Searches everything, cites everything
A major auto parts manufacturer had its knowledge scattered across legacy ERPs, modern systems, emails and documents. We built a multimodal RAG system that searches all of it from one place — and attaches the source to every answer it gives.
1
search interface, replacing the five they had
3
layers: ingestion, retrieval, generation
0
answers shipped without a source citation
The enterprise search challenge
Our client, a major auto parts manufacturer, was struggling with information scattered across legacy ERPs, modern systems, emails, and documents. Teams couldn't find what they needed.
Before
- Five different search systems — teams had to search multiple places
- No source attribution — nobody could verify an answer was right
- Keyword-only search — relevant results missed whenever the wording differed
- Long time to find answers, because product information sat everywhere
After
- A single unified interface — every system searchable from one place
- Complete source citations — every answer linked to its original source
- Semantic understanding — finds the answer regardless of wording
- Answers assembled from every system at once, as the data changes
From scattered records to a sourced answer
Five stages, each one doing a job the next depends on. The retrieval is hybrid on purpose: meaning and exact match are different problems and a catalogue needs both.
Data ingestion
Continuously indexes ERPs, documents, emails, databases — multi-format parsing, metadata extraction and real-time indexing as records change.
Vector processing
Creates embeddings and stores them in a vector database, so meaning is searchable rather than only the words somebody happened to type.
Query understanding
Processes natural-language queries semantically, so a question phrased in a person's own words still reaches the right records.
Hybrid search
Combines vector and keyword search rather than choosing between them: the dense side catches meaning, the keyword side catches part numbers.
Answer generation
The model writes the answer, and every claim in it carries the document, page and section it came from. An answer with no source is not shipped as an answer.
Three layers, each replaceable
Ingestion, semantic processing and answer generation are separate layers, so a better embedding model or a different vector store is a swap rather than a rebuild.
Data ingestion layer
Processes ERPs, documents, emails and databases in real time.
- Multi-format parsing
- Metadata extraction
- Real-time indexing
Semantic processing
Vector embeddings and semantic search across all content.
- Vector embeddings
- Similarity search
- Context understanding
Answer generation
Answers assembled by the model with complete source attribution.
- Source citations
- Question suggestions
- Contextual answers
What the system does
Every capability exists to make enterprise search accurate and verifiable, rather than merely fast.
Source attribution
Every answer includes exact source citations — document, page, section, and even specific reviews or Q&As.
Multimodal search
Search across text, images, PDFs, spreadsheets, emails, and any document format in your enterprise.
Smart suggestions
The system suggests related questions based on product Q&As, reviews, and common queries for deeper insights.
SKU-specific search
Search within specific products or across the entire database, with granular control over search scope.
Real-time updates
New documents, emails, and data are indexed as they arrive, so search results reflect what the business knows now.
API integration
RESTful APIs for easy integration with WhatsApp, web chat, and any enterprise application.
Where the confidence comes from
Confidence comes from verifying the evidence exists, not from the model grading itself.
Every extracted value carries its source quote and page. A value without a traceable source is not an answer; it is a guess. That is why the retrieval is hybrid, why the citations are per claim rather than per page, and why a question the corpus cannot answer comes back saying so.
Built with
Bring the question nobody can answer today
This is our AI engineering practice
It is real work and it is where our four products came from. But what Cognilium leads with is narrower: optimization apps that run in tandem with Microsoft Dynamics 365, computing the decisions the ERP records but does not derive — the optimal price, the optimal pick path, the optimal stock level. See the optimization apps · How we build inside the ERP.