Data Engineering & AI
Turn fragmented supply-chain data into real-time insight and AI copilots
DataShip builds modern data platforms, pipelines and AI layers for operators who actually run supply chains. From advanced analytics, Power BI and semantic models to LLM copilots inside Dynamics and warehouse apps, we take you from messy exports to real-time decisions on demand.
Data & AI Fabric Overview
• Live Metrics
1
Pipelines unify your data
We extract, clean and join ERP, WMS, TMS, e-commerce and 3PL data into a single, analytics-ready model.
2
Dashboards & alerts light up
Power BI and other tools show inventory, service levels, orders and margin by channel, customer and product.
3
AI copilots sit on top
LLMs answer questions in plain English, propose scenarios and draft actions inside the tools your team already uses.
1 source of truth
For orders, inventory & logistics
Minutes not days
To answer key questions
Analytics Deep Dive
• Insights
High-value analytics layers
Deep drill-downs for margin, service levels, demand patterns and SKU-level performance.
Top questions answered
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Which customers drive negative margin?
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Which SKUs have volatile demand?
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Where are inventory risks building?
Why DataShip for Data Engineering & AI
Most AI projects fail because the data is messy, the use-cases are vague, or the models never make it into the daily workflow. We fix that by starting with your supply-chain problems, then building the data fabric and AI layer around them.
Supply-chain native
We live in ERP, WMS & logistics
Dynamics 365, SAP, Fishbowl, Blue Yonder, Kinaxis and your e-commerce stack are where we start, not an afterthought.
Right-sized for SMB & mid-market
Modern stack without the overkill
We lean on tools like Azure, Fabric, Power BI, dbt and Fivetran instead of multi-year data platform science projects.
End-to-end delivery
From raw tables to AI copilots
We design the model, build the pipelines, ship dashboards and wire AI into the apps your planners and operators already use.
Built around decisions
Every metric must drive a move
We anchor everything to concrete questions: what to buy, what to move, what to expedite, who to prioritize.
Typical initiatives we lead
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Single source of truth for orders, inventory, shipments and margin.
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Executive dashboards for OTIF, fill rate, forecast error and working capital.
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AI copilots that answer supply-chain questions inside Teams, email or ERP.
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Predictive models for demand, inventory risk, stockouts and late shipments.
What we actually build for you
Less buzzword, more analytics-driven outcomes. These are the components we deliver again and again for supply-chain and operations teams.
Advanced analytics
Deeper insight through statistical modeling and richer metric layers.
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Time-series forecasting and anomaly detection
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Advanced margin, service-level and cash-flow analytics
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Scenario modeling and sensitivity analysis
Dashboards & self-service analytics
The views your team checks every single day.
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Executive OTIF and service-level views
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Planner views for supply, demand and constraints
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Warehouse, logistics and carrier performance
Automation & closed-loop workflows
Insights that trigger the next best move automatically.
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Ticket and task creation from alerts
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Approval-based auto-replenishment suggestions
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Integration back to ERP, WMS and planning tools
Data pipelines & integration
Reliable, scheduled flows from your operational systems.
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Ingest from ERP, WMS, TMS, e-commerce and 3PLs
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Incremental loads and change-data capture
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Orchestration, monitoring and alerting
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Predictive & optimization models
See around corners instead of reacting late.
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Demand forecasting and inventory risk scoring
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Fill-rate and stockout risk alerts
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Late shipment and carrier performance prediction
Central data model & warehouse
One schema everyone can trust.
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Dimensional models for orders, inventory and shipments
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Semantic models for Power BI and other tools
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Role-based access for finance, sales and ops
AI copilots & assistants
LLMs that speak in your data and your language.
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Chat interfaces for supply-chain Q&A
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Natural-language filters and drilldowns
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Action suggestions inside Dynamics, email or tasks
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Your DataShip supply-chain data fabric
We don't replace your systems. We connect and model them so they behave like one brain for planning, execution and decision-making.
Logical layers:
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Source systems — Dynamics 365, SAP, Fishbowl, Blue Yonder, Kinaxis, Shopify, Amazon, 3PL portals, carrier APIs.
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Ingestion & pipelines — ELT jobs, CDC, orchestration and quality checks.
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Warehouse & models — fact / dimension models for orders, inventory, shipments, customers and SKUs.
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Analytics & AI — dashboards, forecasts, anomaly detection, LLM copilots and alerting.
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Actions & automation — tickets, tasks, workflows and write-back into ERP, WMS and planning tools.
Example end-to-end flow
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Orders & inventory sync from Dynamics 365, Shopify and 3PL.
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Pipelines clean, join and publish a unified "Order" and "Inventory" model.
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Power BI dashboards show OTIF, service levels and inventory risk by channel and customer.
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AI copilot lets planners ask: "Where are we likely to stock out in the next 14 days?"
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Automation pushes replenishment suggestions and tasks into ERP and planning boards.
12–16 Week Data Engineering & AI Program
A focused engagement that gets your key data modeled, your first dashboards live and your initial AI copilots in front of users.
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Designed so you can see value in weeks, not years.
Sprint 1
Week 1
Discovery & use-case framing
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Align on 3–5 critical decisions to support
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Map source systems and current reporting
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Define success metrics and constraints
Sprint 2
Weeks 2–4
Pipelines & data model
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Stand up ingestion and core tables
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Design and build dimensional models
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Implement basic data quality checks
Sprint 3
Weeks 5–7
Dashboards, analytics & insights
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Ship core executive, planner and analytics insights views
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Iterate with stakeholders on usability
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Introduce alerting on key thresholds
Sprint 4
Weeks 8–12
AI & automation layer
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Deploy initial AI copilots for Q&A
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Wire automations into ERP / workflow tools
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Handover, training and backlog for phase 2
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What changes for your team
After a Data Engineering & AI engagement, your people spend far less time hunting for numbers and far more time making calls.
1 source of truth
For operations, finance and sales
Everyone is looking at the same numbers, refreshed automatically instead of manually stitched together.
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Hours → minutes
To answer critical questions
From "we'll send a report tomorrow" to typing the question into a dashboard or copilot and getting an answer now.
+10–20%
More leverage on inventory & working capital
Better visibility into where cash is stuck and what can be rebalanced, delayed or accelerated.
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Mini case studies
Examples of how we turn messy operational data into leverage for leadership and planners.
CPG brand with e-commerce + wholesale
Leadership could not reconcile DTC, Amazon and wholesale performance in a single view.
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Unified margin view by channel, customer and product
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Executive dashboard refreshed hourly instead of weekly
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Alerts for negative-margin orders and customers
Apparel company scaling 3PL footprint
Operations team lived in spreadsheets to reconcile ERP, WMS and 3PL data.
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Pipelines pulling data from ERP, WMS and 3PL every 15 minutes
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Inventory and OTIF consistency improved to 99%+
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Time spent reconciling data cut by 80%
Distributor with planning bottlenecks
Planners were overwhelmed with fire drills and manual follow-up.
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Forecast and risk models highlighting true problems
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AI copilot to answer "what if" questions on the fly
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Better service levels with less chaos in the day-to-day
Data Engineering & AI strategy session
Ready to turn your data exhaust into an advantage?
Book a 30-minute working session with the DataShip team. We'll map your top decisions, identify the data you already have, and outline a concrete 8–12 week roadmap.