Operations | Monitoring | ITSM | DevOps | Cloud

How to automate sending and receiving faxes in IT processes? APIs, webhooks, integrations

It's true that fax in 2026 may seem like a technology out of place in today's DevOps environments. However, it's also true that in many regulated industries - it's still a key part of document workflows. The problem isn't necessarily the communication channel itself, but rather how it's handled. Traditional fax requires devices, phone lines, and manual document processing. API integration allows you to migrate this process to a software environment and connect it with existing systems. Curious? Let's dive in.

The Missing Step in Mobile Release Operations: Store Screenshot Management

Mobile release teams are used to managing code, builds, test results, signing credentials and deployment approvals. Store screenshots often sit outside that system. They are treated as a final design request, passed between product, marketing and engineering in a collection of chat messages and shared folders.

R&D Tax Relief, Cash Flow and Forecasting: The Finance Priorities for Growing Tech Companies

Technology businesses often operate in an environment where significant expenditure comes before predictable revenue. Product development, software engineering, specialist staff and infrastructure can require substantial investment while the business is still establishing its market position.

SaaS Sprawl Is Becoming an IT Problem: Here's How to Bring It Under Control

For most organizations, SaaS sprawl does not begin with a bad technology decision. It starts with a useful tool. Marketing needs a new analytics platform. Sales adopts prospecting software. HR adds an applicant tracking system. Engineering signs up for another monitoring service. Someone discovers an AI tool that saves several hours a week and puts it on a company card. Each purchase makes sense on its own.

The Operations Bottleneck That Often Goes Unmeasured

Walk through a well managed manufacturing plant and there is usually no shortage of operational data. Teams monitor machine uptime, cycle times, scrap rates and throughput, then use that information to identify constraints and improve performance. However, some processes that influence overall capacity receive much less attention. In particular, activities that depend on people reading, counting and extracting information from documents are not always measured as operational processes.

From AI Prototype to Production: The Technical Architecture Enterprises Need

Building a generative model that spits out flawless answers in a controlled notebook feels like a massive win for any engineering team. But watching that exact same model crash the second it hits real, concurrent user traffic? That is a frustrating reality check. The gap between a slick proof of concept and a mission-critical deployment is surprisingly wide, and it almost always comes down to the underlying infrastructure. If your systems cannot handle the dynamic load, the smartest algorithm in the world will not save you.

Why AI Adoption Fails Without the Data Work First

Most enterprise AI projects don't fail because the model wasn't good enough. They fail because the data underneath was a mess before anyone switched anything on. Duplicated contacts, contradictory fields, records that haven't been touched in three years but are still floating around in production tables. The AI doesn't know any of that context. It just reads what's there and runs with it.

The Renewal Conversations Nobody Owns Until It's Too Late

Every recurring revenue business has a sales team that closes deals and a delivery team that does the work. But when it comes to the actual renewal, there's this weird dead zone where nobody takes responsibility. Someone eventually pulls a report, notices a contract is 60 days from expiry, and sends a panicked email to a customer they haven't spoken to in months.

CRM API Rate Limits: What Developers Need to Know Before They Build

Every CRM integration starts the same way. You write some code, test it against a sandbox account with a few hundred contacts, and everything works beautifully. Then you push to production, where the account has 50,000 records and three other integrations pulling data at the same time, and suddenly you're drowning in 429 errors.

When login systems become an ops problem

SSO usually enters a company as a convenience project. People are tired of juggling passwords, new employees need access faster, and security wants fewer loose credentials floating around the business. At first, that sounds like a clean IT improvement. Then the company grows, tools multiply, teams work across more environments, and login becomes part of the operating layer that keeps the whole business moving.