Stop preventable losses. Recover capacity. Make more profitable decisions from the data you already have.
Synthetic diagnostic preview · not a live customer score
MIHZI connects the data you already have, builds the pipelines and analytical foundations you are missing, and delivers governed prediction, optimization, and decision workflows in your cloud, on-premises, or as a managed service.
A decision system connects evidence to action.
It collects and governs the right data, produces a score, forecast, scenario, or recommendation, places it inside the team’s workflow, and learns from the outcome under controlled evaluation.
Start with messy data
We can begin with fragmented APIs, files, warehouses, and operational systems. The first useful result may be a governed dataset or dashboard—not a model.
Build the smallest useful method
Rules, statistics, classical ML, forecasts, or optimization—chosen by what performs reliably on unseen data at your real review or planning budget.
Keep people accountable
Interfaces and bounded workflow assistance gather evidence and draft recommendations. Humans remain responsible for regulated, clinical, and adverse decisions.
Four ways the work pays for itself.
We do not sell “efficiency” as an abstract. Each engagement names a baseline and a value measure the buyer already owns.
From governed data foundations to decision workflows.
We connect source systems, build reliable batch or streaming pipelines, create evaluated predictive or optimization services, and put evidence where people act.
One end-to-end example, live.
We took public synthetic transaction data through chronological evaluation, leakage controls, model selection, artifact freezing, deployment, and live inference. The example demonstrates engineering and evaluation discipline for a review-capacity decision; it is not evidence of prevented loss or performance for your organization.
Built for decisions that repeat — and matter.
These are separate engagements. We begin with one repeated decision, one accountable owner, the data available today, and one measure of improvement.
A small paid diagnostic, not a transformation programme.
Prove an approved data-access path, map one decision and its current cost, assess quality and lineage, and decide together whether a model is justified.
Built for data to decision delivery.
MIHZI is led by Brian Musonza, a ML/MLOps engineer with experience in real-time decision infrastructure, feature stores, production ML systems, data pipelines, APIs, and agentic workflows. We publish one end-to-end synthetic proof of the delivery method, research, artifact freezing, and live inference, not claimed performance for your organization.
Your data remains yours.
MIHZI supplies the pipelines, models, interfaces, deployment modules, and agreed operational support. Customer data is not used to train a model for another customer.
Find the first decision worth improving.
In a small paid diagnostic, we trace the data and current process, quantify the cost of the bottleneck, and test whether measurable value justifies deployment.