipelines can be sensitive to odds update cadence
- –Governance depth is less turnkey than pure prediction dashboards
Quant trading engineers
Automate bet selection from AI outputs
Fewer manual steps in trading
Betting operations teams
Run daily pre-match model cycles
More consistent model reporting
Show 2 more scenarios
In-play model maintainers
Control decision logic under odds movement
Lower variance from stale odds
Trigger in-play updates with configuration that respects odds scrape latency and decision thresholds.
Data science leads
Measure ROI per market over time
Faster model tuning loops
Track historical performance to guide model calibration and feature pipeline iteration cycles.
Best for: Fits when a trading team needs automated AI picks delivered through an integration-ready betting API.
More related reading
Sports Insights
vertical specialistSports betting analytics platform providing real-time odds, line movement data, and predictive indicators.
Ongoing performance monitoring tied to forecast outputs helps teams review model drift and decision accuracy over time.
Sports Insights provides prediction outputs paired with analytics meant for backtesting and ongoing performance monitoring, so model changes can be judged against prior runs. The platform also supports decision workflows that compare forecasts against market odds, which reduces manual reconciliation during line updates.
A tradeoff appears in integration effort, because full automation depends on how data feeds and model outputs are wired into a team’s existing betting stack. Sports Insights works best when a workflow owner can define evaluation cadence and ensure input freshness for both pre-match and in-play decisions.
- +Model monitoring supports iterative refinement against observed outcomes.
- +Market comparison workflows reduce manual alignment during odds changes.
- +Pre-match and in-play outputs fit end-to-end decision processes.
- +Backtesting-oriented evaluation helps quantify changes before rollout.
- –Automation depth depends on the quality of the team’s data wiring.
- –Decision governance requires clear internal ownership of evaluation cadence.
- –Odds format and event mapping still need careful operational validation.
- –API and sandbox coverage are not sufficient for rapid trial automation.
Betting analytics teams
Iterate pre-match models with monitoring
Higher stable ROI per market
In-play trading desks
Route predictions into live decisions
Faster in-play execution
Show 1 more scenario
Quant model governance owners
Standardize evaluation and rollout cadence
Lower model drift risk
Evaluation artifacts and performance history reduce ad hoc changes to production models.
Best for: Fits when forecasting teams need monitored model iteration and market-aligned decision workflows.
RebelBetting
vertical specialistValue betting software that identifies mispriced odds across bookmakers using statistical models.
Thresholded execution logic that converts model confidence and line movement into governed bet placement rules.
RebelBetting is best evaluated as an end-to-end decision and execution layer rather than a standalone model notebook. Model backtesting and calibration loops can feed operational rules, then the system can translate predictions into actionable selections with streak-level and outcome tracking. Odds formatting and timing matter because it must reconcile bookmaker feeds into consistent selection objects before it can compute edges and staking.
The main tradeoff is that setup discipline is required to keep model outputs aligned with the odds and market mapping used at runtime. RebelBetting fits scenarios where a team runs frequent model refreshes and needs repeatable thresholds for in-play or pre-match actions without manual spreadsheet edits.
- +Rule-based staking limits reduce Kelly fraction overexposure
- +Model backtesting loops inform operational thresholds
- +Odds ingestion supports consistent selection normalization
- +Automation reduces manual selection-to-bet handoffs
- –Requires careful odds format conversion to avoid stale mappings
- –Governance checks can be lighter than enterprise RBAC expectations
- –Odds update timing can impact edge calculations for fast markets
Betting operations teams
Automate model outputs to live bets
Fewer manual selection errors
Data science teams
Close the loop with backtesting
More consistent model deployment
Show 1 more scenario
Trading-focused bettors
Respond to odds movement
Reduced line shopping misses
Recalculates expected value inputs when odds update and then applies bet selection gates.
Best for: Fits when teams need automated AI picks with controlled staking and consistent odds mapping.
More related reading
Betegy
vertical specialistAI-powered sports betting predictions and analytics platform covering football leagues globally.
Kelly fraction staking with a drawdown-aware guardrail logic helps cap bankroll downside during model drift.
Betegy is an AI betting software solution focused on decisioning workflows that connect model outputs to market offers. It is designed to support model training and evaluation loops alongside bet sizing, with mechanisms aimed at minimizing bad-stake risk via Kelly fraction controls.
Betegy also targets odds handling needs like odds format conversion and expected value calculation so automated picks can be compared consistently across bookmakers. It pairs these capabilities with operational controls for running the same process across multiple markets and seasons.
- +Kelly fraction controls reduce oversized exposure from volatile model recommendations
- +Expected value calculator helps rank picks using a consistent value framework
- +Odds format conversion supports comparing similar markets across different odds feeds
- +Model backtesting supports iteration before production deployment
- –Automation requires careful configuration of inputs and mapping to each market type
- –Odds scrape latency handling is a critical dependency for fast moving lines
- –In-play betting requires separate workflow tuning versus pre-match models
- –Extensibility depends on the available API surface for external trading execution
Best for: Fits when a betting desk needs automated EV ranking and controlled Kelly staking across many markets.
ZCode System
vertical specialistAutomated sports betting prediction system using statistical algorithms and trend analysis.
Versioned model workflows that keep test configurations and decision rules isolated from live runs.
ZCode System delivers AI betting software that turns sportsbook and exchange odds feeds into model-driven betting workflows. The solution focuses on automation around model scoring, selection rules, and bet-ready outputs, with a workflow layer that supports repeated pre-match and in-play runs.
It also targets operational control such as environment separation for testing versus production and centralized management of model versions. Integration depth is oriented toward ingestion, normalization, and output endpoints rather than manual spreadsheets.
- +Workflow automation converts model scores into bet-ready decisions.
- +Model version separation helps keep test results from leaking into production.
- +Odds ingestion supports feed normalization for consistent downstream logic.
- +Rules-based selection supports repeated runs across multiple markets.
- –Integration depth depends on custom mapping for each odds source format.
- –Feature pipeline coverage is thin for advanced calibration and drift monitoring.
- –Governance controls for roles and audit trails are not explicit in typical setups.
- –In-play latency handling is not stated with measurable throughput targets.
Best for: Fits when a trading desk needs automated scoring-to-decision workflows with controlled test environments.
Leans.ai
vertical specialistAI and machine learning platform that generates sports betting predictions by simulating thousands of game outcomes.
Pick pipeline automation built for operational decisioning, rather than analysis-only outputs or read-only dashboards.
Leans.ai targets teams that want prediction-led betting picks with an integration-first workflow. Its core capabilities center on building lean pick pipelines from model inputs and operationalizing them into bet-ready decisions.
The differentiator is how it supports automation and extensibility around pick generation, not just dashboard-style insights. For tighter pick discipline, it fits setups that can wire its outputs into trading loops with closing line and expected value checks.
- +Automation-oriented pick workflow that reduces manual handoff steps
- +Extensibility focus makes it easier to connect pick logic to external systems
- +Supports model-input to bet-decision pipelines with clear operational stages
- +Useful fit for closing-line discipline when paired with EV validation
- –Automation depth can require engineering time to wire correctly
- –Native governance controls for multi-user betting operations appear limited
- –In-play model execution coverage is less explicit than pre-match workflows
- –Odds format conversion and line normalization require external handling
Best for: Fits when teams need automated pick pipelines and can integrate bet outputs into their own line and EV validation.
More related reading
OddsJam
SMBAlgorithmic betting software that scans sportsbook odds to identify positive expected value betting opportunities in real time.
Odds deviation thresholding that converts closing line behavior into actionable pre-match signals.
OddsJam focuses on turning sportsbook line movement into model-backed betting picks, with an emphasis on odds deviation and calibration against market behavior. The workflow centers on automated monitoring of price changes and targeted signals for pre-match decisioning.
It also supports stake sizing approaches tied to expected value thinking, including Kelly fraction style constraints for drawdown control. Integration depth is oriented around connecting trading-style data and outputs into external tools for repeatable bet execution.
- +Odds deviation signals map model edges to specific line shifts
- +Expected value driven pick workflows reduce guesswork per market
- +In-play and pre-match views support consistent model decisioning
- +Automation friendly outputs help feed external bet execution tools
- –Works best with disciplined bankroll rules and stake limits
- –Setup for data capture and scraping workflows can be time consuming
- –Line movement signals still require manual confirmation in edge cases
- –Output customization can lag for niche markets and formats
Best for: Fits when a team needs repeatable, line-movement driven AI picks feeding external automation and staking rules.
Forebet
vertical specialistMathematical football prediction service that uses statistical models to forecast match outcomes across global soccer leagues.
Forebet’s recommendation logic applies consistent selection criteria to its pre-match predictions.
Forebet combines football match predictions with automation-oriented workflows for turning forecast signals into betting decision support. The tool focuses on pre-match modeling outputs, forecast horizons, and condition-based recommendation logic rather than only static tips.
It also provides historical review views that help test whether predicted edges translated into returns using back-checkable result perspectives. Forebet’s distinct angle is workflow guidance around pick selection and disciplined tracking instead of a general-purpose prediction API.
- +Pre-match prediction outputs are presented with actionable selection filters
- +Historical result views support manual edge review against past recommendations
- +Recommendation logic helps standardize pick selection across repeated runs
- +Clear separation between forecast outputs and decision steps reduces tip sprawl
- –Prediction coverage is strongest for football and weaker for other markets
- –API and automation depth for trading-grade line movement is limited
- –Backtesting remains more checklist-based than full model pipeline evaluation
- –Odds-format conversion and staking math are not exposed as programmable primitives
Best for: Fits when a football-focused team needs repeatable pre-match pick workflows without building a full betting engine.
More related reading
Genius Sports
enterpriseSports data, technology, and integrity services with AI-powered betting and media products.
Event-to-market identifier mapping and update orchestration that keeps model state aligned with live feeds across the event lifecycle.
Genius Sports delivers AI betting infrastructure built around sportsbook and media data partnerships, with APIs used for odds-related and event lifecycle workflows. Core capabilities include model-ready sports data feeds, odds and event mapping workflows, and operational tooling for feeding predictions into automated betting decisioning.
Integration depth shows up in how Genius Sports structures event entities, market identifiers, and updates so downstream models can keep their state synchronized. Automation depends on API-driven provisioning and repeatable mappings between provider data and betting market formats.
- +Strong event and market entity mapping for model-to-betting alignment
- +API-driven update flows support continuous prediction refresh
- +Data normalization reduces manual odds format conversion work
- +Operational controls fit multi-team model deployment and iteration
- –More integration effort is needed to match sportsbook-specific market granularity
- –Automation relies on disciplined configuration of mappings and identifiers
- –In-play feature wiring can require custom feature engineering per sport
- –Throughput tuning is needed to handle odds update bursts without lag
Best for: Fits when model teams need frequent event and odds updates with tight entity mapping for automated bet decisions.
Action Network
SMBSports betting analytics and content platform with predictive metrics and odds comparison.
Editorial betting picks paired with odds context for human-in-the-loop workflows and downstream affiliate conversion.
Action Network is an editorial and data brand that publishes betting content and odds coverage rather than serving as an AI betting engine for automated wager placement. Its distinct asset is the combination of modeled pick narratives, odds sources, and betting-market commentary that teams can reference while building their own staking logic.
Action Network also supports affiliate distribution, which changes how prediction and odds data flows into real-world decision making. For automation-first teams, the key question is how much of that workflow can be integrated through available syndication or partner interfaces.
- +Odds-heavy editorial workflows for pre-match decision context
- +Consistent publication cadence around matchups and lines
- +Clear separation between analysis content and wager execution logic
- +Affiliate distribution path for converting recommendations into referrals
- –Not built as an API-first prediction market API service
- –Limited evidence of programmatic closing line value data exports
- –Automation requires custom scraping or partner integration work
- –Governance controls for multi-user model operations are not product-native
Best for: Fits when betting teams want odds-informed analysis and partner content, not full AI wager execution.
Conclusion
After evaluating 10 gambling lotteries, PredictZ stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right ai betting software
This guide covers AI betting software built to convert model signals into wagering decisions, including PredictZ, Sports Insights, RebelBetting, Betegy, ZCode System, Leans.ai, OddsJam, Forebet, Genius Sports, and Action Network.
Across these tools, the biggest differences show up in prediction-to-execution chaining, the handling of odds changes through mapping and thresholds, and how much automation and governance the workflow supports. Readers will also see how backtesting loops, drawdown guardrails, and event-to-market identifier mapping affect operational reliability when lines move.
AI betting software that converts model signals into automated wagers via odds mapping, EV logic, and execution controls
AI betting software takes outputs from forecasting or scoring models and turns them into bet-ready picks with odds-aligned inputs, EV ranking, and governed execution steps. PredictZ emphasizes a prediction output mapping layer that converts model signals into execution-ready odds-format inputs for automated bet logic.
Other platforms lean into decision governance and monitoring rather than only producing predictions, such as Sports Insights with performance monitoring tied to forecast outputs and RebelBetting with thresholded execution logic that applies governed bet placement rules. Tools also vary in how they manage model change control through backtesting and calibration loops, how they reduce exposure with Kelly fraction staking and drawdown-aware guardrails, and how they adapt to market updates through event-to-market identifier mapping or odds deviation thresholding.
Integration and automation features that determine bet execution reliability
AI betting software has to turn forecasting outputs into odds-format inputs that staking and execution steps can consume without manual fixes. PredictZ is built around a prediction output mapping layer that converts model signals into execution-ready odds-format inputs for automated bet logic.
In practice, operational wins come from how systems handle model change control, odds volatility, and event-to-market alignment. Betegy adds a drawdown-aware guardrail around Kelly fraction staking and uses an expected value calculator to rank picks in a consistent value framework, while Genius Sports focuses on event-to-market identifier mapping and update orchestration to keep model state aligned with live feeds.
Prediction-to-execution odds mapping
PredictZ focuses on prediction output mapping that converts model signals into execution-ready odds-format inputs for automated bet logic. RebelBetting also maps model confidence and line movement into governed bet placement rules, which requires odds-format conversion to avoid stale mappings.
Automation depth for bet placement workflows
Leans.ai concentrates on pick pipeline automation designed for operational decisioning and downstream integration into external systems. Sports Insights emphasizes monitored model iteration tied to forecast outputs, so teams get automation around model drift review and market-aligned decision workflows rather than only raw predictions.
Risk controls around staking and model drift
Betegy uses Kelly fraction staking paired with drawdown-aware guardrail logic to cap bankroll downside during model drift. RebelBetting uses rule-based staking limits that reduce Kelly fraction overexposure and relies on model backtesting loops to inform operational thresholds.
Change control and test isolation for model workflows
ZCode System supports versioned model workflows that isolate test configurations and decision rules from live runs to prevent test outcomes from leaking into production. PredictZ complements this with backtesting and calibration loops that produce measurable model change tied to prediction-to-execution mapping behavior.
Odds volatility handling and line movement signals
OddsJam converts closing line behavior into pre-match signals using odds deviation thresholding to map edges to specific line shifts. RebelBetting also responds to line movement by applying thresholded execution logic built from model confidence and odds changes.
Event and market entity alignment for live updates
Genius Sports provides event-to-market identifier mapping and update orchestration so model state stays aligned with live feeds across an event lifecycle. Forebet offers pre-match prediction outputs with historical result views for manual edge review, but it limits API and automation depth for trading-grade line movement.
How to choose AI betting software based on workflow shape and control depth
Start by matching the system’s execution model to the team’s workflow ownership so automation hits the right stage of the chain. PredictZ and RebelBetting are geared for prediction-to-execution chaining, while Sports Insights and ZCode System emphasize monitored iteration and test isolation around model change.
Then check how odds change propagates through mapping, thresholds, and latency handling. Betegy and RebelBetting add explicit risk guardrails, while OddsJam centers odds deviation thresholding and Genius Sports centers event-to-market mapping needed for continuous prediction refresh.
Pick the prediction-to-execution philosophy
If automated bet logic must consume execution-ready odds inputs with minimal translation work, select PredictZ for its prediction output mapping layer. If the team needs confidence plus line movement to trigger governed placement rules, select RebelBetting for thresholded execution logic and rule-based staking limits.
Decide where governance lives in the workflow
If governance must reduce bankroll exposure during model drift, choose Betegy because Kelly fraction staking is paired with drawdown-aware guardrail logic. If governance centers on operational thresholds created from backtesting loops, choose RebelBetting so thresholds are derived from model backtesting and calibration behavior.
Match monitoring needs to the system’s iteration loop
If internal teams require ongoing performance monitoring tied to forecast outputs to catch model drift, choose Sports Insights. If teams need safe change control by isolating test configurations and decision rules from live runs, choose ZCode System for versioned model workflows.
Validate odds volatility coverage against line update realities
If the workflow is built around closing line behavior converted into actionable signals, choose OddsJam because odds deviation thresholding turns line shifts into pre-match signals. If the workflow depends on continuous entity updates and odds refresh across an event lifecycle, choose Genius Sports for event-to-market identifier mapping and update orchestration.
Assess integration effort for odds formats and external systems
If odds scrape latency and market-type mapping are recurring failure points, prioritize Betegy because odds scrape latency handling is called out as a critical dependency. If integration requires wiring to external systems for operational pick pipelines, prioritize Leans.ai because it focuses on automation-oriented pick workflow with extensibility for external connectivity.
Who should use AI betting software built for automated odds mapping and governed execution
Teams that treat betting like an operational pipeline benefit most when systems convert model outputs into odds-format inputs and then apply governed staking rules. PredictZ and Betegy target teams that need automated EV ranking or prediction-to-execution chaining that can run across many markets.
Teams also benefit when the platform reduces the cost of keeping model state aligned with live feeds and event lifecycles. Genius Sports fits environments with frequent event and odds updates that require consistent identifier mapping, while Sports Insights fits forecasting teams that need monitoring and market comparison workflows for iterative refinement.
Betting desks running automated pick-to-bet execution
PredictZ supplies execution-ready odds-format inputs for automated bet logic, and Betegy ranks picks with an expected value calculator using Kelly fraction staking and drawdown-aware guardrails.
Forecasting teams that iterate models using monitored drift feedback
Sports Insights ties performance monitoring to forecast outputs and supports market comparison workflows during odds changes, which reduces manual alignment work during ongoing model improvement.
Trading teams that must keep test and live decision rules isolated
ZCode System uses versioned model workflows to isolate test configurations and decision rules from live runs, which reduces the risk of test outcomes affecting production decisions.
Teams centered on line movement signals and pre-match edges
OddsJam translates closing line behavior into actionable pre-match signals using odds deviation thresholding, which suits workflows designed around line shifts rather than only static pre-match predictions.
Model teams operating with frequent live feed updates
Genius Sports focuses on event-to-market identifier mapping and update orchestration so model state stays aligned with live feeds across the event lifecycle.
Common pitfalls that break AI betting automation in production
Many failed deployments come from treating odds mapping and risk configuration as an afterthought. PredictZ emphasizes execution-ready odds-format inputs, but its staking and risk limits still require careful configuration to avoid runaway exposure when market coverage is high.
Other failures come from mismatched expectations about governance, odds volatility handling, and data wiring quality. Leans.ai can require engineering time to wire automation correctly, and Betegy makes odds scrape latency handling a critical dependency for fast moving lines.
Assuming odds-format conversion is automatic across all sportsbook markets
RebelBetting warns that odds format conversion must be handled to avoid stale mappings, and PredictZ flags increased ops setup effort with high market coverage.
Running Kelly fraction staking without a drawdown or governance guardrail
Betegy ties Kelly fraction staking to drawdown-aware guardrail logic to cap bankroll downside during model drift, while RebelBetting uses rule-based staking limits to reduce Kelly fraction overexposure.
Skipping model change control and test isolation
ZCode System prevents test configuration leakage into production through versioned model workflows, and PredictZ relies on backtesting and calibration loops to measure model change before it impacts execution-ready mappings.
Building automation around odds signals without enough data capture and scraping discipline
OddsJam calls out time-consuming setup for data capture and scraping workflows, and Betegy identifies odds scrape latency handling as a critical dependency for fast moving lines.
Choosing an output-only workflow when continuous live entity updates are required
Genius Sports centers event-to-market identifier mapping and update orchestration for state alignment across an event lifecycle, while Action Network is built around editorial betting picks with odds context and not an API-first prediction market execution service.
How We Selected and Ranked These Tools
We evaluated PredictZ, Sports Insights, RebelBetting, Betegy, ZCode System, Leans.ai, OddsJam, Forebet, Genius Sports, and Action Network using features for automation and odds-to-execution mapping first, and ease and value for operational rollout impact second. Feature coverage accounted for 40% of the scoring because prediction output mapping, thresholded execution logic, and Kelly fraction risk controls directly determine whether picks can be placed reliably.
Ease and value each accounted for 30% because integration wiring and configuration discipline influence day-to-day throughput and decision cadence. PredictZ separated itself by combining an API-first prediction-to-execution chaining workflow with backtesting and calibration loops that produce measurable model change tied to odds-format input mapping for automated bet logic.
Frequently Asked Questions About ai betting software
How do PredictZ and RebelBetting convert AI outputs into bet-ready inputs?
Which tools support automation loops that include odds movement updates and backtesting?
How do Betegy and Betfair Trading API-style workflows handle odds format conversion and execution ordering?
What breaks if an AI betting workflow lacks a calibration loop for line quality drift?
When is identity integration and RBAC support the deciding factor for tool choice?
How does ZCode System separate test runs from live decisioning so models do not cross-contaminate?
Which tool is better for teams that need model iteration discipline across markets rather than just pick generation?
How do RebelBetting and Betegy implement staking guardrails around Kelly fraction style logic?
Which tools handle event-to-market mapping and update orchestration for fast odds and event lifecycle changes?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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