Industry

E-commerce AI,
built on order data.

Merchandising, search, support and forecasting for retail, built against the order data that holds the answer — with a person committing every stock and budget decision.

WHERE IT BREAKS TODAY

Margin and acquisition cost

Every paid click costs more than it did last year while conversion rates stay flat, so the levers left are the ones inside the store: search, merchandising, retention and the cost of support.

Static storefronts

Most platforms show every visitor the same catalogue in the same order, and leave the customer to do the work of finding the product that would have converted.

Support cost that scales with revenue

Support cost grows in step with orders, and most of that contact is about a specific order — a question a help-centre chatbot cannot see, let alone answer.

OUR APPROACH

Integration first, then the model

Almost everything that matters in retail AI depends on what the system can see. A support agent that can read order status, carrier tracking and returns history — and take a bounded set of actions within a value limit — resolves a large share of routine contacts; the same model over a help centre resolves very few. Search that knows which products actually sell together outperforms search that only knows the product text.

So we start with the integration, and we measure rather than quote. Deflection is measured on your own ticket history. Forecast quality is measured against your sales record. The system proposes stock and budget rebalancing; a planner confirms it, because an automated commit converts a data error into a financial one.

HOW IT DEPLOYS

PHASE 01

Unify the stack

Storefront, ERP, 3PL and CRM are connected into a single middleware layer, so the system reads real order and stock state rather than a copy of it.

PHASE 02

Wire the funnel

Order-aware support, personalised search and long-tail landing pages go live, each with a permission envelope, a quality gate and an audit record.

PHASE 03

Measure, then extend

Forecasting models watch purchasing patterns and ad spend, and propose stock and budget rebalancing for a planner to confirm — with the result measured on your own figures.

WHAT WE BUILD

Order-Aware Support Agents

Agents wired into order management, carrier tracking and returns, taking bounded actions within a value limit — process a return, reissue a label, update an address before dispatch — in the languages your catalogue and your customers use.

SEE CUSTOMER SERVICE →

Long-Tail Landing Pages

Large numbers of specific landing pages and product descriptions targeting long-tail search, with quality gates so they are indexable rather than flagged as scaled content.

SEE PROGRAMMATIC SEO →

Demand Forecasting

Forecasting models that read purchasing patterns, seasonality and ad spend to predict inventory requirements and inform return on ad spend, with the proposed action left to a planner.

SEE DEMAND FORECASTING →

Cross-Platform Syncing

Middleware that keeps storefront, ERP, 3PL and CRM in step, reconciling inventory and order state with exceptions routed to a person rather than silently overwritten.

SEE CROSS-PLATFORM WORKFLOWS →

CONTROLS WE IMPLEMENT

GDPR-ALIGNED CONTROLS CCPA-ALIGNED CONTROLS NO CARD DATA IN THE AI PATH CONSENT-AWARE PERSONALISATION NO TRAINING ON YOUR DATA

These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and the audits and attestations your sector requires remain yours to run — see our trust centre.

DESIGN TARGETS

7-day

REOPEN WINDOW ON DEFLECTION

RETRIEVAL SOURCES: DOCS + ACCOUNT STATE

100%

ANSWERS CARRYING A CITATION

Properties we design and build to, not measured client outcomes.

E-COMMERCE USE CASES

ECM.01 Merchandising & Site Search Semantic search, ranking and recommendation over a catalogue that changes faster than a model can be retrained. → ECM.02 Demand Forecasting Forecasting inventory and promotion response, where the loss function is asymmetric and rarely modelled that way. → ECM.03 Retail Customer Service Agents Order-aware support agents wired into the systems that hold the answer, not a FAQ with a chat window. →

QUESTIONS

QUESTIONS — 4

It depends almost entirely on integration depth rather than on the model. An agent wired into order management, carrier tracking and returns resolves a large share of routine contacts, because most retail contacts are about a specific order; one built over a help centre resolves very few. We scope the integration and measure the rate on your own ticket history rather than quoting one.

Generating large numbers of specific landing pages and product descriptions to target long-tail search — the thousands of low-competition queries a catalogue can answer precisely. Done without quality gates it produces pages that search engines treat as scaled content; done with them, each page has to earn its place by being genuinely useful for its query. We build the gates first, and we measure indexation and organic traffic rather than promising either.

Forecasting models read purchasing patterns, seasonality and ad spend together to predict inventory requirements and to inform return on ad spend. The system proposes stock routing and budget rebalancing; a planner confirms it. Forecast quality is measured against your own sales history before anything is acted on.

Yes. Middleware connects Shopify or another storefront, ERP systems, 3PL providers and CRM platforms, reconciling inventory and order state across them with exceptions routed to a person. It typically requires no platform migration; the integration is usually the larger part of the build.

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30-minute technical call · no deck