Every basket is a model output.

Retail AI is unforgiving: latency is conversion, relevance is revenue, and a bad recommendation is a lost customer. We build search, pricing, forecasting, and CX systems measured the only way retail respects — on the P&L.

Discuss your use case →What we build

quantpi · industry/telemetry

$ industry.constraints()
✓ metrics: conversion · AOV · forecast error · latency < 100ms
✓ deployment: cloud · hybrid · air-gapped
✓ proof standard: measured on your data
# domain constraints are design inputs, not blockers

IND/01What's at stake

In retail, model quality has a daily P&L.

A 5% search-relevance improvement isn't a metric — it's revenue you can read in tomorrow's dashboard. Equally, every 100ms of added latency taxes conversion. Retail AI engineering is therefore ruthless about two things: measurable lift through controlled experiments, and serving budgets that protect the experience.

IND/02What we build here

Use cases we ship

Semantic & hybrid product search

Vector + keyword search with merchandising controls — synonyms, intent, typos handled; business rules respected; results explainable to category managers.

lift: A/B-measured

Demand forecasting

SKU-by-location forecasts with promotions, seasonality, and cannibalization modeled — measured on stockout and waste reduction.

granularity: SKU × site

Dynamic pricing engines

Elasticity-aware pricing within guardrails you set — margin and competitiveness balanced, every price defensible.

guardrails: merchant-set

Recommendation systems

Session-aware recommendations tuned to the metric you choose — AOV, attach rate, or discovery — with controls for fairness across catalog.

tuning: metric-explicit

AI customer experience

Order, returns, and product Q&A grounded in your catalog and policies — deflection without the brand damage of a hallucinating bot.

grounding: catalog + policy

Review & feedback intelligence

Aspect-level mining of reviews and support contacts — quality issues and assortment gaps surfaced weekly, not quarterly.

cadence: weekly signal

IND/03Domain constraints we design for

Built for your constraints

Latency budgets enforced (search < 100ms p95)
Controlled experiments (A/B) as the standard of proof
Catalog-scale embedding refresh without recompute blowouts
Merchandising and compliance rules layered over ML
Peak-season load tested before peak season
Privacy-conscious personalization (consented data only)

IND/04Questions, answered straight

FAQ

How quickly can search improvements show revenue impact?

Fast — search is the highest-velocity AI investment in retail. A hybrid-search upgrade typically reaches A/B test within 6–8 weeks, and relevance lifts of 5–15% on conversion-weighted metrics are common when replacing pure keyword search. Your baseline determines the headroom; we measure it first.

Can you work with our existing platform (Shopify, commercetools, custom)?

Yes — we integrate via APIs and event streams rather than demanding replatforming. Search, recommendations, and pricing all deploy as services alongside your existing stack.

How do you handle the fashion/seasonal cold-start problem?

With content-based signals: new items inherit behavior from visual and attribute similarity until they earn their own interaction data. For forecasting, analog-item methods and attribute-level models bridge the gap between launch and statistical maturity.

Dynamic pricing sounds risky for brand trust. How do you control it?

Guardrails are the product: floor/ceiling bands, change-frequency limits, competitor-match rules, and category exclusions you define. Every price movement is logged with its rationale — auditable by your team and defensible to your customers.