If your dashboards already tell you what happened, we build the next layer: forecasts, classifiers, optimizations, and decision support — embedded in the apps your team already uses, not stranded in a notebook.
Most analytics teams plateau after the dashboards land. The numbers are right, the charts are clean — but the next question (what's likely to happen, and what should we do about it?) needs a different toolkit. This is where most internal teams get stuck.
A data scientist built a forecast that works. It lives in a notebook, runs on her laptop, and updates when she remembers. The business is still using a spreadsheet.
Beautiful Power BI reports tell ops what already happened. Nothing in the same view says what will happen next week or which lever to pull.
You have an ML model. Your decision-makers have Excel. Without an interface they trust, the model's output never leaves the data team.
From a forecast inside a finance review to a real-time anomaly alert on a plant floor — the work below shows up as production software, not a deliverable PDF.
Hierarchical time-series models with prediction intervals. Built to slot into the planning process, not replace it.
Churn, lead quality, fraud, credit, ticket triage. Calibrated probabilities and explainability your risk team will accept.
Customer cohorts, asset behaviour groups, route patterns. Cohorts that survive contact with the marketing or ops team.
Linear, mixed-integer, and constraint-based optimization for routing, scheduling, pricing, and capacity — surfaced as a slider, not a solver log.
Topic modelling, sentiment, classification, and entity extraction — applied to support tickets, voice-of-customer, and contract corpora.
Models embedded directly into Power BI, Excel, or a custom Streamlit / Dash app — where the decision actually gets made.
Streaming inference for IoT, manufacturing QC, and ops monitoring. Alerts that fire before the shift ends, not the next morning.
Versioning, retraining, drift monitoring, and approval workflows. So the model still works in month nine, with someone owning it.
Most organizations sit comfortably on rungs 1–2. The value is on rungs 3–5. We help you climb without skipping foundations — each layer is a real capability, not a slogan.
Five phases. We size each one against a single decision the model is meant to support — not "the data."
Define the decision, the user, the cadence, and the cost of being wrong. Pick the metric the business will accept.
A naive forecast or simple heuristic. Often surprisingly good — and the bar everything else must beat.
Feature engineering, candidate models, honest holdout. We pick the simplest one that beats baseline by enough to matter.
Wrap the model in a UI your decision-maker actually uses — Power BI, Excel, a Streamlit app, or an API. Score on a schedule.
Monitoring, drift detection, retraining cadence, and a named owner. The model still works in month nine.
Outcomes worth aiming for from a focused predictive or prescriptive engagement. Magnitudes will vary by use case; we'll baseline yours during framing.
Hierarchical, reconciled, and honest about uncertainty. Embedded in the same review the planners already run.
Churn, leads, fraud, or risk scoring that beats the rules-based heuristic on the metric the business actually pays for.
An optimization or simulation surface where leaders move sliders and see the consequence — not a static slide.
Anomalies and threshold breaches surfaced into Teams, email, or the ops console while the shift can still act on them.
Versioned, retrained on a schedule, monitored for drift, and owned by a named person — not a hero.
A six-to-eight-week engagement that takes one well-framed use case from discovery to production — model, interface, and the operations plan to keep it running.
Engagements scoped per use case. Fixed-price options available for pilots; T&M for build-out and the second use case.
The questions we get most often during scoping calls. If yours isn't here, write to info@arkimetrix.com.
Three things: (1) the question is forward-looking — what will, what should, what if — not just what was; (2) there's a decision attached, made by a real person on a real cadence; (3) historical data exists in roughly the shape and depth needed. If two of three are weak, you're better off improving the diagnostic layer first. We say so during framing.
No. Many of our predictive engagements are run alongside an analytics team that has Power BI skills but not ML. We bring the modelling pattern and the deployment surface; your team brings the domain knowledge. By the end of the pilot, your analysts can read, retrain, and extend what we built.
Wherever fits your stack. Microsoft Fabric (notebooks + ML) for Power BI–centric estates, Databricks or Azure ML where the data lives in a lakehouse already, or a Python service behind an API for everything else. We don't bring a preferred platform — we bring patterns that work across them.
Predictive answers "what's likely to happen" — a forecast, a propensity score, an anomaly flag. Prescriptive answers "given that, what should we do" — an optimization, a recommendation, a what-if simulation. The latter is where most of the business value lives, and it's almost always built on top of a predictive layer that already works.
Yes — and we usually do. Excel via custom functions or Power Query; Power BI via paginated reports, Q&A, or a custom visual; or both at once. The interface is part of the deliverable, not an afterthought — a model nobody opens delivers no value.
A 30-minute scoping call. Bring the decision you wish was easier — a forecast, a score, an optimization, anything. We'll tell you whether it's worth modelling, what the simplest baseline looks like, and what we'd build first.