From a demand signal
to margin you actually capture.
Intlix connects demand, inventory, catalogue, pricing, fulfilment and service signals so retail teams can move while the commercial opportunity is still live — not after the next batch report lands.
How we create value
Industry context changes the engineering.
The operating friction
Demand, catalogue, inventory, campaign and fulfilment signals move at different speeds in separate systems. By the time the picture is complete, the customer or the stock position has already changed.
We use that friction to define the context, integration and controls the engineering has to solve — rather than handing over a generic list of use cases.
How Intlix intervenes
Engineer backward from the outcome.
Start from the decision
Map the workflow, the people in it, the systems it touches, and the measure it is judged on.
Assemble the context
Connect the data, documents and systems that decision actually depends on.
Put intelligence in the path
Apply models, agents and automation where they improve the call — not wherever they fit.
Run it in the open
Keep human authority, evaluation, observability and running cost visible after go-live.
The operating journey
Six steps, one accountable chain.
Sense
Market, customer and demand signals
Plan
Demand and merchandise context
Position
Inventory and supply placement
Activate
Price, promotion and channel
Serve
Order, fulfilment and support
Learn
Outcome, quality and economics
What we build
Four engineering motions.
One operating outcome.
Modernize commerce data
Real-time pipelines, customer and product context, governed data products.
Teams work from a current operating picture, not last night's extract.
Engineer demand and customer intelligence
Forecasting, recommendation, merchandising and service intelligence.
Signals translate into specific, assignable decisions.
Connect commerce workflows
Pricing, inventory, campaigns, fulfilment, support and returns.
Intelligence moves the commercial action instead of stopping at a dashboard.
Run and optimize
DataOps, AI observability, experimentation and governance.
The signal-to-action loop gets tighter with every cycle.
Let's talk about your retail / cpg workflows.
Bring us the decision that is slow, manual or hard to defend. We will map what it would take to engineer it properly.
Start the conversationSector questions
What retail / cpg teams
ask us first.
How do you handle demand forecasting at SKU level?
With models that account for promotions, cannibalization and substitution rather than treating each line independently, and that degrade gracefully for the long tail where history is thin.
Can this work across channels?
It has to. Inventory, pricing and fulfilment decisions made per channel are the usual reason online and store availability disagree.
What about personalization and privacy?
Consent and purpose limitation are enforced in the data layer, so a customer's preferences cannot be used for something they did not agree to, regardless of what a downstream team requests.
How quickly can we see results?
Forecast accuracy and availability improvements are measurable within a season. We agree the baseline before starting so the comparison is honest.
Do you integrate with our ERP?
Yes. The ERP stays the system of record; we add the decision layer around it rather than duplicating its data.