DATA & ANALYTICS

Verum Review 2026

Most data quality tools give you software and expect your team to run it. Verum takes a different approach: it's a managed service that combines AI automation with human review to clean your CRM data for you. For RevOps leaders who know their data is a mess but can't allocate the headcount to fix it, that outsourced model is genuinely appealing. The question is whether giving up control of your data cleaning produces results you can trust.

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The Verdict: Verum occupies a unique position in the data quality market as a managed service rather than a self-serve platform. For RevOps teams that have the budget but not the bandwidth to tackle data quality, Verum's AI-plus-human model delivers clean data without consuming internal resources. The fixed per-record pricing is refreshingly transparent, and the 10x speed claim over traditional methods holds up for bulk cleanup projects. The trade-off is control: you're trusting an external team with your data quality standards. For teams that have repeatedly deprioritized data cleaning because nobody has time, Verum solves the staffing problem. Just define your standards clearly before handing over the keys.

Company Overview

What Is Verum, from a RevOps Seat?

Verum is an AI-powered managed data cleaning service designed for B2B go-to-market teams. Unlike traditional data quality tools where you buy software and run the cleanup yourself, Verum handles the actual data cleaning work. You send them your data, define your standards, and their combination of AI models and human reviewers delivers cleaned records back. It's data quality as a service rather than data quality as a software platform.

The AI component handles pattern recognition, standardization, and bulk transformations at scale. The human review layer catches edge cases, validates ambiguous matches, and applies judgment that pure automation misses. This hybrid model is what enables Verum's speed claims: AI handles the 80% that's straightforward, and humans handle the 20% that requires context. The result is faster turnaround than a fully manual approach and higher accuracy than a fully automated one.

For RevOps leaders, Verum addresses a specific operational reality: data cleaning is important, everyone agrees it's important, and it almost never gets prioritized because the team is always fighting fires. Verum removes the resourcing bottleneck by making data quality an external service rather than an internal project.

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RevOps Reality Check

Verum cleans your data at a point in time. Without ongoing data governance processes and prevention of new dirty data from entering your systems, you'll need Verum again in 6-12 months. Use the clean baseline as motivation to implement data entry standards and validation rules.

Pricing

What Verum Actually Costs

Verum uses per-record pricing, which makes cost predictable and directly tied to the scope of your data quality problem. This is a departure from the per-user or flat annual license models that most data tools use, and it's well-suited to project-based cleanup work.

Plan	Price	What’s Included
Standard Cleaning	Per-record pricing	Standardization, formatting, deduplication identification, field normalization Most Common
Deep Enrichment	Per-record (higher tier)	Standard cleaning plus data enrichment, verification, and validation against external sources
Full Service	Custom project scope	End-to-end data audit, cleaning, enrichment, CRM re-import, and validation reporting White Glove

Keep In Mind

Per-record pricing means you pay for exactly what you clean. No shelfware risk.
Volume discounts apply for larger datasets. Get a quote based on your actual record count.
One-time cleanup projects and ongoing maintenance engagements are both available.
Pricing varies by complexity: simple standardization costs less per record than full enrichment and verification.

Core Features

What Verum Does Well

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AI-Powered Pattern Recognition

Machine learning models identify data quality issues at scale: formatting inconsistencies, incomplete records, standardization gaps, and potential duplicates.

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Human Review Layer

Trained data analysts review edge cases, validate ambiguous matches, and apply contextual judgment that pure automation misses. The human layer catches what AI gets wrong.

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10x Processing Speed

The AI-human hybrid model processes records roughly 10 times faster than traditional manual cleanup. Bulk projects that would take months internally are completed in weeks.

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Quality Reporting

Detailed reports on what was cleaned, what changed, and the overall data quality improvement. Before-and-after metrics give you the numbers to present to leadership.

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CRM Integration Support

Handles data export from and re-import to major CRMs including Salesforce and HubSpot. The service manages the technical logistics of getting clean data back into your systems.

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Data Security Protocols

SOC 2 compliance, encryption in transit and at rest, and access controls on your data. Addresses the security concerns inherent in sharing CRM data with an external service.

Limitations

Where Verum Falls Short

No tool is perfect. Here are the real trade-offs you should know about:

You Give Up Direct Control

With a self-serve tool, you control every merge rule, every standardization decision, and every exception. With Verum, you define standards and review results, but you're not making record-level decisions during the process. For RevOps leaders who want granular control over data transformations, this outsourced model requires a trust-building period. Start with a small batch to validate quality before committing a full dataset.

"The first time we sent data to Verum, I was nervous about the loss of control. After reviewing the results on our test batch, the quality was higher than what we were achieving internally. Now I wish we'd outsourced sooner."

RevOps Manager, Series C SaaS

Not a Continuous Solution Without Ongoing Engagement

Verum is primarily a cleaning service, not a real-time data quality platform. It excels at bulk cleanup projects but doesn't sit inside your CRM preventing bad data from entering. You'll need complementary tools like validation rules, duplicate prevention, and data entry standards to maintain the clean baseline Verum establishes.

Newer Market Entrant with Smaller Track Record

Compared to established data quality vendors like Validity (DemandTools), Informatica, or even Cloudingo, Verum is a newer entrant. The managed service model is differentiated, but the company's track record and customer base are smaller. For risk-averse enterprise procurement teams, this can be a friction point in the evaluation process.

"Our procurement team flagged Verum as a vendor risk because they're newer. We mitigated it by starting with a limited project scope and expanding after proving results. Smart approach for any newer vendor."

Sr. Director of Ops, Mid-Market B2B

Evaluation

Pros and Cons Summary

+ The Good Stuff

Managed service model removes the internal resourcing bottleneck for data quality work.
AI-plus-human hybrid delivers both speed (10x faster) and accuracy (human edge-case review).
Per-record pricing is transparent and directly tied to scope. No shelfware risk.
Quality reporting provides before-and-after metrics for leadership presentations.
Handles both CRM export and re-import logistics, reducing the technical burden on your team.
Addresses the real reason data quality projects stall: nobody has time to run them.

- The Problems

Loss of granular control over individual record-level cleaning decisions.
Not a real-time solution. Doesn't prevent bad data from entering your CRM ongoing.
Newer vendor with a smaller track record than established data quality platforms.
Requires sharing CRM data with an external party, which triggers security and procurement review.
One-time cleanup without ongoing governance means you'll need repeat engagements.
Per-record pricing on very large datasets can exceed the cost of annual platform licenses.

Decision Framework

Should You Buy Verum?

BUY VERUM IF

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You have the budget but not the bandwidth for data quality

RevOps teams that know their data is a problem but can't carve out the internal resources to fix it will get the most value from Verum's managed approach.

Your CRM data quality is demonstrably poor and it's impacting reporting, routing, or sales productivity.
You've tried to prioritize internal data cleanup multiple times and it keeps getting deprioritized.
You have budget for a data quality project but not headcount to execute it.
You need a clean baseline fast (weeks, not months) and can maintain it with internal governance after.
Your team is comfortable with a managed service model and the vendor security review process.

SKIP VERUM IF

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You need ongoing, embedded data quality automation

Teams that need real-time data quality enforcement or prefer to own the tooling and process in-house should look at platform solutions instead.

You need real-time duplicate prevention and data validation at the point of entry.
Your team has the capacity and skills to run data quality tools internally.
You prefer full control over every data transformation decision.
Your procurement process cannot accommodate a newer vendor with a shorter track record.
You need a permanent, always-on data quality layer rather than project-based cleanup.

Alternatives

Verum Alternatives Worth Considering

Tool	Starting Price	Strength	Best For
Cloudingo	$1,096-$10K/yr	Self-serve Salesforce dedup	Teams wanting to own the dedup process internally
Openprise	From $35K/yr	Full data orchestration platform	Enterprise teams needing ongoing, automated data management
DemandTools (Validity)	Custom pricing	Established Salesforce data toolkit	Teams wanting self-serve tools with a proven track record

🔍 Questions to Ask Before Signing

How bad is our data, quantitatively? Measure duplicate rates, field completeness percentages, and standardization consistency before engaging Verum. You need the baseline to scope the project and measure the outcome.
What will we do to maintain data quality after Verum cleans it? A one-time cleanup without governance is a recurring expense. Before engaging, plan the validation rules, duplicate prevention, and data entry standards that will preserve the clean state.
Can we start with a pilot batch? Send a subset of records (1,000-5,000) for cleaning first. Review the results against your standards. Validate quality before committing a full dataset to the service.
What are our data security requirements for external sharing? Verum requires access to your CRM data. Run the vendor through your security review early so procurement doesn't become a bottleneck after you've already scoped the project.
What's the per-record cost at our volume, and how does it compare to doing it in-house? Get a specific quote based on your record count and complexity. Compare it to the internal labor cost: hours needed multiplied by fully loaded hourly rate of the team members who would do the work.

FAQ

Frequently Asked Questions

How do RevOps teams use Verum?

RevOps teams use Verum for bulk CRM data cleanup projects that internal teams cannot prioritize. The most common engagement is a full CRM audit and clean: Verum ingests your Salesforce or HubSpot data, applies AI-driven standardization and deduplication identification, runs human review on edge cases, and delivers cleaned records back with a quality report. Teams also use Verum for pre-migration cleanup (cleaning data before switching CRMs), post-acquisition data merging, and quarterly maintenance passes to address data decay from normal operations.

Is Verum worth it for RevOps?

Verum is worth it when your team knows data quality is a problem but cannot allocate headcount to fix it. Calculate the internal cost: if a RevOps analyst at $120K/year fully loaded would spend 3 months on a cleanup project, that is $30K in labor for a one-time effort. Verum's per-record pricing often beats that math, delivers faster results (weeks vs. months), and produces higher accuracy through the AI-human hybrid model. The managed service model is particularly valuable for teams that have repeatedly deprioritized cleanup because other fires take precedence.

How much does Verum cost?

Verum uses per-record pricing that varies by complexity. Standard cleaning (standardization, formatting, dedup identification) costs less per record than deep enrichment (cleaning plus verification against external sources). Full Service projects are custom-scoped. Volume discounts apply for larger datasets. The per-record model means you pay for exactly what you clean with zero shelfware risk. Get a quote based on your actual record count and data complexity. One-time cleanup projects and ongoing maintenance engagements are both available.

What are the main limitations of Verum?

Three limitations to consider. First, you give up direct control over record-level decisions. You define standards and review results, but you are not making individual merge decisions during the process. Start with a pilot batch to build trust. Second, Verum is a cleaning service, not a real-time prevention tool. It does not sit inside your CRM blocking bad data at entry. You still need validation rules and duplicate prevention for ongoing hygiene. Third, Verum is a newer vendor with a smaller track record than established platforms like Validity or Informatica, which can trigger procurement concerns at risk-averse enterprises.

Verum vs Cloudingo for data quality?

Verum and Cloudingo solve the same problem (dirty CRM data) with fundamentally different models. Cloudingo is self-serve software: you buy the license, configure matching rules, and run the dedup yourself. Verum is a managed service: you hand over the data and get clean records back. Cloudingo costs $1,096-10K/year and requires internal expertise to run. Verum charges per-record and requires no internal bandwidth. Choose Cloudingo if you have someone to own the tool and want ongoing, embedded dedup. Choose Verum if you need a cleanup done fast and nobody has time to run the software.

The RevOps Report’s Bottom Line

Verum solves the data quality resourcing problem that plagues most RevOps teams. The managed service model means you get clean data without consuming internal bandwidth, and the AI-plus-human approach delivers both speed and accuracy. Per-record pricing keeps costs transparent and scope-aligned. It's not a replacement for real-time data quality tools or ongoing governance, but it's the best option for teams that need a clean baseline fast and don't have the headcount to do it themselves. Start with a pilot batch, validate the quality, then scale. And use the clean state as the foundation for implementing the governance processes that prevent the next cleanup cycle.

But know the trade-offs:

Start with a small pilot batch (1,000-5,000 records) to validate quality before committing full scope.
Plan your ongoing data governance strategy before the cleanup. A clean baseline without prevention is temporary.
Run the vendor through security and procurement review early. External data sharing adds lead time to the process.