Innerluxes

Innerluxes INNERLUXES | Software Consulting and Development Company. Building scalable, high-performance digital products.

Specializing in Chromium browsers, custom websites, mobile & desktop apps, SaaS platforms, and AI-powered solutions.

23/08/2026

A search on a clinic map can say more than you think.

Someone looking for a doctor nearby might be searching because of a health problem. That search, on its own, can hint at what's wrong with them.

Most map tools never think about this. We did, while building one for a healthcare portal.

Here's what we changed:
Locations get saved once, not looked up every time someone visits.
Pins group together instead of covering the map with hundreds of dots.
There's a simple list view too, so it works for people using screen readers.
And none of it can be traced back to who searched.

Fast. Easy to use. And private by design.

If you're building something patient-facing, this is worth getting right from day one.

Full breakdown 👇 (link in comments)

23/08/2026

A bank came to us with a process that only existed in one form: buried inside their core systems, readable by nobody except the engineers who built it.

Onboarding, AML checks, account opening, loan approval every rule was code. Every change was a deployment.

So we built them a way to actually see it. A drag-and-drop tool where the process is a diagram, not hidden logic. The people running the process can read it, and change simple things like scoring rules, without waiting on engineering.

On top of that: a portal where every case has an owner, a timer, and an escalation path so when something gets stuck, someone notices right away instead of finding out later.

The part we're proudest of: no more "the process lives in someone's head.

If your team's dealing with the same thing, the full story is here 👇

22/08/2026

We build software for other companies. That means our engineers spend all day inside someone else's source code under NDA.

One downloaded repo. One screenshot. One code snippet pasted into the wrong AI chatbot. That's all it takes to lose a client's trust.

VPNs don't stop a copy-paste. VDI is slow and expensive. Heavy DLP software just makes developers miserable.

So we built our own secure browser instead. Sensitive client work runs in a locked workspace, downloads and screenshots are controlled automatically, and contractors only ever see the one client they're working for.

It still feels like a normal browser. No slowdown, no relearning anything.

Client code stays where it should because the tool keeps it there, not because someone has to remember a rule.

If you're curious how it works, the full story is here 👇 (link in comments)

Here's what a hospital room really looks like for a stroke patient.Tubes. Monitors beeping. A bed rail in the way. The b...
22/08/2026

Here's what a hospital room really looks like for a stroke patient.

Tubes. Monitors beeping. A bed rail in the way. The bed reclined at whatever angle it happened to land on.

Nothing like the clean, well-lit room most AI demos get built and tested in.

That's the mess we had to design for.

We built a bedside system that watches a patient move, speak, and respond to simple prompts on camera then turns that into measurements a clinician can rely on. Limb movement. Facial symmetry. Reaction speed. Speech clarity.

And here's the part we're proud of: it never has the final word. Every result goes to the clinician as a suggestion, not a verdict. They decide.

Because good healthcare isn't about a clever AI demo. It's about giving every patient the same careful, unhurried assessment no matter the bed angle, no matter the language they speak.

Full breakdown 👇 (link in comments)

21/08/2026

Ever get a test result back that says normal and wonder if anyone actually looked closely enough to know that?

That's the exact risk we had to design around while building an AI tool that screens eye photos for diabetic retinopathy and other eye disease.

Turns out the hardest part wasn't teaching the AI to spot disease. It was teaching it to recognize a bad photo blurry, badly lit and say "I can't tell" instead of guessing normal.

So now the system checks photo quality first, flags anything unreadable while the patient's still in the chair, and only then runs the actual disease-detection models. A real clinician makes every final call.

Small design choice. Huge difference for the person on the other end of it.

If it helps, the full story is here 👇 (link in comments)

A hospital needs a behavioral health consult. Right now.A specialist has to respond inside an agreed window. Notes have ...
21/08/2026

A hospital needs a behavioral health consult. Right now.

A specialist has to respond inside an agreed window. Notes have to land back in the patient's record

automatically, no manual re-typing, no lost paperwork.

That's the easy part to describe. It's the hard part to build.

Most platforms cut corners here. Coordinators get blanket access to everything because it's faster." That's exactly how sensitive mental health data ends up in the wrong hands.

We built it differently. Every consult moves through one tracked path intake to documented outcome. Coordinators see only what their role needs. Specialists get full context. Response windows are enforced by the system, not chased after the fact.

Compliance stops being a scramble. It just happens.

Full breakdown 👇 (link in comments)

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