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Adeptia Named an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Data Integration Tools

Adeptia has been named an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Data Integration Tools, published November 2, 2026. We believe this reflects Adeptia's focus on the first mile of data: getting information from partners, customers and internal systems mapped, validated and ready to use.

Here's what we think it says about where data integration is going, and where Adeptia fits.

What is the first mile of data, and why does it matter?

Most data integration conversations start at the warehouse or the lakehouse. The harder problem usually sits upstream.

Data arrives from carriers, brokers, banks, providers and internal systems in formats nobody agreed on. Someone has to map it before anything downstream works. In regulated industries, mapping work is slow, manual and expensive, and it's often the reason a new partner takes months to onboard instead of days.

That's the first mile. It's where Adeptia has focused for more than 25 years.

What did Gartner say about Adeptia?

In the report, Gartner notes that customers cite Adeptia's AI data mapping, self-service capabilities and support for external partner onboarding as strengths.

We read that as validation of the problem we've built the company around. We'll let the report speak for itself on the rest.

How is AI changing data integration?

Gartner reports that the data integration market grew 13.4% in 2025 to $6.6 billion, and names agentic data integration as a major contributor to that growth. From where we sit, the shift is practical. AI is most useful in data integration where the work is repetitive and pattern-based, and mapping is exactly that kind of work. Every new partner file looks a little different from the last one. An AI model that learns from past mappings can propose the next one, and a human can review it instead of building it from scratch.

The second shift is agents. As companies put AI agents into production, those agents need a reliable way to request data and kick off workflows without someone writing a new integration each time.

What does Adeptia Automate do?

Adeptia Automate brings data integration, application integration and B2B data exchange together in one platform. Recent releases added:

  • Agentic, self-learning AI data mapping that improves as it learns from your mappings
  • Unstructured data processing for documents and files that don't arrive as clean tables
  • Model Context Protocol (MCP) support so AI agents can trigger integration workflows directly

Customers in insurance, financial services and other regulated industries run Adeptia in the cloud or self-managed, depending on their requirements.

What Adeptia's CEO says

"Most enterprise data problems start well before the data reaches a warehouse," said Charles Nardi, CEO of Adeptia. "Data shows up from partners and internal systems in formats nobody agreed on, and someone has to map it. That's the work we built Adeptia around. AI data mapping takes the slow, manual part out of it, so teams can onboard a new partner or data source without starting from scratch every time."

Frequently asked questions

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Find out how Adeptia Automate can cut partner and customer data onboarding from months to days. Bring a sample file, and we’ll show you AI mapping it live.

Gartner Disclaimer

GARTNER and MAGIC QUADRANT are trademarks of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner's business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

Source: Gartner, Magic Quadrant for Data Integration Tools, Thornton Craig, Robert Thanaraj, Sharat Menon, Jenna Goodrich, Nina Showell, 2 November 2026. [Add report link once provided]