01
Custom software development
Backend systems, web applications and APIs built around your process.
Services
AI automation in existing software
Putting a language model to work inside the systems you already run, with the checks that make it usable in production rather than impressive in a demo.
The useful kind of AI is boring.
Most of the value a business gets from a language model isn’t a chatbot. It’s the dull middle of a process. Reading an incoming email to decide which queue it belongs in. Pulling four fields out of a supplier’s PDF. Writing a first reply for someone to edit, or turning a week of support tickets into something a manager will actually read.
Those steps already exist in your company. Somebody does them by hand, every day, and finds them tedious. A model can do the first pass and a person keeps the final say. That division is the difference between a feature people keep using and one that gets quietly switched off after a month.
The engineering is ordinary. The model is one more external service that gets called, checked, logged and paid for. What makes it different from a normal API is that it can be confidently wrong. So the first thing to settle is what happens when the output is bad.
01 / How it runs
Start from the work, not from the technology.
The question is never where AI could be used. It’s which repetitive step costs real hours and produces something somebody can check.
-
01
Pick one step worth automating
Repetitive, with a clear input and a checkable output. One step, not a strategy.
-
02
Try it by hand first
Run a batch of real examples through the model yourself before any code exists. It is a cheap way to find out whether this is worth building at all.
-
03
Decide what a wrong answer costs
Before any code: who notices a mistake, and what happens when the provider is slow or down. That answer shapes everything after it.
-
04
Build it where the work happens
The call sits inside your own application, behind your own code, with retries, timeouts, spending limits and logging you can read.
-
05
Measure it against the manual result
Compare it with how the step was done before. If it isn’t better, that’s a finding too, and a cheap one to get early.
02 / What you get
What gets delivered.
-
A working step inside your system
Not a separate tool somebody has to remember to open. It runs where the work already happens.
-
A human check where it matters
Review, approval or an edit step anywhere a wrong answer would reach a customer, an invoice or a decision.
-
Limits and a paper trail
Caps per run and per period, so a loop or a busy day can’t turn into a surprising bill, and every call logged so an odd result can be traced back instead of argued about.
03 / When to look elsewhere
A model is often the wrong tool.
If the rule can be written down, write the rule. A lookup table, a regular expression or an if-statement is cheaper, faster, free to run and right every time. Reaching for a model where ordinary code would do is a common mistake, and an expensive one.
If a wrong answer can’t be caught before it reaches a customer, and there’s no room for a review step, the risk usually outweighs the saving. Not every process can take a step that is only right most of the time.
If the data involved can’t leave your own infrastructure, that has to be settled first. There are ways to work within that, but they change the cost and the timeline. That’s not something to discover halfway through.
04 / Other services
The rest of what Kevbyte does.
02
System and API integrations
Dependable connections between the systems and platforms you already run.
04
Software maintenance
Extending and improving software that is already in production.
05 / Contact
Have a project in mind?
A short conversation is usually enough to tell whether it’s a good fit. If it isn’t, you’ll hear that too.