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Study AI in retail contexts
Examine how AI methods — demand models, recommendation engines, computer vision, NLP — perform in real retail environments with real constraints.
Applied AI research for retail technology · Belgium and Europe
Where AI meets retail technology.
Retail AI Research vzw (in formation) is a research association focused on AI applications in retail technology. We study how AI tools and methods can be applied responsibly in retail — and publish what we learn openly.
Applied AI research ✳ retail technology ✳ consumer intelligence ✳ demand forecasting ✳ store automation ✳ AI validation ✳ retail science ✳ open methods ✳ applied AI research ✳ retail technology ✳ consumer intelligence ✳ demand forecasting ✳ store automation ✳ AI validation ✳ retail science ✳ open methods ✳
Why we exist
AI is reshaping retail. The research needs to keep up.
AI applications in retail are advancing faster than the evidence base that should guide them. We close that gap: studying what AI tools actually do in retail contexts, under what conditions, and with what limits.
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Separate capability from claim
Distinguish what an AI system demonstrably does from what vendors or commentators assert it does. Evidence quality matters.
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Publish usable findings
Research that stays in journals is not enough. We produce open methods, benchmarks and field notes that practitioners and researchers can actually use.
AI research areas
Three applied AI research tracks.
We focus where AI application in retail technology is most consequential, most under-researched, and most in need of honest evaluation.
Track 01
AI for retail decision-making
How AI tools support — or distort — decisions about ranging, pricing, location, format and inventory. We study the gap between model output and actual retail outcomes.
Track 02
Consumer intelligence & behaviour
Applied AI methods for understanding consumer behaviour: what the data shows, where models break down, and how findings from one context transfer — or fail to transfer — to another.
Track 03
Retail technology evaluation
Structured evaluation of AI-powered retail systems — computer vision at shelf, autonomous checkout, demand forecasting APIs — with reproducible test protocols and open results.
Our method
The Exact Answering Machine
The Exact Answering Machine (EAM) is our structured research framework. Applied to AI research in retail: it forces a precise question, records the evidence the AI system produced, names the conditions and limits, and returns a conclusion the evidence can actually support.
Define what the AI is supposed to do.
For example: does this demand-forecasting model reduce stockout rate for short-lifecycle SKUs at a specific store type and assortment depth?
- State the decision and measurable success criteria
- Record data sources, model assumptions and constraints
- Define what the evaluation can and cannot prove
Report what the evidence shows.
Measured outcomes, failure modes and boundary conditions stay separate from vendor claims or prior expectations. The finding can be: adopt, pilot further, reject — or acknowledge insufficient evidence.
- Visible evidence, conditions and limitations
- No generalised claims beyond the test scope
- A replayable evaluation record
Responsible-use boundary: EAM supports research discipline and evidence management for AI evaluation. It does not replace legal, product-safety, data-protection, consumer-protection or commercial responsibility.
What we do
Applied AI research you can read, challenge and build on.
Our founding statutes define a public-interest mission: applied AI research for retail technology, open knowledge sharing, and carefully bounded field validation. Not product resale, not payment intermediation.
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Applied AI research
Research into AI methods and tools as applied in retail: demand models, recommendation systems, computer vision, NLP and automated decision systems in real retail environments.
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Open benchmarks & methods
Protocols, datasets, evaluation frameworks and methodological documents for AI in retail that others can reproduce, cite and improve.
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Learning & knowledge transfer
Workshops, public briefings and partnerships with researchers, retail organisations, local authorities and technology developers working on retail AI.
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Bounded field validation
Carefully documented field experiments — including pop-up and local-market tests — that evaluate AI tools in practice and publish methods, findings and limits transparently.
How we work
Better AI research makes better retail technology.
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AI capability, not AI hype
We show what an AI system did in a defined context — not what it might theoretically do at scale.
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Retail context is not generic
An AI model trained on one retail format, region or assortment type may not transfer. We treat context as a research variable, not a footnote.
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Open findings benefit everyone
Retail AI research locked behind proprietary walls benefits only the owner. We publish so the whole sector can learn.
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Limits are part of the result
An AI evaluation that finds no significant effect is a valid finding. We report it — not suppress it in favour of a better narrative.
Get involved
Bring a question worth researching.
We welcome early conversations with retailers, technology developers, researchers, local institutions and specialists who want to study — or challenge — how AI is being applied in retail.
Retailers & tech teams
Bring an AI evaluation question.
How is your AI tool actually performing? What conditions change the outcome? Transparent research can separate signal from noise.
Start a conversation ↗Venues & public partners
Contribute a site or dataset.
Real retail environments and real operational data are the hardest part of AI research. A venue, district or dataset can unlock a research question.
Share an opportunity ↗Researchers & specialists
Challenge the method.
Expertise in ML, retail operations, data engineering, consumer behaviour, law or ethics of AI in commerce — all perspectives sharpen the work.
Join the network ↗Formation notice
Retail AI Research vzw is in formation. The contact mailbox is provisional; legal registration, registered-office and enterprise-number details will be added here after incorporation.