How can retail companies use RAG for internal knowledge?

Key facts

API surface	OpenAI-compatible /v1/chat/completions; change base_url and model name
Models	30+ models including long-context and multilingual options
Retrieval	Embeddings and RAG are live for retail document search with citations
Agents	Function calling and agent orchestration are live
Deployment	Plugsky cloud, your VPC, on-prem or air-gapped
Data residency	Region selection and sovereign deployment options
Free tier	Free plan with 2 free AI models (plugsky-micro, plugsky-lite), no card
Endpoint roadmap	Audio, images, moderation, batch and fine-tuning are coming soon

TL;DR

• Put product catalogs, store manuals and supplier agreements behind a cited RAG assistant.
• Deploy in your VPC, on-prem or air-gapped so catalog and supplier data stays inside your boundary.
• Embeddings and RAG are live today; audio and image endpoints are on the roadmap.
• Function calling lets agents read PIM and support systems on request.
• Evaluate on the free plan, then move to a private deployment for production.

How it works, step by step

1. Inventory document sources: product catalogs, store manuals, supplier agreements, returns policies and service scripts.
2. Classify content by sensitivity and decide what may leave an on-prem or air-gapped boundary.
3. Create a Plugsky account and API key on the free plan, or request a private deployment.
4. Chunk and embed approved documents, keeping the vector store inside your own environment if required.
5. Wire retrieval into an assistant that cites sources and refuses out-of-scope questions.
6. Add function calling for PIM and support systems once retrieval is trusted.
7. Log queries and answers, review them weekly, and expand coverage document class by document class.

Original data

Try it yourself

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Where retail teams get value first

Start with document-heavy work that already has an owner and an approval path:

• Product knowledge for staff: answer spec and compatibility questions from the catalog with citations.
• Store operations: turn manuals and planograms into searchable guidance.
• Supplier terms: find lead times, minimums and terms across agreements.
• Policy consistency: give the same answer to return and pricing questions across stores and channels.

Each use case is retrieval-first: the model answers from your documents and shows where the answer came from.

Architecture: retrieval first, agents second

A workable stack is small: an ingestion pipeline that chunks and embeds approved documents, a vector store you control, and a chat call that receives the top matches as context. Plugsky embeddings and chat completions are OpenAI-compatible, so the glue code is familiar to any team that has used the OpenAI SDK.

Add function calling only after retrieval is trustworthy. Agents can then request data from PIM, ERP, POS and support systems through your own APIs, with the model choosing the call and your service enforcing permissions.

Residency, access and auditability

Retail data is sensitive. Plugsky supports cloud, VPC, on-prem and air-gapped deployments, with region selection for residency requirements. Air-gapped installations keep prompts and documents inside your network entirely.

Use scoped API keys per application, rotate them on a schedule, and log requests and responses under your retention policy. Keep supplier-confidential and pricing-sensitive content scoped by role, and never let the assistant quote values outside approved documents.

A rollout plan that stays explainable

Pick one document class, build a fifty-question evaluation set with known answers, and measure retrieval hit rate and answer accuracy before expanding. Publish the assistant to one team, collect the questions it could not answer, and close those gaps by adding documents rather than prompts. Keep supplier-confidential and pricing-sensitive content scoped by role, and never let the assistant quote values outside approved documents. Keep every answer traceable to a source.

Honest comparison

Capability	Plugsky	Generic chatbot	Building in-house
Retail document search	RAG with citations over your corpus	No access to internal documents	You build ingestion and evaluation
Deployment	Cloud, VPC, on-prem, air-gapped	Vendor cloud only	Your infrastructure
Model choice	30+ models behind one API	Single vendor model	You host each model
Function calling	Live for PIM and support systems	Limited or unavailable	Custom integration work
Pricing	Flat monthly self-serve plans; see live pricing	Per-seat subscription	GPU plus operations cost
Audio and image endpoints	Coming soon	Varies by vendor	Separate pipelines to maintain

Frequently asked questions

Can our data stay inside our own network?

Yes. Plugsky supports VPC, on-prem and air-gapped deployments, so prompts, documents and embeddings remain inside your environment when required.

Does Plugsky train on our documents?

For strict requirements, choose a private or air-gapped deployment so content stays inside your environment. Review the current data-handling terms and DPA for cloud plans before rollout.

How do citations work?

Your retrieval layer passes the top matching chunks into the chat call, and the assistant returns answers with references to those chunks. Require a refusal when no source clears the relevance threshold.

Which model should we use for catalogs and policy documents?

Benchmark a long-context model against chunked RAG on your own questions. Long-context helps with whole-document reasoning; RAG is cheaper and easier to cite.

Is there a free way to evaluate this?

Yes. The free plan includes two free AI models with no card, and the 14-day full-access trial lets you test larger models on your documents.

Can it power customer-facing answers?

Yes, with guardrails: restrict retrieval to approved customer-safe content, require citations, and hand off to a human when no source clears the relevance threshold.

Can it read supplier PDFs and spec sheets?

Text-based PDFs work today. Vision and OCR endpoints are coming soon for scans and image-only documents.