RAG Centric PLM
A conversational AI assistant for the Centric PLM knowledge base, integrated directly into Microsoft Teams
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RAG Centric PLM is a conversational AI assistant developed by Digit-AI that turns roughly 1,400 Centric PLM technical documentation files into a knowledge base searchable in natural language, accessible from Microsoft Teams. Retail/fashion consultants and project managers ask their questions and get a sourced answer in seconds, with citations to back it up. The RAG pipeline chains a router (Claude Haiku 4.5) that selects which collections to query, a hybrid retrieval (Qdrant, cosine + BM25, ~54,800 chunks across 5 collections) followed by a cross-encoder reranker, then a generation step (Claude Sonnet 4.6) that writes the answer with source citations. The application is in production on Azure (francecentral region), with incremental ingestion from SharePoint and Confluence, versioned backup and auto-healing.
The client context
Centric PLM consultants spend a considerable amount of time searching for information across rich but scattered documentation
Retail/fashion consultants and project managers deploying Centric PLM juggle very dense, scattered documentation: administration guides, expression examples, integration scripts, client reports. Finding the right answer could take several hours. RAG Centric PLM makes this knowledge base searchable in natural language and answers in seconds, with source citations.
Scattered documentation
Administration guides, expression examples, integration scripts, client reports… spread across several media.
High search time
A precise question could require several hours of manual digging before getting a reliable answer.
Multi-client, multi-format
Client-specific configurations (Site.xml, Site.js, slicers, expressions), integration scripts in SQL/CSV/XML.
Evolving knowledge
The base grows every week: new training, client feedback, Centric product updates.
Functional scope
Consultants and project managers deploying Centric PLM in retail and fashion. Covered processes: natural-language querying from Teams, sourced answers, exploration via a web interface, incremental ingestion of the sources and continuous quality evaluation.
Key constraints
Sovereign hosting in the francecentral region · Answers systematically sourced and auditable · An evolving base synchronized automatically · Security (prompt-injection defenses, centralized secrets, non-root containers) · High availability with backup and auto-healing.
What RAG Centric PLM solves
Three areas to make Centric PLM knowledge instantly accessible and reliable
Reduce search time
Go from hours of manual digging to a few seconds for a precise answer, directly in Teams.
Reliable, sourced answers
Every answer cites its sources (file, collection, score) and relies on low-temperature generation for factual rigor.
An always-current base
Incremental ingestion from SharePoint and Confluence to continuously reflect evolving knowledge, with no manual reindexing.
What RAG Centric PLM enables
Beyond the conversational chat, an application designed to fit into everyday tools and evolve continuously
Conversational bot in Teams
Ask a question in a channel or direct chat and get an answer in seconds, without switching tools. Native Teams SSO authentication.
Sourced answers
Every answer cites the chunks used (file name, client, collection, relevance score) so you can trace back to the source document.
Streamlit web interface
For advanced uses: collection exploration, ingestion statistics, replay of an evaluation over a set of questions.
Incremental ingestion
Daily SharePoint sync (delta query) and weekly Confluence sync (CBFR + CKB spaces): only new or modified files are reprocessed.
Versioned backup
Daily Qdrant snapshot to Azure Blob, versioning by date and GFS retention (7 days / 4 weeks / 6 months), with point-in-time restore.
Continuous evaluation
A set of 39 evaluation questions replayable on every release, with side-by-side comparison and auto-classification (complete / partial / refusal).
Tech stack
A modern, largely open-source stack deployed on Azure Container Apps in the francecentral region for data sovereignty
Language & API
Vector & embeddings
Artificial intelligence
Integrations
Infrastructure
CI/CD & secrets
Distinctive pattern — A 3-stage routed RAG pipeline: rather than naive vector search, the pipeline chains a router (Claude Haiku) that targets the right collections, a hybrid retrieval (cosine + BM25) followed by a cross-encoder reranker, then a generation step (Claude Sonnet, temperature 0.1) that writes a sourced answer protected against prompt injection. The result: precise, traceable and reliable answers over a base of ~54,800 chunks.
Architecture diagrams
Sources synchronized into Qdrant, a three-stage RAG pipeline, and dual access via Teams + a web interface — all on Azure francecentral
Knowledge sources
SharePoint Online
Centric drive · daily delta (Graph API)
Confluence Cloud
CBFR + CKB spaces · weekly
Incremental ingestion
Chunking + E5-large embeddings
Only new/modified files
Vector store
Qdrant 1.12
~54,800 chunks · 5 collections · cosine + BM25
RAG pipeline
Router (Claude Haiku 4.5)
Retrieval + reranker
Generation (Claude Sonnet 4.6)
User access
Microsoft Teams bot
Native SSO · sourced answers
Streamlit web interface
Exploration, stats, evaluation
Platform (Azure francecentral)
Container Apps · Blob GFS backup · Doppler
Sovereign hosting
Impact and qualitative gains
The service is in production: here is how the consultant experience has evolved
| Indicator | Before | With RAG Centric PLM | Gain |
|---|---|---|---|
| Time to find a precise technical answer | Several hours, sometimes several days | A few seconds to a few minutes | Drastically reduced |
| Access to documentation | Many tools (drives, wikis, emails) | A single entry point: a Teams chat | Centralized |
| Answer traceability | Hard (from memory or screenshots) | Every answer cites its sources | Auditable |
| Knowledge-base updates | Manual, occasional indexing | Daily SharePoint + weekly Confluence sync | Automated |
| Data security | Documents shared by email, attachments | Azure francecentral hosting, anti-injection | Hardened |
Security & operations
Sovereign hosting
All services in the francecentral region (Paris) on Azure. Internal ingress between services, external ingress limited to the API and the web app.
Centralized secrets
Managed via Doppler, then injected as referenced secrets in the Container Apps. No plaintext keys, with bandit / gitleaks / hadolint audits.
Prompt-injection defenses
The system prompt guards against user prompt injection. A secret-redaction filter on logs, and containers running as a non-root user.
Backup & restore
Daily Qdrant snapshot to Azure Blob, versioned copy with GFS rotation and one-command point-in-time restore.
Auto-healing
A sentinel job runs every 15 minutes: if a Qdrant collection is empty while a backup exists, it restores automatically.
GitOps CI/CD
A push to main triggers the build of the 5 Docker images via GitHub Actions, the push to GHCR and the Container Apps deployment.
Sample questions handled
A few real questions asked of the application (excerpt from the internal evaluation set), illustrating the diversity of topics covered
How do you set up a watchAttribute in Centric PLM?
The watchAttribute JavaScript API is configured in Site.js to trigger an action when a user changes an attribute value on a node. Several patterns exist (same node, target node, multiple nodes via changeList).
Sources: 5 chunks from the configuration and reference collections (Centric Configuration Training, AAT-AdvancedAttributeConfiguration).
What is the difference between list, map and vector attributes?
The answer summarizes the three structures, their typical use case in Centric configuration, and points to the corresponding Site.xml examples for each attribute type.
Sources: Chunks from configuration, Centric training and client integration examples.
What are the main steps of a PLM version-upgrade project?
The application lists the typical sequence (preparation, pre-prod, acceptance, prod), with the points to watch per module and references to the Centric release notes.
Sources: Reference chunks, Centric v7.4 release notes, project sheets.