Make (Integromat)

What it claims to do

• AI Modules
• No-code Workflows
• Visual Builder
• Data Mapping

Claimed benefit. Advanced logic capabilities, error handling, visual workflow design

Reported use case. Sales operations teams automating multi-step processes across platforms

Source: the vendor and the capability mapping work behind this map. The Revenue AI Report has not independently verified these figures. Verified findings live in Research, with method, sample, and field date attached. Unfamiliar terms are defined in the AI and Revenue Dictionary.

Published case studies

• TripleTen

  Reduced ad setup time by 5 to 7x and processed 300,000 CRM events monthly using Make AI Agents.

  Read the Make (Integromat) case study ↗

• ChargeGuru

  Cut project delivery time from months to days and reduced development time to 15 minutes.

  Read the Make (Integromat) case study ↗

• Stellantis &You UK

  Auto-closed 18,000 customer messages and cut 151 hours of mundane work using AI-supported messaging.

  Read the Make (Integromat) case study ↗

We found a minimum of 3 published case studies here. There may be more we have not found. Case studies are published by the vendor. Customer names, figures, and outcomes are the vendor's claims. The Revenue AI Report has not audited them. Independently checked findings live in Research, with method, sample, and field date attached.

How to read these claims

The sample is chosen by the seller

A case study is the best result the vendor is allowed to publish. It is not a sample of all customers. The accounts that churned do not get a page.

There is no control group

Almost no vendor study compares the team using the tool against a matched team that did not. Without that comparison, the lift reported cannot be separated from headcount changes, pricing changes, seasonality, or a good quarter.

The baseline is usually missing

A percentage gain means nothing without the starting number. A 300% increase in meetings from two meetings a week is eight. Ask for the absolute figures and the time window.

Activity is not revenue

Most published gains are activity metrics: emails sent, replies, hours saved, meetings booked. Closed revenue, win rate, and retention are the metrics that survive a board meeting. Ask which one the study actually measured.

Named logos are not always customers

Logos and case studies have been published for accounts that had already churned or had only run a pilot. Ask the reference directly, by name, and ask how long they have been live.

Field notes: what users say in public

Independent feedback from review sites and practitioner forums, not vendor marketing. This is what a buyer would hear from a peer who has already run the tool.

Make offers stronger value than Zapier for complex workflows, but expect a steeper learning curve and occasional platform stability issues.

Ask these on the call

1. 01What SLA and incident communication process exists for outages that affect webhook or scenario data?
2. 02What onboarding or training resources exist to shorten the learning curve for complex scenarios?
3. 03How is performance monitored after platform updates, given reported browser crashes and delays?

How to read review evidence

Review sites are a biased sample

Most reviews are collected by the vendor, often with an incentive attached. Scores cluster high across the whole category, so a 4.6 average is closer to par than to proof. Read the one and two star reviews first, and read the most recent ones, because product and pricing change faster than the average score does.

Forums show the failure modes, not the base rate

Reddit and Hacker News threads surface what breaks, which is exactly what a business case needs. They do not tell you how common the problem is. Treat a repeated complaint as a question for the vendor, not as a verdict.

Complaints about price are usually complaints about structure

Seat minimums, credit packs that expire, annual lock-in, and per-action pricing produce most of the pricing anger in public reviews. Get the structure in writing, not the headline number.

Ratings are a snapshot

Every score here is dated. Check the live page before you cite it in a board deck.

The friction above is the tool level version of a pattern the Report has already measured. See Rollback for the method, sample, and field date behind it.

Claims versus the record

No citable discrepancy between this vendor's public claims and independent reporting was found at the time of the last check. That is not verification. It means nothing has been published either way, so the claims above still rest on the vendor's own account.

What has to be true before you buy

The Report does not review tools in isolation. Every tool on this map is connected to three things we publish elsewhere on the site: a decision framework that tells you how to evaluate it, a research theme that shows what we have measured in the market around it, and an essay that applies both to a real case. Those links appear at the bottom of this section so you can verify our reasoning instead of taking this page at face value.

Ambition level: Reinvent

The function is rebuilt. Judge it on the revenue model, and expect a governance owner.

Decide the data model, access control, and rollback plan before the first agent goes live.

Friction at this stage

• Data trapped in disconnected systems
• Manual transfer between tools
• No governance over who can deploy what

Common questions about Make (Integromat)

What does Make (Integromat) do?

Powerful visual automation platform with AI capabilities that connects apps and services to automate complex workflows. It sits in the Specialized AI Platforms & Tools category and maps to the Operations and data stage of the revenue journey.

Where does Make (Integromat) fit in a revenue team?

Make (Integromat) maps to the Operations and data stage at the Reinvent level of ambition, and is usually owned by the RevOps and GTM engineering seat. Reported use: Sales operations teams automating multi-step processes across platforms

Has Make (Integromat) been acquired or changed status?

Integromat was acquired by Celonis in October 2020, then renamed itself 'Make' in February 2022 while remaining a Celonis-owned product. Recorded October 2020. Source: https://www.prnewswire.com/news-releases/celonis-acquires-integromat-an-industry-leading-online-automation-platform-301152316.html

Does Make (Integromat) publish customer case studies?

Yes. 3 named customer stories are published, including TripleTen, ChargeGuru, Stellantis &You UK. These are vendor claims, not figures verified by The Revenue AI Report. A case study is the best result a vendor is allowed to publish, not a sample of all customers.

What do buyers say about Make (Integromat)?

Friction reported: Users report performance issues including browser crashes and severely delayed automation execution after platform updates Documented service outages affecting webhook data submissions Make offers stronger value than Zapier for complex workflows, but expect a steeper learning curve and occasional platform stability issues.

What should we ask Make (Integromat) before buying?

What SLA and incident communication process exists for outages that affect webhook or scenario data? What onboarding or training resources exist to shorten the learning curve for complex scenarios? How is performance monitored after platform updates, given reported browser crashes and delays?

Answers are assembled from the vendor material, published case studies, and independent evidence shown on this page. Terms are defined in the AI and Revenue Dictionary.