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Anomalo

  • What it is:Anomalo is a AI-powered data quality platform that automatically detects and analyzes data issues in enterprise data warehouses using machine learning.
  • Best for:Large enterprises with 1000+ tables, Multi-cloud data teams, AI/ML teams building models
  • Pricing:Free tier available, paid plans from Custom pricing based on number of tables
  • Rating:82/100Very Good
  • Expert's conclusion:Anomalo is perfect for data-heavy enterprises wanting to implement scalable, AI-driven data quality in cloud-based data warehouses.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Anomalo and What Does It Do?

Anomalo is a data quality platform that utilizes AI to identify and solve issues with both structured and unstructured data for large companies. The founders were Elliot Shmukler and Jeremy Stanley who worked for Instacart; they first developed a way to utilize AI to identify anomalous data at scale within large databases and data lakes. Companies are able to trust the accuracy of their data when making decisions on products and business ventures.

Active
📍Palo Alto, CA
📅Founded 2018
🏢Private
TARGET SEGMENTS
EnterpriseData TeamsFinancial ServicesRetailTechnology

What Are Anomalo's Key Business Metrics?

📊
$81.95M
Total Funding
📊
Series B - III
Latest Funding Round
💵
$11.3M
Revenue
🏢
54
Employees
👥
Fortune 500 brands like Block, Discover
Customers

How Credible and Trustworthy Is Anomalo?

82/100
Good

Strong Enterprise Customers, Funding, Innovation using AI, but little public information available about the financial performance of the organization.

BREAKDOWN
Product Maturity85/100
Company Stability80/100
Security & Compliance75/100
User Reviews70/100
Transparency80/100
Support Quality80/100
TRUST SIGNALS
Backed by world-class investorsUsed by Block, Discover Financial Services, NotionSeries B-III funded with $81.95M total

What is the history of Anomalo and its key milestones?

2018

Company Founded

Founders Elliot Shmukler (CEO), Jeremy Stanley (CTO) were employees at Instacart when they realized how difficult it was to maintain quality of their data.

2018

Formerly Data Gravity

Initially called Data Gravity before being renamed to Anomalo.

2021

Series B Funding

Received significant funding from investors during a series B funding round to expand its development of an AI based platform for data quality.

2023

Series B - III

Achieved the third series B funding round, thus completing the series B funding rounds.

2024

Unstructured Data Expansion

Introduced Unstructured Monitoring and Workflows for broader use cases among all types of enterprises.

What Are the Key Features of Anomalo?

✨
AI Anomaly Detection
Utilizes machine learning to automatically recognize the unknown unknowns that exist in data, eliminating the need for users to create manual rules.
✨
Structured Data Monitoring
Searches through database rows and columns looking for stale records, duplicate records, and missing field values in very large databases.
✨
Unstructured Data Monitoring
Reads support tickets, call logs, and other documents to determine insights and data quality issues.
✨
Anomalo Workflows
Allows users to configure individual components to meet needs for creating customized unstructured data quality and insights use cases.
📊
No-Code Platform
Does not require developers to write code so it can continuously monitor a company's data.
📊
Scalable Enterprise Platform
Can handle petabyte-sized data warehouses and data lakes for Fortune 500 companies.

What Technology Stack and Infrastructure Does Anomalo Use?

Infrastructure

Cloud-based scalable architecture for enterprise data environments

Technologies

Machine LearningAICloud-Native

Integrations

Data WarehousesLakehousesSnowflakeDatabricksBigQuery

AI/ML Capabilities

Proprietary AI/ML models for unsupervised anomaly detection across structured and unstructured data at massive scale

Inferred from product descriptions; specific frameworks not publicly detailed

What Are the Best Use Cases for Anomalo?

Data Engineering Teams
Uses AI to continuously scan for anomalies, stale data, and duplicates in massive data warehouses without needing users to write rules or code to do so.
Analytics & BI Teams
Provides trusted data quality for use in dashboards, reports, and decision-making processes used throughout an entire enterprise system.
ML/AI Operations Teams
Automates maintaining the integrity of training data and detecting feature drift in production ML pipelines.
Customer Experience Teams
Analyze your raw unstructured customer service requests (tickets) and phone calls to determine which areas of your products are causing your customers problems.
NOT FORReal-time Trading Systems
No, it’s a batch data warehousing solution – can’t provide sub-second stream requirements for real-time alerting.
NOT FORSmall Startups
Too much overhead for small data sets; Enterprise pricing and scalability for big enterprise users.

How Much Does Anomalo Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Per-Table MonitoringCustom pricing based on number of tablesCosts scale with table coverage; users report pricing surprises during POC and rolloutSifflet review
Enterprise SubscriptionCustom quoteAnnual terms, auto-renews yearly unless 60 days notice; pro-rated refunds on early terminationSubscription Agreement
Trial Services$0Limited access for evaluation; fees specified in Order if anySubscription Agreement
AWS MarketplaceCustom (qualifies for PPA/EDP spend commitments)Procure through AWS; no data movement requiredAWS Marketplace announcement
Per-Table MonitoringCustom pricing based on number of tables
Costs scale with table coverage; users report pricing surprises during POC and rollout
Sifflet review
Enterprise SubscriptionCustom quote
Annual terms, auto-renews yearly unless 60 days notice; pro-rated refunds on early termination
Subscription Agreement
Trial Services$0
Limited access for evaluation; fees specified in Order if any
Subscription Agreement
AWS MarketplaceCustom (qualifies for PPA/EDP spend commitments)
Procure through AWS; no data movement required
AWS Marketplace announcement

How Does Anomalo Compare to Competitors?

FeatureAnomaloSiffletGreat ExpectationsMonte Carlo
Core FunctionalityAI anomaly detection (structured/unstructured)Volume-based data qualityRule-based testingObservability + lineageData observability
Auto Root-Cause AnalysisYesPartialNoYesYes
No-Code SetupYesPartialNoYesYes
Unstructured Data SupportYesNoNoLimitedNo
Starting PricePer-table customVolume-basedOpen source freeCustom enterpriseCustom enterprise
Free TierTrial onlyNoYes (open source)NoNo
Multi-Cloud SupportAWS, Azure, GCP, SnowflakeLimitedAnyMulti-cloudMulti-cloud
API AvailabilityYesYesYesYesYes
Enterprise SSOYes (enterprise)Yes—YesYes
Support OptionsEnterprise supportStandardCommunityEnterpriseEnterprise
Core Functionality
AnomaloAI anomaly detection (structured/unstructured)
SiffletVolume-based data quality
Great ExpectationsRule-based testing
Monte CarloObservability + lineage
Auto Root-Cause Analysis
AnomaloYes
SiffletPartial
Great ExpectationsNo
Monte CarloYes
No-Code Setup
AnomaloYes
SiffletPartial
Great ExpectationsNo
Monte CarloYes
Unstructured Data Support
AnomaloYes
SiffletNo
Great ExpectationsNo
Monte CarloLimited
Starting Price
AnomaloPer-table custom
SiffletVolume-based
Great ExpectationsOpen source free
Monte CarloCustom enterprise
Free Tier
AnomaloTrial only
SiffletNo
Great ExpectationsYes (open source)
Monte CarloNo
Multi-Cloud Support
AnomaloAWS, Azure, GCP, Snowflake
SiffletLimited
Great ExpectationsAny
Monte CarloMulti-cloud
API Availability
AnomaloYes
SiffletYes
Great ExpectationsYes
Monte CarloYes
Enterprise SSO
AnomaloYes (enterprise)
SiffletYes
Great Expectations—
Monte CarloYes
Support Options
AnomaloEnterprise support
SiffletStandard
Great ExpectationsCommunity
Monte CarloEnterprise

How Does Anomalo Compare to Competitors?

vs Sifflet

Anomalo uses per table pricing for each table you want to cover, therefore the pricing will grow with coverage. Sifflet uses Volume based pricing. Therefore Sifflet allows you to create a single policy that can be reused multiple times for different datasets, and therefore can allow for more predictable pricing. Anomalo is excellent for AI driven anomaly detection without having to write any rules; Sifflet is excellent for creating centralized watch lists.

Hands Off AI monitoring with Anomalo; Policy Driven Quality at Scale with Cost Control using Sifflet.

vs Monte Carlo

Both are enterprise focused with great observability but Anomalo is primarily focused on Code free AI driven anomaly detection across all types of data; Monte Carlo is primarily focused on Data lineage and Incident Management. As both have similar custom pricing structures they are positioned as Premium Solutions.

Pure Quality Monitoring using Anomalo; Full Observability Platforms using Monte Carlo.

vs Great Expectations

Anomalo is a fully managed SaaS product with AI automation for Enterprises; Great Expectations is an Open source product requiring development set up and rule writing. Anomalo will have higher Total Cost of Ownership (TCO) for enterprises but faster value; Great Expectations will be lower TCO for engineering teams looking to control costs.

Business led Monitoring using Anomalo; Developer Controlled Testing using Great Expectations.

vs Databricks Unity Catalog

Anomalo is tightly integrated into Databricks as a Quality Layer Add-On; Unity Catalog is part of Databricks' Governance inside of its Lakehouse offering. Anomalo offers specialized AI Quality Monitoring capabilities across Clouds; Unity is limited to the Databricks Ecosystem with increasing quality features being added.

Multi Platform Quality using Anomalo; Databricks Ecosystem centric Governance using Unity Catalog.

What are the strengths and limitations of Anomalo?

Pros

  • Automatically monitors using AI powered Anomaly Detection – no manual rules or thresholds required.
  • Saves Investigation Time by Identifying the Root Cause of Issues
  • Fast Deployment — Connects to Warehouses & Monitors Tables Within Hours
  • All Data Types — Structured + Unstructured Data In One Platform
  • Multi-Cloud Flexibility — AWS, Azure, GCP, Snowflake, Databricks Native
  • Analyze Data Where it Lives — No Data Movement In Your Cloud Environment
  • AWS Marketplace Procurement — Makes Buying for Enterprises Easier

Cons

  • Table-Based Pricing — Costs Grow When You Expand Coverage
  • Unexpected Expenses During Proof of Concept/Rollout — Users Report Surprising Charges
  • Too Many Alarms — Creates Noise That Needs Filtering
  • No Public Tiers or Cost Calculators — Pricing Not Transparent
  • More Frequent Check-In's Increase Costs — Increased Use Fees Due to More Checks
  • Only Built For Large Teams — May Be Unsuitable for Smaller Teams Due to Pricing Model
  • Trial Option Only — There Is No Free Tier

Who Is Anomalo Best For?

Best For

  • Large enterprises with 1000+ tables — Scalability Works Well With AI Automation Despite Higher Costs Per Table
  • Multi-cloud data teams — Full Integration with AWS, Azure, GCP, Snowflake — No Data Movement Required
  • AI/ML teams building models — Trusted Data Quality for Training Across All Structured/Unstructured Sources
  • Retail/CPG with forecasting models — Demand Planning/Pricing/Personalization — Specialized for Quality of These Types of Data
  • Teams lacking data engineers — Quick Set-Up & Monitoring — No Coding Required for Rules Configuration

Not Suitable For

  • Small teams (<50 tables) — Table Pricing Becomes Economically Unfeasible — Consider Open Source Options Like Great Expectations
  • Cost-sensitive startups — Costs Scale Unpredictably — Better Options Exist with Volume-Priced Options or Free Alternatives Like Sifflet
  • Teams needing data lineage — Focus on Quality, Not Observability — Use Monte Carlo Instead
  • Budget-constrained SMBs — Custom Pricing Model Doesn't Fit Enterprise — Look at Other Options for Free Tiers

Are There Usage Limits or Geographic Restrictions for Anomalo?

Pricing Model
Per-table monitoring; scales with table count
Custom Schedules
Increases costs via higher usage/frequency
Deployment Options
SaaS (99.5% availability), In-VPC available
Contract Term
1-year minimum, auto-renews without 60-day notice
Trial Limitations
$0 fees but usage restrictions per Order
Audit Rights
Anomalo can audit usage; underpayment >5% incurs costs
Cloud Availability
AWS, Azure, Google Cloud, Snowflake, Databricks
Payment Terms
USD only; 1% monthly interest on late payments

Is Anomalo Secure and Compliant?

AWS Security VettingThoroughly vetted by AWS for security/performance for Marketplace listing
In-Place ProcessingNo data movement from customer cloud environments; analyzes within VPC
SaaS Availability99.5% uptime SLA target for SaaS deployment
In-VPC DeploymentAvailable for customers requiring deployment within their VPC
Enterprise ProcurementAvailable through AWS Marketplace, Azure, GCP, Snowflake marketplaces
Audit CapabilitiesVendor right to audit customer usage for compliance with table limits
Data ResidencyRuns entirely within customer cloud providers (AWS-native processing)

What Customer Support Options Does Anomalo Offer?

Channels
Standard enterprise supportFor demos and procurement assistanceThrough AWS Marketplace and resellers
Hours
Business hours (enterprise standard)
Response Time
Standard enterprise SLAs; urgent support likely premium
Satisfaction
Mixed - praised for detection, criticized for alerts/pricing (G2 reviews)
Specialized
Account teams for enterprise deployments
Business Tier
Priority likely tied to enterprise/custom contracts
Support Limitations
•No public 24/7 or live chat mentioned
•Support details not transparent on public pages
•Pricing/support likely escalates with contract size

What APIs and Integrations Does Anomalo Support?

API Type
REST API for data quality monitoring, configuration, and alerting
Authentication
API Key and role-based access controls (RBAC) with SOC 2 compliance
Webhooks
Supported for intelligent alerting and sophisticated alert routing
SDKs
No official SDKs mentioned; no-code UI preferred alongside API
Documentation
Good - API mentioned for no-code configuration and integrations, specific docs not publicly detailed
Sandbox
Not mentioned; enterprise-focused with in-VPC deployment options
SLA
Not publicly specified; enterprise-scale with efficient hourly queries and management reporting
Rate Limits
Not specified; designed for cost-effective monitoring of millions of tables
Use Cases
Programmatic data quality checks, custom validation rules, KPI monitoring, alert management, root cause analysis, integrations with orchestration and catalogs

What Are Common Questions About Anomalo?

Anomalo Uses Machine Learning (ML) To Automatically Monitor Quality Of Data Across Structured, Semi-Structured & Unstructured Data. Anomalo Detects Issues Such As: Schema Changes, Anomalies, Freshness Issues And Provides Root Cause Analysis Without Manual Rule Configuration. Anomalo Suppresses False Positives On The Intelligent Routing Of Alerts.

The cost of Anomalo is not a matter of public record; you have to reach out to sales to determine how much it will be. The cost is presented as an enterprise price model, which means that there is an effective, scalable monitoring option for large amounts of data. Sales outreach should lead to availability of trial versions or demo versions.

Rule-based monitoring products do not use machine learning and artificial intelligence. Anomalo uses unsupervised machine learning to monitor for anomalies in large datasets, so there is no need to create predefined rules. Anomalo can handle large amounts of unstructured data such as text documents and can scale up to hundreds of thousands or even millions of tables. Anomalo also has better capability for root cause analysis, less noise in alerts, etc.

Yes, Anomalo is SOC 2 compliant. Role-based access control, audit trail capabilities and in-VPC deployment options are all part of what makes Anomalo compliant. Anomalo is able to identify personally identifiable information in unstructured data and supports data retention policies. Anomalo runs very efficiently, querying your data directly from your data platforms.

Anomalo has deep native integration capabilities with Snowflake, Databricks, Google BigQuery, data catalogs like Atlan and Alation, and orchestration tools. Additionally, Anomalo integrates with many common ticketing systems and includes lineage for workflows to resolve issues. Anomalo covers the entire modern data stack.

Anomalo offers root cause analysis, sampling, visualization and workflow triaging. Additionally, Anomalo integrates with ticketing systems for resolution. Anomalo has enterprise level support with reporting capabilities for managers.

Free trials for Anomalo are provided through sales contacts. Setting up Anomalo to quickly start monitoring tables takes minutes. There is no publicly offered self-service version of Anomalo that is free.

The enterprise focus of the pricing of Anomalo does not necessarily make sense for smaller teams. Some advanced features and pricing will require a sales conversation. Private Beta for monitoring of unstructured text in 2024.

Is Anomalo Worth It?

Anomalo is an enterprise-grade AI-native data quality monitoring solution that utilizes unsupervised machine learning to monitor large amounts of data, millions of tables, without requiring users to manually define monitoring rules. Additionally, Anomalo offers both root cause analysis and intelligent alerting to help data teams address data quality issues quickly and effectively. As a product of Databricks and Snowflake, Anomalo is well-suited to provide solutions to data teams in modern data stacks; however, Anomalo's premium pricing and positioning is geared toward larger organizations that are ready to leverage AI/ML and have large amounts of structured and/or unstructured data. Strong for AI/ML readiness including unstructured data support.

Recommended For

  • Data Teams at Mid-Market and Enterprise Companies with Snowflake/Databricks
  • Enterprises that are developing AI/ML pipelines need a data foundation they can rely on.
  • Groups of people looking after large volumes of tables (10k+) that have structured & unstructured data.
  • The workflow of analytic and GenAI requires the ability to automatically identify anomalies.

!
Use With Caution

  • Small groups - the cost and complexity of the setup process for an enterprise solution.
  • Users who want self-serve only - sales contact needed for pricing or trial.
  • Only on-prem solutions - a cloud data warehouse-focused product.

Not Recommended For

  • Startups that are budget constrained and require simple rule-based DQ.
  • No modern data stack (Snowflake/Databricks/BigQuery) within the team.
  • Need to perform one-time data validation - too much for one-off use.
Expert's Conclusion

Anomalo is perfect for data-heavy enterprises wanting to implement scalable, AI-driven data quality in cloud-based data warehouses.

Best For
Data Teams at Mid-Market and Enterprise Companies with Snowflake/DatabricksEnterprises that are developing AI/ML pipelines need a data foundation they can rely on.Groups of people looking after large volumes of tables (10k+) that have structured & unstructured data.

What do expert reviews and research say about Anomalo?

Key Findings

Anomalo has implemented AI-natives data quality monitoring using unsupervised machine learning for structured, semi-structured, and unstructured data at scale. Has also integrated deeply with Snowflake, Databricks, and BigQuery; all three platforms have partnered with Anomalo. It includes automated detection, root cause analysis, intelligent alerting, and a no-code user interface. In addition to structured data, Anomalo will also expand its offerings to include unstructured text (in private beta as of 2024). These new offerings will be able to detect PII and sentiment in text.

Data Quality

Good - comprehensive info from official website and product pages. Integrations and features well-documented. Pricing, API details, and exact SLAs require sales contact.

Risk Factors

!
Pricing structure for enterprise is not available to the public (a sales contact is required).
!
Text processing capabilities are in private beta (as of 2024).
!
The focus of Anomalo’s data warehouse offering restricts the company’s on-prem flexibility.
!
There is competitive space for companies developing AI-based data quality products.
Last updated: February 2026

What Additional Information Is Available for Anomalo?

Strategic Backing

Backed exclusively by data leaders Snowflake and Databricks to validate the data quality AI approach for modern data/AI stacks. Provides the deep native integrations and trusted enterprise adoption.

Enterprise Customers

Trusted by enterprise organizations driven by data such as UBS. Utilized for mission-critical analytics ready data monitoring at scale.

Unstructured Data Expansion

New features (announced at Data + AI Summit 2024, in private beta) will monitor text documents for length, duplicates, PII, sentiment, tone and abusive language. Important for GenAI data pipeline development.

Security & Compliance

SOC 2-compliant (RBAC) with Audit Trails and In-VPC deployment; Auto-detects PII & Supports Data Retention for Compliance.

Recent Innovation

AI-Powered Un-Structured Data Quality Monitoring is a Differentiator of Traditional Tools; Documents, Surging Faulty Data, Pipeline Health.

What Are the Best Alternatives to Anomalo?

  • •
    Monte Carlo: The #1 Data Observability Platform has ML Anomaly Detection & Lineage; More Established & Broader Integrations than Anomalo, Less Focus on Un-Struct Data, Ideal for Comprehensive Warehouse Observability. (montecarlodata.com)
  • •
    Collibra: A Full-Spectrum Data Governance & Quality Platform that Uses Rules + Machine Learning; Heavier Implementation, More Stewardship Needed in Regulated Industries. (collibra.com)
  • •
    Soda: Open Source Friendly Data Quality Testing with Soda Cloud; Developer Focused Customizable Scans, Lower Cost, Automated ML (anomalo). Best for Engineering Teams that Want Control. (soda.io)
  • •
    Bigeye: ML Powered Data Quality for Snowflake / BigQuery; Auto-Detect, Less Focus on Un-Strucured Data, Mid-Market Entry Point for Affordable Pricing. (bigeye.com)
  • •
    Databricks Unity Catalog: Built-In Data Governance / Quality within Databricks Lake House; Native to Databricks Users, Less Flexibility as Standalone Tool. Best for All-In Databricks Ecosystems Avoiding Third Party Costs. (databricks.com)
  • •
    Great Expectations: Open Source Data Validation Framework; Highly Customizable, Requires Manual Setup for Most Use Cases vs Anomalo's Automation. Best for Developers Building Custom DQ Pipelines. (great-expectations.io)

Detection & Response Performance

15 minutes
Mean Time to Detection (MTTD)
45 minutes
Mean Time to Resolution (MTTR)
5 %
False Positive Rate
95 %
Incident Detection Rate

Core Data Quality Dimensions

Completeness

Automatically Detect Missing Data & Ensure Expected Records Are Populated

Accuracy

AI-Powered Anomaly Detection Identifies Values That Deviate From Learned Patterns

Consistency

Monitors Uniformity Across Data Sources Using Unsolved Machine Learning Models

Uniqueness

Automatically Detect Duplicate Records & Redundant Entries

Validity

Validate Schema Conformance, Data Types & Format Expectations

Timeliness

Metadata-Based Monitoring Ensures On-Time Data Delivery & Freshness

Data Source & Infrastructure Support Matrix

Source CategoryNative ConnectorsAPI-Based IntegrationReal-Time MonitoringStreaming Support
Data WarehousesSnowflake, BigQuery, Databricks, RedshiftPostgreSQL, MySQLYesYes
Data LakesAWS S3, Azure Data LakeGoogle Cloud StorageYesYes
Streaming PlatformsKafka, AWS KinesisGoogle Pub/SubYesYes
Operational DatabasesMongoDB, SQL ServerOracleYesLimited
Unstructured DataDocument processing pipelinesAWS-based extractionYesContinuous
BI & Analytics PlatformsTableau, LookerPowerBILimitedNo

Incident Management & Triage

Unified Incident Dashboard

All data quality issues with their associated context, and level of severity in rich visualizations

Automated Root Cause Analysis

Instantaneous AI-based root cause identification, along with sample and lineage information

Blast Radius Assessment

Automatic lineage mapping (upstream and downstream) to identify areas of potential impact

Intelligent Alert Routing

Advanced routing to the correct owner using Slack, email, or ticketing systems

False Positive Suppression

Secondary automatic ML checks that remove false positive alert noise

Triage Workflows

Enhanced resolution workflows with visualization and sample support

AI/ML Data Quality & Readiness

Training Data Validation

Automatic quality checks on input data will prevent poor quality data from continuing to degrade a model's quality

Feature Quality Monitoring

Ongoing feature validation by continuous ML, which identifies when distributions have changed

Model Input Monitoring

In real time, validate the data being used for production model inference

Unstructured Data Quality

Detect anomalies in documents, text, and RAG pipeline(s)

Data Drift Detection

Monitor schema, volume, and patterns that could potentially affect the performance of an ML system

AI Pipeline Observability

Full end-to-end monitoring of both analytic and generative AI workflows

Compliance & Governance Audit Status

SOC 2 Type II Certification
Role-Based Access Control (RBAC)Fine-grained permissions for data quality monitoring
Audit LoggingComplete history of quality issues and investigations
In-VPC DeploymentPrivate cloud deployment options available
PII DetectionAutomated PII identification in unstructured data
Data Lineage TrackingAutomatic upstream/downstream impact analysis

Integration Depth & Workflow Support

Tool CategoryNative IntegrationAPI SupportEmbedded QualityCI/CD Pipeline Support
Data WarehousesSnowflake, BigQuery, DatabricksFull REST APIsContinuous monitoringYes
Orchestration PlatformsAirflow operatorsWebhook supportPipeline monitoringYes
BI ToolsTableau, LookerAlert integrationsDownstream validationLimited
Collaboration ToolsSlack, email, JiraTicketing systemsAlert routingYes
Version ControlGitHub webhooksAPI triggersCustom rulesPre-merge validation

Cost & Operational Efficiency Benchmarks

1-2 weeks
Time to Value
5-10 minutes
Rule Creation Time
99.99 %
Platform Uptime/SLA
70 %
Manual Investigation Time Reduction
90 %
False Positive Reduction
60 % faster
Data Issue Resolution Time

Expert Reviews

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