Retail.
Cookieless personalisation that out-converts the old stack.
Retail growth runs on first-party data now: acquiring shoppers, personalising every visit, and earning repeat purchase, even as AI agents insert themselves between store and shopper. The cookie era is over; the winners own the customer data and never leak a single identifier.
- R01ePrivacy and TCF 2.2 enforcement across EU markets
- R02Loyalty and purchase data leaking through marketing-platform integrations
- R03Personalisation drift into automated decision-making under GDPR Art. 22
What's actually
reshaping the sector.
The shifts our research is tracking, named, current, cited.
During Cyber Week 2025, AI and agents drove $67B in sales and influenced 20% of all global orders (Salesforce). The agent is inserting itself between store and shopper: win the transaction, or be disintermediated.
Salesforce →Adobe found traffic to retail sites from generative-AI tools rose sevenfold year-over-year over the 2025 holidays, and AI-referred shoppers convert at higher rates and spend more per visit. The acquisition channel has changed shape.
Adobe (via Digital Commerce 360) →McKinsey finds personalisation leaders generate 40% more revenue from it, and done right it cuts customer-acquisition costs up to 50% and lifts revenue 5-15%, on first-party data you own.
McKinsey →Zalando, Europe’s largest online retailer, runs its AI infrastructure on European-founded Hopsworks; Carrefour built one of Europe’s largest retail data lakes; Schwarz’s Stackit offers EU sovereign cloud.
Zalando / Hopsworks →In retail, your edge is first-party data the competition cannot buy, and AI that never leaks it.
The agentic CDP will personalise everything. Own the data it runs on.
Retail's old growth engine, rented audiences stitched from third-party cookies, has collapsed. The new one is agentic: continuous 1:1 personalisation by agents acting on first-party data, the model Databricks and Hightouch are racing to define. The winners own that data outright and activate it without leaking a single identifier.
Run that personalisation on a sovereign engine and the old stack's costs, the perpetual leak, the banner that taxed every journey, disappear, while conversion rises on cleaner first-party signal. Wait, and you watch a competitor run agentic 1:1 marketing while you plan campaigns by hand.
We rebuild collection first-party and server-side, plug in a composable, no-copy CDP, and keep the decisioning on sovereign inference. You get personalisation that out-converts the banner era and never exports who your customer is.
Where each AI workload
should live.
| Workload | Placement | Rationale |
|---|---|---|
| Acquisition & personalisation engine | Sovereign | First-party loyalty data is the moat. |
| Loyalty & retention decisioning | Sovereign | Repeat-purchase signal that must not leak. |
| On-site shopping agent & service | Hybrid | Frontier reasoning, sovereign customer memory. |
| Product content & creative | Frontier | Non-personal, throughput-led. |
What is yours to encode,
and what you are renting by accident.
Before any platform decision, one question settles the rest: how much of what your organisation knows can be written down precisely enough for a machine to act on it, and who ends up owning that writing. We run a short engagement to answer it, and it produces working artefacts rather than a report.
Two artefacts come out of this. An operating ontology, the nouns: the entities, relationships, and states your business actually runs on, described once and precisely. And agentic skills, the verbs: the procedures, thresholds, and judgments your best people apply, written down, versioned, and testable instead of retold.
A placement map addressed to the CTO or CIO office: what runs on your compute, what is bought, what is rented, who holds the keys, and what each costs. One test cuts through most of the debate. If a supplier vanished on a Friday, what stops working on Monday, and how long would it take you to replace it?
Where an open-weight model fine-tuned or adapted on your own corpus beats a frontier call, where a small purpose-built model for one narrow task beats both, and where the frontier still earns its fee. Geographic sovereignty falls out of that answer rather than having to be argued for on its own.
- →Brand voice, and the rules that make copy sound like you rather than like a model
- →Assortment and substitution logic: what may replace what, and when
- →Promotional guardrails: the discounts you will not run, and the reasons why
- →Clienteling: how your best associates open, follow up, and close
- →Supplier and quality standards applied at intake
An image model adapted on your own product photography and lookbooks generates on-brand assets at zero marginal cost, without a season in development becoming training data for the open web.
- +A register of encodable know-how, ranked by value and by how fast it decays
- +A draft operating ontology in an open format, not inside a vendor's platform
- +Two or three working skills, built and evaluated on your real work, not demonstrated on a slide
- +A bring-it-in-house plan the CTO or CIO office can actually staff
- +A model strategy: open weights, fine-tuning, purpose-built small models, and where the frontier stays
- +An evaluation harness that shows it works, and keeps showing it after we leave
This is a CTO and CIO office engagement, not a procurement exercise. Your own people have to end up running it, because the moment the encoding is delegated, the thing being encoded quietly stops being yours.
Palantir calls this layer an ontology, and the idea is right: a governed model of your objects, links, and actions that agents can act on. It lives inside their platform. 8090 will design, build, host, and maintain the software around it for you. Both are serious, and both leave the same question open. At the end of it, who owns the layer that holds your judgment? We build the same artefacts in open formats, on compute you control, and hand your CTO the keys.
Your brand's creative engine,
on hardware you own.
The same four layers we ship in legal, instantiated for a retailer. The change that matters most here is on the creative side: generation can now run locally, which means campaign work stops being an upload.
A clienteling copilot store staff can actually use on the floor, a creative studio that generates on your own product imagery and brand archive, and promotion and assortment decisioning wired to first-party demand.
Tone of voice, product knowledge, styling and substitution rules, service standards, and promotional guardrails compiled into skills, so every generated asset and every recommendation starts from the house position rather than from a generic model's guess.
Loyalty, basket, and clienteling data never leaves. Image and copy generation on unreleased product runs locally, so a season's line does not become somebody else's training data, and only finished, non-confidential work goes outward.
A box in the studio or the head office for creative generation, a managed instance for the decisioning layer, or both, depending on where the sensitive half of the work sits.
The application layer here is built for you, not bought. The pattern is the one we already ship, as a product, in legal.
Four postures.
One codebase.
The same software in every posture, so a team can start hosted and end up air-gapped without a migration project. You move along the ladder when you decide to, not when a contract renews.
Your stack stood up on our infrastructure in days and pointed at real work, so it is judged on your matters rather than on a demo. The pilot fee credits against the hardware when you migrate.
Teams that want proof before capital expenditure.
The open-source cores installed on a machine you own, by a script anyone can read first. No account with us, no telemetry, no phone-home. Code flows one way, from us to you, and only when you ask for it.
Small teams, and anyone who wants to inspect the source before trusting it.
An isolated instance per client on sovereign infrastructure, European by default, run, patched, and monitored by us, with the security and backup credentials in your hands rather than ours.
Organisations without an infrastructure team to spare.
Dedicated AI hardware inside your building, with the applications and the open-weight models you choose installed and tested before it ships. It runs with the network unplugged, and it keeps working whatever happens to us.
For a retail creative team the appliance is the point: unlimited iteration on unreleased product, at zero marginal cost per image, with nothing uploaded anywhere.
Work that cannot leave the building, ever.
- +Deployment and hardening, on your machines or ours
- +Knowledge encoding: your playbooks and positions compiled into installable skills
- +Model selection, tuning, and an evaluation harness built on your own work
- +Remote maintenance and monitoring at a published monthly rate
- +Updates as ordered, readable migrations, always after an encrypted backup
- +Backup, restore, and continuity drills you can actually rehearse
- +Enablement for the people who use it daily, not only for IT
- +Governance evidence: routing logs, audit trail, and an attestation pack
- Reach into your instance or see your work
- Revoke your software or disable a licence remotely
- Force an update on you
Not promises. Consequences of the architecture: your keys and your data are generated on your machine and stay there, and nothing calls home.
Get the full
retail
placement map.
We send a sector-specific brief with workload-by-workload placement guidance and a reference architecture for sovereign deployment.