Creovix
Selected case studies

Transformation,
told honestly.

A small selection of the work we can talk about. Each one structured the way we actually thought about it — problem, thinking, approach, innovation, outcome.

W—01
Finance · Europe

An institutional trading floor, rebuilt as a single surface.

The problem
A top-ten European bank's discretionary trading desk operated across 41 systems, 11 spreadsheets and three vendors. Latency was measured in conversations, not milliseconds.
The thinking
We didn't start with the screens. We started with the question every trader asks at 6:43am — 'where do I stand?' — and worked backwards from the shape of that answer.
The approach
A two-year program. New data spine. Real-time risk model. One trading surface designed around the human, not the asset class.
The innovation
An agentic layer that pre-stages morning packages, surfaces drift, and writes the first draft of every compliance note before a trader sits down.
The outcome
Time-to-first-trade dropped from 22 to 4 minutes. Compliance exceptions fell 71%. The desk's headcount stayed the same. Its capacity nearly doubled.
The lesson
The right interface isn't a feature on top of a system. It is the system.
W—02
Climate · Global

Forecasting wildfire risk across three continents.

The problem
Emergency services were combining satellite feeds, weather APIs and ground reports manually. The window between signal and action was widening, not narrowing.
The thinking
The frontier wasn't more data. It was a model that could reason across modalities in real time — and an interface that respected how incident commanders actually make decisions.
The approach
A multimodal forecasting system. Live satellite, weather telemetry and ground sensor fusion. A decision surface designed with the people running rooms at 3am.
The innovation
An on-prem inference path that runs in disconnected regions — the places where the model is needed most are the places connectivity fails first.
The outcome
Mean time from signal to dispatch fell by 47%. Three national services now run on the platform. One regional director told us it gave them back an hour every shift.
The lesson
Build for the hardest night, not the easiest demo.
W—03
Healthcare · MENA

A clinical co-pilot inside the EMR of a national hospital network.

The problem
Doctors were spending more time documenting than diagnosing. AI pilots had failed twice — too generic to trust, too noisy to use.
The thinking
The model wasn't the problem. The deployment was. We started with clinical workflows, ended with evaluation suites, and only then built the surface.
The approach
An evidence-aware assistant native to the EMR. Tuned on internal records under strict governance. Evaluated against patient outcomes — not benchmark accuracy.
The innovation
Evals as a product. Every model change is tested against a suite of real clinical scenarios curated by the network's own physicians.
The outcome
Documentation time per patient down 34%. Clinician trust scores up from 28% to 81% in eight months. National rollout begins next quarter.
The lesson
Trust isn't earned with accuracy. It's earned with restraint, evaluation, and the right hand-off.
W—04
Industry · Global

Predictive maintenance for a heavy-rail operator.

The problem
Eleven years of sensor data, maintenance logs and incident reports — and almost none of it was being used to prevent the next failure.
The thinking
The asset wasn't the trains. It was the institutional knowledge buried in PDFs and field notebooks. We had to surface it before any model could help.
The approach
A unified asset graph. Predictive models trained on the full history. A field interface that respects the conditions engineers actually work in — gloves, cold, no signal.
The innovation
An offline-first mobile companion that turns a hand-written note into a structured signal the model learns from overnight.
The outcome
Unplanned downtime fell 38% in the first eighteen months. Maintenance windows shortened by an average of 22%. The operator is exporting the system to two peer networks.
The lesson
The unfair advantage is the data already inside the company. Most of our job is helping it find a voice.