How can ecommerce teams use RAG for internal knowledge?

Key facts

API surface	OpenAI-compatible /v1/chat/completions; keep your existing SDK
Grounding	Embeddings and RAG are live for product, policy and supplier knowledge
Models	30+ models behind one API, from free tiers to frontier
Deployment	Plugsky cloud, your VPC, on-prem or air-gapped
Access control	Scoped API keys, rotation and usage analytics; enterprise SSO and RBAC options
Endpoint roadmap	Audio, images, moderation, batch and fine-tuning are coming soon
Pricing model	Flat monthly self-serve plans; no per-token billing on self-serve
Free tier	Free plan with plugsky-micro and plugsky-lite; 14-day full-access trial

TL;DR

• Ground ecommerce answers in approved internal content with RAG instead of model memory.
• Keep customer or supplier content inside VPC, on-prem or air-gapped deployments where policy requires it.
• Scope retrieval per team, client or site so permissions and confidentiality hold at query time.
• Keep a named human owner for every customer-impacting decision.
• Re-embed on catalogue and policy changes so answers stay current.

How it works, step by step

1. Inventory the content to make searchable: product catalogues, return and shipping policies, supplier documentation and support macros.
2. Define a data boundary for the pilot that excludes restricted material until controls are proven.
3. Choose a deployment target: cloud for public content, VPC, on-prem or air-gapped for restricted data.
4. Build an evaluation set with support leads and merchandisers so answer quality is judged by domain experts.
5. Ingest, chunk and embed approved documents, and require citations on every answer.
6. Add refusal behavior for questions outside the indexed, approved content.
7. Review logged interactions on a schedule and expand only after accuracy and access checks pass.

Original data

Try it yourself

Open the RAG sandbox →

Where ecommerce teams start

Start with internal questions that already have a written answer. High-value first workloads include:

• Support agent assist: draft replies from policy, shipping and returns content.
• Product knowledge: answer specification and compatibility questions from catalogue data.
• Supplier and operations docs: retrieve vendor terms, lead times and compliance notes.
• Merchandising guidelines: surface category rules, pricing policies and content standards.

Each use case augments staff with cited answers; none replaces support team lead judgment.

A private RAG architecture for ecommerce knowledge

The stack is consistent across industries: ingest approved product catalogues, return and shipping policies, supplier documentation and support macros, chunk and embed with a multilingual embedding model, store vectors inside your environment, and call chat completions that answer only from retrieved context. Plugsky embeddings and chat completions are OpenAI-compatible, so teams already using OpenAI SDKs change the base URL and keep their code.

Catalogue data changes constantly: schedule re-embedding on product and policy updates, and prefer metadata filters for category, region and status alongside vector similarity. Use JSON mode for structured outputs into support tooling.

Access control, confidentiality and audit

Commercial data, supplier terms and customer details require separation. Filter retrieval by function such as support, merchandising or finance, and keep personally identifiable order data out of the assistant corpus. Policy answers that affect a customer should be reviewed by a human before they are sent.

The technical controls are consistent: enforce permission-aware retrieval in your own service layer, scope API keys per application, team or tenant, rotate keys, and retain request and response logs on a defined schedule. Regulatory obligations vary by jurisdiction and sector, so map them with counsel rather than assuming one framework covers every deployment; Plugsky supplies the deployment and logging primitives you document.

See the RAG API for related deployment and control detail.

Rollout and human oversight

Pilot on published policy and catalogue content before touching customer or supplier content. Build a labelled question set with support leads and merchandisers, then measure retrieval hit rate, citation correctness and answer accuracy before and after every index or model change. Require citations on every answer, refuse out-of-scope questions, and name a human owner for every customer-impacting decision.

Review logged interactions weekly at first, correct the index rather than the prompt when retrieval misses, and expand the corpus only when accuracy and access checks pass.

Honest comparison

Capability	Plugsky	Public AI assistants	Building in-house
Data boundary	Cloud, VPC, on-prem or air-gapped	Vendor cloud only	You control fully
Grounding	Embeddings and RAG are live for product, policy and supplier knowledge	Uncontrolled retrieval	You assemble and operate
Access control	Scoped keys, usage analytics, enterprise SSO and RBAC options	Account-level only	Custom identity work
Auditability	Request and response logging	Limited	You build logging
Pricing	Flat monthly self-serve plans; see live pricing	Per-seat or per-token	GPU plus operations cost
Time to pilot	Days	Hours, without residency control	Quarters

Frequently asked questions

Can ecommerce teams keep data private with Plugsky?

Yes. Choose the deployment boundary that matches the data: Plugsky cloud for public content, or your VPC, on-prem and air-gapped options for restricted material. Access is controlled with scoped API keys and usage analytics.

Do we need to fine-tune on our internal documents?

Not for a first release. Fine-tuning is coming soon and is better for style than facts. RAG keeps answers current, permission-aware and traceable to a source, which matters more for internal knowledge.

Which model should we use?

Start free with plugsky-micro and plugsky-lite to validate retrieval, then evaluate mid-tier and frontier models from the 30+ model catalogue on your own question set.

How do we stop wrong or unsupported answers?

Restrict the assistant to approved indexed content, require citations, refuse out-of-scope questions and keep a human decision-maker for every regulated or client-facing outcome.

How is pricing structured?

Self-serve plans are flat monthly with unlimited fair-use usage, and there are no per-token charges on self-serve plans. See the live pricing page for current plans and the free tier.

Can it draft customer support replies?

Yes, from approved policy and product content. Route the draft through your existing review or QA process, especially for refunds, warranty and compliance questions.

How do we keep catalogue answers current?

Re-embed on product and policy changes, version chunks, and filter by status so discontinued or region-restricted items do not appear.