Author: Rob Davenport

For years, custom software development followed a fairly simple rule: the problem had to be expensive enough to justify the solution.

Even a relatively small application could take weeks or months to design, build and test. That meant custom software was usually reserved for large projects, established products or business problems with a clear financial return.

Smaller operational problems were often left unsolved.

A company might have employees copying information between systems, renaming hundreds of files or updating records one at a time. Everyone knew the process was inefficient, but building custom software to fix it would cost more than the time it saved.

Agentic AI is beginning to change that calculation.

Developers can now move from an idea to a working application much faster. This makes it practical to build focused tools for problems that would never have justified traditional development costs.

We recently experienced this firsthand at Rovidx.

The problem: moving a large video library

We have a library of more than 800 videos stored on Vimeo for one of our streaming projects.

Vimeo has worked well for us, but it was no longer the best fit for how we wanted to manage and deliver this particular library. We decided to move the videos to Bunny, keep a local archive of the original files and update the video links inside our WordPress content management system.

The migration presented a few challenges.

Many of the videos were private and required authenticated access. Our WordPress database contained Vimeo HLS URLs rather than simple public video links. Each video needed to be downloaded, saved locally, uploaded to Bunny and matched with the correct WordPress content.

The old URL then needed to be replaced without disturbing the rest of the content.

Doing this manually would have meant:

• Finding each video in Vimeo
• Downloading the original file
• Saving it into an organized local archive
• Uploading it to the correct Bunny library
• Finding the matching post in WordPress
• Replacing the old Vimeo stream URL
• Confirming the new video worked
• Tracking which videos had been completed

Repeating that process hundreds of times would have consumed days of attention. It would also have created plenty of opportunities for mismatched videos, duplicate uploads and incorrect links.

It was exactly the kind of repetitive problem that should be automated.

Building a purpose-built migration tool

Instead of moving the library manually, I scoped out a small Electron application to manage the process.

The application needed to connect securely to Vimeo using OAuth, access the private video library and download the source files. It also needed to connect to Bunny, upload each video and monitor its processing status.

On the WordPress side, it needed to identify posts containing old Vimeo HLS URLs and replace them with the correct Bunny URLs.

The application also needed practical safeguards:

• A local archive of every downloaded video
• Migration progress and status information
• A clear record of successful and failed transfers
• Retry support when a download or upload failed
• Protection against uploading the same video twice
• The ability to resume an interrupted migration
• No automatic deletion of the original Vimeo files

I worked through the requirements and gave the plan to Codex to build and test.

About an hour later, I had a working application with an interface for connecting the services, reviewing the library and managing the migration.

The app turned a long series of manual tasks into a process that could run automatically and be monitored from one place.

There was still human work involved. The requirements had to be defined. Authentication, matching logic and failure handling needed to be considered. The results also had to be tested before the software could be trusted with the full library.

AI made the implementation dramatically faster. It did not remove the need to understand the problem.

The real change is which problems become worth solving

The interesting part of this project isn’t the video migration itself.

A video migration tool has a narrow purpose. We may use it for this project, complete the migration and never need it again.

In the past, that limited lifespan might have made the software difficult to justify. It would have been easier to assign someone the manual work than spend weeks developing an internal application.

That calculation changes when a useful tool can be designed and built in hours.

Businesses deal with small inefficiencies every day:

• Staff copy information from emails into a CRM
• Reports are assembled from several spreadsheets
• Documents are manually renamed and filed
• Product information is entered into multiple systems
• Customer records are checked against separate databases
• Media files are downloaded, converted and uploaded individually
• Website content is updated one page at a time

A large software platform may be unnecessary for these problems. In many cases, the business already has the systems it needs. The missing piece is a focused tool that connects those systems or automates one painful part of the workflow.

AI-assisted development makes more of these tools economically practical.

Custom software can become smaller and more specific

Software companies have traditionally tried to build products that serve as many customers as possible. That makes sense when development is expensive. The cost needs to be spread across a large customer base.

The result is often a platform filled with features that individual customers never use.

AI makes another model possible.

Instead of forcing a company to change its processes to fit a general-purpose product, developers can build smaller applications around the company’s existing workflow.

That could be:

• A desktop utility that processes files in a specific way
• An internal dashboard that combines information from several services
• A tool that moves data between two systems
• A customer portal built around an existing business process
• An application that turns a manual approval process into a managed workflow
• A specialized importer for an unusual data source
• A reporting tool designed around the metrics a company actually uses

These applications don’t need to become large SaaS products. They need to solve the problem reliably.

For a small business, saving an employee five hours every week can be meaningful. Preventing recurring data-entry errors can be even more valuable. Until recently, the cost of developing the solution could easily outweigh those benefits.

That barrier is getting lower.

Fast development still requires experience

It would be easy to look at tools like Codex and conclude that software development has become automatic.

It hasn’t.

AI can write code quickly, but speed alone doesn’t produce dependable software. Someone still needs to understand the workflow, identify the risks and decide what the application should do.

In the video migration project, the difficult questions weren’t about drawing buttons on the screen.

They were questions like:

• How do we securely access private videos?
• How do we match a Vimeo video to the correct WordPress post?
• What happens if an upload succeeds but the CMS update fails?
• How do we prevent duplicate work?
• Can the migration safely resume after an interruption?
• How do we confirm that the new video is ready before replacing the old URL?
• What information should be logged for troubleshooting?
• Which actions should never be automated?

These are software design and business-process questions.

AI helps an experienced developer explore options, produce code and test an implementation much faster. The developer still provides the context, judgment and accountability.

That distinction matters when software is touching customer records, financial information, private media or essential business systems.

What this means for Rovidx

For Rovidx, this expands the range of problems we can reasonably help clients solve.

A client may not need a six-month software project. They may have one recurring task that wastes time every week. They may use several platforms that don’t communicate properly. They may have an internal process held together by spreadsheets, emails and manual updates.

Previously, building a custom solution for that kind of problem could have been too expensive.

Now we can examine the workflow, identify the bottleneck and determine whether a focused application or automation can remove it.

Some solutions may become full software products. Others may be small internal tools designed for one company and one specific job. Both can create real value.

This also changes how we think about software planning.

Instead of spending months trying to define every possible feature before development begins, we can build a smaller working version, test it against the real process and improve it based on what we learn.

That reduces the risk of building the wrong thing. It also helps clients see results sooner.

A different kind of software business

AI is going to put pressure on parts of the traditional software industry. Development timelines will shrink. Clients will expect faster results. Some types of routine coding will become less valuable on their own.

At the same time, more businesses will be able to consider custom software for the first time.

The opportunity is no longer limited to building large platforms.

There is a growing need for people who can understand how a business operates, find the expensive or frustrating parts of the workflow and turn those problems into reliable software.

That combination of business understanding and technical judgment will matter more as writing the initial code becomes faster.

The video migration application started as a way to avoid hours of repetitive work. It ended up demonstrating something much larger.

When software becomes faster to create, we don’t simply build the same products more quickly. We can start solving an entirely different class of problems.

And for many businesses, those smaller problems may be the ones most worth solving.