Now accepting projects — Q3 2026

Retail & Consumer Goods: Custom AI Solution

Retail AI opportunities are usually concrete: 80% of service questions are the same 20 issues, recommendations beat hand-curated lists, and personalised loyalty marketing lifts AOV. We start from operating reports and build the measurable levers first.

// AI use cases to start with

Service RAG and triage

Feed return policies, loyalty rules, promotion terms, and store FAQs into RAG to auto-answer the top 80% of questions; route the rest to humans with summarised context.

Product recommendation and marketing personalization

Use CRM + purchase history + behaviour signals to build recommendations and audience segments better than “best sellers” and generic EDM — billions of records are not required.

Order / receipt automation

Order anomaly detection (amount, address, return frequency) and receipt / invoice extraction to remove manual time from reimbursement and store reconciliation.

// How EKel would deliver it

01Pick one measurable KPI — first-response time, recommendation conversion, loyalty open rate, or anomaly capture rate.
02Build a retail reference dataset from real service records, member behaviour, order anomalies, and return cases.
03Connect AI into existing CRM / POS / e-commerce systems instead of standing up another back office nobody uses.
04Operate with dashboards, human sampling, and logs after launch; run an outcome review every sprint.

// Best fit

→Retail brands with high service volume, tens of thousands of members, and cross-channel order and return flows.
→Teams that want AI to enhance existing service, loyalty marketing, or product recommendation flows.
→Organizations that want to prove ROI in one sprint before buying a complete AI platform.

// Sources

Salesforce Retail Salesforce Agentforce for Industries

Back to industries See retail Salesforce →

// Custom AI architecture

Retail AI is not another dashboard — it is a four-layer customer-bound stack.

// LAYER L4

User layer

Service agents, members, marketing, store managers — interacting via web, mobile, agent desk, LINE bot. Every interface keeps a human-review escape hatch.

Web · MobileAgent deskLINE · Messenger

// LAYER L3

Application layer

Built with **Vibe Coding** — custom apps embedded in service RAG / recommendation / member marketing / anomaly detection / receipt extraction. Multi-step flows (e.g., "member return → auto-compensation → re-recommendation → marketing trigger") run as **agentic workflows**. One use case, one KPI — no “unified AI platform.”

Vibe CodingAgentic WorkflowCCaaS hooks

// LAYER L2

AI layer

LLM Gateway + RAG (policies, FAQs, products, member rules) + recommendation model + eval pipeline + guardrails. Models swap; guardrails do not.

LLM GatewayRAG · RecoEval · Guardrails

// LAYER L1

Data layer

Vector DB + structured data (member, order, product, return) + document store + full audit log. Member PII classification decides what may go to commercial LLMs.

Vector DBMember · OrderAudit log

// Pre-launch ops checklist

Six compliance and quality red lines for retail AI.

Member PII classification

Which member fields (name / phone / address / purchase history) may go to commercial LLMs vs only private deployment? Without privacy-law and GDPR clarity, do not start.

Recommendation explainability

Why these 3 items for this customer? AI must answer (which signals, which history). Marketing / compliance / member complaints all need this. Black-box recommendation cannot be debugged.

Promo rules vs AI boundary

Discount bounds, tier limits, bundle logic — these belong in a rules engine, not in AI. AI handles “what to recommend,” not “how deep the discount can go.”

Return / refund anomaly

High-frequency returns, cross-address repeat refunds, anomalous amounts need ML model + rules + human review in three layers — AI alone false-positives real customers; rules alone miss novel fraud.

Hallucination monitoring

After service RAG launches, continuous sampling (auto-eval + human review). Strict thresholds on binary answers (product spec, return policy) — one wrong reply may surface as a public complaint.

Eval & reference dataset

Before launch, run 100+ real retail scenarios (FAQ, returns, product comparison, member rights, promo rules). Re-run on every model version. No pass, no ship.

// FAQ

Five questions clients ask most about retail AI.

01Will AI recommendation actually beat best-seller sorting?

When customers have 1+ year of purchase history and the member base is 10k+, yes. Best-seller recommends the same item to everyone; AI uses collaborative filtering + content embeddings + personal preferences to surface what this specific customer might buy — conversion typically lifts 30–100% over baseline. With sparse data and small member counts, best-seller may still win — AI without data is essentially guessing.

02What if service RAG answers incorrectly? Will customers complain?

Yes, they will. Two layers: (1) define what AI can answer (FAQ / product specs / return policies — yes; specific order details / complaint escalation — no); (2) high-consequence answers (returns, member rights, promo rules) carry a “AI-summarised, agent confirms” disclaimer. In practice, 80% of service inquiries are the same 20 questions — RAG hits 95%+ accuracy on those, the rest route to humans.

03Our product and member data are limited — does AI still apply?

It depends on the use case. Service RAG and best-practice lookup don’t need millions of records — just structured policy with clear sources. Product recommendation suffers with sparse data; start with collaborative filtering + content-based, and upgrade when data accumulates. Document extraction (KYC, receipts, invoices) doesn’t depend on your data volume at all — frontier models handle individual documents directly.

04How to balance member PII against commercial LLM use?

Don’t send PII to commercial LLMs as a baseline. Redact before prompting: name → [USER], phone → [PHONE], address → [ADDRESS] so the LLM sees only anonymised context. Retrieval results go through the same filter. When a use case truly needs PII (e.g., generating a name-personalised EDM for one member), route through a private deployment or compliance LLM gateway — not commercial.

05Why not use off-the-shelf retail AI products?

If off-the-shelf fits, buy it — engineer’s judgement, not consulting line. Custom is right when: (1) the brand’s member rules / product schema / promo logic don’t match the SaaS schema; (2) deep integration with proprietary POS / e-commerce / CRM is required; (3) AI behaviour itself is the brand differentiator (“our service voice / recommendation logic is distinctly ours”). The first two are common at mid-sized and larger retailers.

Retail AI starts with one workflow and one KPI.

We can design the first AI sprint from your real service and order data, then decide whether to expand into recommendation, loyalty, or automation next.

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