Wholesale, manufacturing & distribution
Forecast demand. Fix your data. Automate the orders.
Most AI fails in operations because the data underneath is scattered across ERP exports, spreadsheets and inboxes. We start with the data: reliable pipelines first, then forecasting and automation your planners can trust.
- Wholesalers & distributors
- Manufacturers
- Inventory-heavy businesses
What we build
Where AI pays off first
Demand forecasting
Forecasts by product, customer and location, with accuracy tracked every cycle so planners know when to trust them.
Inventory & reorder alerts
Early warnings for stock-outs and overstock, with suggested reorder quantities your team approves.
Order processing automation
Purchase orders from emails and PDFs read, checked and entered into your system, with exceptions flagged for people.
Maintenance signals
Early indicators of equipment issues from sensor and maintenance data, so problems are fixed before they stop production.
Data engineering first
- ETL pipelines that bring ERP, spreadsheet and system data into one trusted place
- Built on Databricks, Spark and SQL where they fit, and on your existing platform where they don't
- Data quality checks that catch broken inputs before they reach a forecast
- Forecast accuracy measured and reported, not assumed
- People approve orders and changes that matter
- Documented so your team can run and extend it
What you receive
- Automated data pipelines with quality checks
- A forecasting model with accuracy tracking against real outcomes
- Dashboards or alerts your planners use every day
- One automated workflow, such as order entry from email
- Documentation and handover to your team
How we deliver
Proof first. Then scale fast.
- Step 1
Discover
We learn your goals, data and constraints, and agree on what success looks like and how we'll measure it.
- Step 2
Prototype
A focused proof-of-concept on real examples shows what works before larger investment.
- Step 3
Build
We engineer the production system with your team: secure, tested and integrated with your tools.
- Step 4
Secure & launch
Security review, evaluation against agreed benchmarks, and a controlled rollout.
- Step 5
Improve
Monitoring, feedback loops and ongoing improvements as your needs and the models evolve.
FAQ
Common questions
Our data is messy. Can we still start?
Yes, and most businesses are in the same position. The first phase cleans and connects the data that matters most for one use case, so value arrives early instead of after a long data project.
Do we need Databricks?
No. We use Databricks and Spark when the data volume and team setup justify it, and work with your existing database or cloud platform when that's the better fit. We'll recommend the simplest option that meets the goal.
Can it connect to our ERP?
Usually, through exports, database connections or APIs. We review your ERP and systems in discovery and confirm the integration approach before building.
How accurate will the forecasts be?
That depends on your data and products, so we never promise a number upfront. We measure accuracy against your real history during the proof of concept, and you decide whether to proceed based on those results.
How long does a first project take?
A first pipeline plus one forecast or automation typically takes a few weeks to a couple of months, depending on data sources and integrations.
Ready to take AI past the demo?
Tell us what you want to build. We'll reply within one business day, and we're happy to sign an NDA first.