Retrieval-Augmented Generation
Ground your LLMs in your own knowledge — with citations, not guesses.
Production-grade RAG pipelines connecting large language models to your proprietary data, so every answer is accurate, current, and traceable to a source.
- Every answer cites its origin
- Source-grounded
- Keyword + vector retrieval
- Hybrid search
- Retrieval accuracy tracked
- Continuously evaluated
Technical architecture
How the RAG pipeline works
From a raw document to a source-cited answer — the same eight stages behind every retrieval system we build.
- DocumentsSource files, wikis, tickets & systems
- Document ProcessingCleaning, parsing & normalization
- ChunkingSplit into retrieval-sized passages
- EmbeddingsConverted into vector representations
- Vector DatabaseIndexed for fast semantic search
- RetrievalHybrid search surfaces relevant context
- LLMModel reasons over retrieved context
- ResponseAccurate, source-cited answer
The problem
A model that doesn't know your business will guess
Foundation models are trained on public data — ask them about your product, your policies, or your customers, and they'll either say they don't know or confidently make something up.
- Fine-tuning alone goes stale the moment your documents change
- Ungrounded models hallucinate answers that sound plausible but are wrong
- No citation trail makes it impossible to verify or trust an answer
- Search across scattered systems (wikis, tickets, docs) stays fragmented
The solution
Answers grounded in your real, current knowledge
We build retrieval pipelines that pull the right information at query time and hand it to the model as grounding — fast to update, accurate, and auditable.
Always current
Retrieval reads live data, so answers reflect this week, not last year's training cut-off.
Traceable
Every answer links back to its source document for verification.
Tuned for relevance
Hybrid search and re-ranking keep retrieval precise, not just plausible.
Capabilities
What we build
The full retrieval pipeline, not just a vector database.
Ingestion pipelines
Document chunking and embedding from all your knowledge sources.
Vector database architecture
Hybrid search combining keyword and semantic retrieval.
Retrieval & re-ranking
Pipeline tuning so the most relevant context reaches the model.
Source citation
Answer-grounding frameworks that show their work.
Evaluation harnesses
Accuracy and relevance metrics tracked continuously.
Access control
Enterprise permissions respected inside the retrieval layer itself.
Technology
Technology we use
Purpose-built retrieval infrastructure, not a single database choice.
Vector stores
- Pinecone
- Weaviate
- pgvector
Orchestration
- LangChain
- LlamaIndex
Evaluation
- Custom retrieval-precision harnesses
Architecture
How a RAG pipeline is structured
Five stages between a raw document and a trustworthy answer.
01
Ingestion
Documents are chunked and normalized from source systems.
02
Embedding
Chunks are converted into vector representations for semantic search.
03
Retrieval
Hybrid keyword and vector search surfaces the most relevant chunks.
04
Re-ranking
Retrieved results are re-ordered for maximum relevance before reaching the model.
05
Grounded generation
The model answers using only retrieved, cited context.
Use cases
Where we've applied this
Enterprise
Enterprise knowledge assistant
Company-wide Q&A grounded in wikis, docs, and internal systems.
Legal & Compliance
Document Q&A
Ask questions directly against contracts, reports, and manuals.
Enterprise
Internal search
Unified semantic search across previously fragmented systems.
SaaS
Customer support
Agent-facing copilot grounded in product documentation.
Enterprise
Employee assistant
HR and IT self-service answers grounded in internal policy.
Regulated Industries
Policy assistant
Compliance-safe answers grounded in current policy documents.
R&D
Research assistant
Synthesizes findings across large technical or scientific corpora.
Process
How we build a RAG system
- 01
Audit knowledge sources
Identify and structure the documents worth retrieving from.
- 02
Build the pipeline
Ingestion, embedding, and retrieval infrastructure stood up and tuned.
- 03
Evaluate
Test retrieval precision and answer groundedness against real queries.
- 04
Deploy & refresh
Keep the knowledge base current as source documents change.
Benefits
What grounding actually buys you
Fewer hallucinations
Answers are constrained to what's actually retrieved.
Verifiable answers
Citations let users check the source themselves.
Faster updates
New documents are searchable immediately, no retraining required.
Unified search
One retrieval layer across previously fragmented knowledge systems.
Keep exploring
Related services
Generative AI
Custom LLM applications, fine-tuning, and generative content systems built for enterprise use.
ExploreAI Chatbots
Conversational AI assistants for support, sales, and internal operations that actually resolve issues.
ExploreAI Agents
Autonomous, tool-using AI agents that plan, act, and complete multi-step business tasks.
ExploreFAQ
Frequently asked questions
RAG retrieves relevant, current information at query time rather than baking it into model weights — making it faster to update, more accurate, and auditable via source citations.
Want your AI to actually know your business?
Tell us what knowledge it needs to be grounded in — we'll design the retrieval pipeline around it.