Automate eCommerce and logistics processes, using AI where it fits
I automate eCommerce and logistics processes with conventional integrations and AI, selecting the simplest approach that can produce a measurable outcome. AI is used only where a probabilistic step offers a verifiable advantage.
Describe the processProcesses to measure before automating them
Data copied between shop and ERP
Orders, records and documents move manually across systems without a clear measure of errors and rework.
Catalogues to classify or enrich
Attributes, descriptions and translations require review rules and a path for uncertain cases.
Tickets and exceptions to route
Requests and anomalies need classification without removing uncertain cases from human operators.
Documents to read and verify
Delivery notes, invoices, orders and attachments contain data to extract, validate and reconcile with operating systems.
Where rules, integrations or AI belong
Scope is confirmed only after measuring the problem and checking data, systems and the consequences of an error.
Deterministic automation
Rules, APIs, queues and controls for steps that must produce a predictable result.
Catalogue enrichment
Classification, attributes and content drafts with review criteria and validated output.
Ticket and exception classification
Assisted routing with thresholds, uncertain cases and escalation to a person.
Document extraction
Data from PDFs, email or scans checked before it enters an operational system.
AI inside existing systems
Focused functions connected to eCommerce, ERP, PIM or WMS without imposing a general new platform.
Quality and cost monitoring
Errors, uncertain cases, usage and costs become observable signals for keeping or correcting the automation.
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Describe the processHow the work proceeds
1. Measure the problem
We define current cost, volumes, errors, available data and process ownership.
2. Select the approach
We separate deterministic steps from those where AI may help, including limits and alternatives.
3. Run a bounded pilot
We deliver a verifiable scope with success criteria, validation and escalation for uncertain cases.
4. Produce and monitor
Quality, cost, errors and human intervention are monitored before extending the flow.
Technical responsibility rather than an AI promise
I bring more than 20 years of experience across eCommerce, integrations and operational software. I personally lead analysis, architecture, implementation and verification: AI is a selective tool inside a governed process, not a universal replacement for development or human judgement.
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