DataWise is a boutique production-ML practice: forecasting, vision, and online learning. In your cloud, or inside a closed network.
Multi-horizon sales and demand forecasts, graded openly against actuals every month.
Item-level forecasting, dynamic safety stock, and purchasing recommendations inside your ERP.
Segmentation, measurement, and visual inspection with human-in-the-loop tooling.
Self-hosted LLMs, retrieval, and vision inside closed networks. Nothing leaves.
Churn analysis, receivables anomaly detection, and behavioral classification on real event data.
Organizational knowledge agents and document intelligence over your own content.
End-to-end operations transformation: workflows scoped, systems centralized, automations and observability inside the platforms you already run: Priority, SAP, monday.com and more.
Per-Unit Intelligence is item-level demand forecasting with dynamic safety stock and purchasing recommendations, built to integrate with the ERP you already run.
Success KPI, defined up front:
at least a 10% reduction in purchasing error
A three-year data-science engagement for a Mueller-group industrial manufacturer (US market). Forecasts graded against actuals every month; CEO recommendation on file.
For a security-sensitive organization: architected and specified a zero-egress AI enclave, with private networking end to end, an MLOps foundation, self-hosted LLMs, and vision object detection. Built and demonstrated a fully isolated, self-contained AI assistant: chat with organizational knowledge, retrieval over internal documents, deep-research workflows, document and diagram generation. Everything runs inside the closed network. Nothing leaves it.
Built and demonstrated a SAM-based system that automatically segments airfields in satellite and aerial imagery and measures them with precision tooling, wrapped in a purpose-built UI with manual correction, object addition, and report generation. It turns the costliest phase of geospatial vision work into a supervised, minutes-long task.
Built a full operations agent living in WhatsApp, working in Hebrew voice notes and text. It knows the organization's content library, finds the right videos, manuals, and deliverables, handles compression and format constraints automatically, and delivers them in chat instantly. It clarifies on demand and supports selling, marketing, and in-meeting moments in real time.
The same staged structure as our actual scope-of-work documents. Each stage ends with a measurable result and a decision, and the engagement earns its next stage. The full page: how we work
שלב א' · אפיון
We map your data, your systems (Priority, SAP, חשבשבת), and the business decision the model must serve. The deliverable is a scope-of-work with success KPIs agreed before anything is built.
כל מדד מוגדר עם בסיס להשוואה. לדוגמה: שיפור 'טעות הרכש' בלפחות 10% מהמצב הקיים.
Honest exit point. Characterization can conclude that AI is not the right tool yet, or that your data is not ready. The engagement can end here, and the findings are yours.
שלב ב' · הוכחת היתכנות ובחינת תוצאות אמת
We build the capability on your real data and grade the results against the agreed KPIs (בחינת תוצאות אמת). Not a demo: a measured test period with error tracking you can read yourself.
Honest exit point. If the results do not clear the bar we set together, the engagement ends here, with the full evaluation in hand.
שלב ג' · הטמעה
Deployment into the systems you already run, as a staged rollout (הטמעה מדורגת). Monthly grading against actuals continues in production, the same ledger discipline shown above.
You own what we build: models, code, and pipelines. No platform meter, no lock-in.
We will tell you whether the data can carry it, what it would take, and how it will be graded.
