RAG6 min read
How RAG Systems Actually Work
A plain-language walkthrough of retrieval-augmented generation, from ingestion and chunking to grounded, cited answers, and where it tends to fail.
Innovaith Editorial
Solution 02 · Generative AI & RAG
Put what your business already knows at everyone's fingertips.
Your policies, contracts, tickets and manuals already hold the answers. We connect an AI assistant to them, so anyone can ask a question in plain English and get an answer from your own material, with a link to where it came from. It respects who is allowed to see what.
From documents to grounded answers
- Documents
- Ingestion
- Chunking
- Embeddings
- Vector search
- Retrieval
- LLM
- Grounded answer
What we build
Generative AI & RAG, engineered for production.
- 01
Knowledge assistants
Conversational access to policies, procedures and product knowledge, with sources cited in every answer.
- 02
Enterprise & semantic search
Search that understands meaning across documents, tickets and databases, and respects access control.
- 03
Document intelligence
Extract, classify and compare information across contracts, forms, reports and technical documents.
- 04
LLM applications
Drafting, summarisation and analysis tools built around your content, workflows and review processes.
- 05
Retrieval pipelines
Ingestion, chunking, embeddings, hybrid search and re-ranking tuned for your data rather than left on defaults.
- 06
Evaluation & quality
Separate measurement of retrieval quality and answer faithfulness, so improvements can be proven.
How it works
From documents to grounded answers
Every answer can be traced back to the passages it was built from.
- 01
Documents
PDFs, wikis, tickets, databases and file shares, each with its access rules.
- 02
Ingestion
Content is parsed, cleaned and kept in sync as sources change.
- 03
Chunking
Documents are split along their structure so each passage keeps its meaning.
- 04
Embeddings
Passages are converted into vectors that capture meaning, not just keywords.
- 05
Vector search
Hybrid semantic and keyword search finds candidate passages, filtered by permission.
- 06
Retrieval
Candidates are re-ranked and the most relevant context is assembled.
- 07
LLM
The model answers using only the retrieved context, with guardrails applied.
- 08
Grounded answer
A response with citations, or an honest “I don't know” when the sources are silent.
Where it helps
Built around the work, not the technology.
Typical places this capability changes how work gets done.
Support & success teams
Faster, consistent answers drawn from product docs, past tickets and internal runbooks.
Legal, risk & compliance
Review and compare clauses and policies, with the source passage always one click away.
Engineering & IT
Search across architecture docs, incidents and code documentation in plain language.
Sales enablement
Accurate answers about products, pricing rules and security questionnaires.
Engineering approach
What makes it production-grade.
Permission-aware retrieval
Answers only draw on content the user is allowed to see, enforced at query time.
Grounding & citations
Responses are tied to sources, and the system says so when it cannot find an answer.
Freshness
Incremental sync keeps the index current as documents change or are deleted.
Measured quality
Test sets and metrics for retrieval, faithfulness and helpfulness, tracked over time.
Typical technology
- OpenAI models
- Anthropic Claude
- Google Gemini
- Open-source models
- Python
- FastAPI
- Vector databases
- Embeddings & re-ranking
- Evaluation & tracing
- Multimodal models
- TypeScript
- Node.js
- PostgreSQL
- Redis
- Data pipelines
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Explore other capabilities
Agentic AI & Automation
An assistant that doesn't just answer. It looks things up, takes the next step in your systems and checks the result, with your team approving anything important.
- Answering customer questions
- Handling orders and refunds
- Updating records
- Sending follow-ups
See AI agents
Computer Vision
Cameras and images turned into useful signals: what is on the shelf, what happened in the shop, which item has a defect.
- Shelf and stock checks
- In-store safety and loss
- Spotting defects
- Counting people and vehicles
See computer vision
Data & Analytics
Scattered business data turned into forecasts, early warnings and dashboards your team will actually use.
- Sales and demand forecasts
- Early warnings
- Live dashboards
- One place for your data
See data and analytics
Idea → Architecture → Prototype → Production → Scale
Ready to explore Generative AI & RAG?
Tell us what you're trying to build, test, automate or improve. We'll tell you honestly where we can make a real difference, and where we can't.