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Employees paste company data into ChatGPT every day. What leaks, what the account tier changes, and how to run private AI on your own servers.
When running AI on your own hardware actually pays off, answered through the three questions enterprises keep asking: control, cost, and performance.
Most teams are experimenting with AI in quality engineering. Very few trust it enough to make it part of the real work. The gap says a lot about how test case generation should be built.
The full technical follow-up to a LinkedIn post 68,000 people read: architecture, stack, versions, costs, trade-offs, and the ROI calculator.
The mass-market support gap has been around about as long as the industry has. Distributor FAEs, dedicated account teams, more training every year: all of it helped and none of it closed the gap. AI is the first thing I've seen that actually can.
Why the workarounds your team already uses are the best map of what to build, and where to keep it.
AI tokens are getting cheaper, but demand is rising faster. As technical industries put AI into documents, code, standards, and compliance workflows, future costs become harder to predict - but there is something companies can do to counteract that issue.
The AI Act rewards companies that can see their AI, explain it and control it. Six practical steps to get ready, and why ownership makes each one easier.
A recap of the LinkedIn Live session held on 28 May 2026, co-hosted by GSMA and its AI technology partner, Understand Tech.
Turning long, dense standards into clear, structured test cases is one of engineering’s toughest challenges. Check out our article to see how our newly developed Test Case Generator makes the process faster, simpler, and more reliable.
In this article, we look at three real-world use cases that show how AI-In-a-Box makes secure, enterprise-grade AI possible.
This article explores the growing risks of using public AI tools for enterprise data and explains why true AI sovereignty depends on control over inference, encryption keys, and data flows, not just hosting location.
Discover the story behind Naama Bak and Understand Tech
Documentation for semiconductor products and embedded systems software solutions is extensive, often reaching into thousands of pages. Navigating the mountain of documentation is time-consuming and potentially error-prone. This translates into high support costs for the companies selling these products and frustration for engineers.
AI at work comes with big promises… and a lot of myths. Let’s clear the air: here are the 5 myths we hear most, and what’s actually true with Understand Tech.
The AWS Builders Blog has published a step-by-step guide showing how to deploy Understand Tech inside your own AWS account using a PaaS deployment
When I worked at the semiconductor industry, I lived the daily grind of product features questions in particular on security chip products.
Plug an Understand Tech agent into your ServiceNow (or Jira) history so every inbound request is triaged automatically.
Semiconductor products, especially those designed for cybersecurity, come with extensive documentation. Each product may have thousands of pages of datasheets, application notes, and example code.
At Understand Tech, we work with organizations that need more than AI experiments. They need governed, scalable, and secure tools that integrate into real operations. One example is RTE international.
Technical standards and specifications are essential but complex, often involving thousands of pages. Finding precise information is usually limited to basic searches (Ctrl+F) or relying on busy experts.