Production RAG Engine Development for Enterprise Knowledge
Stop losing institutional knowledge. We build retrieval-augmented generation systems that give your team instant, accurate answers from your own documents — at scale.
Your knowledge is locked up
The information your team needs exists somewhere in your organisation. The problem is getting to it instantly, reliably, and in the right context.
Documents nobody reads
SOPs, contracts, training manuals, and product guides gather dust on shared drives. The knowledge exists — but nobody can find it when they need it.
Wrong answers from generic AI
ChatGPT doesn't know your processes, your pricing, or your compliance requirements. Generic AI hallucinates. A RAG engine grounded in your documents does not.
New hires taking months to onboard
Without a single, queryable source of truth, new team members spend weeks asking colleagues for context that should be instantly accessible.
What is a RAG Engine?
RAG — Retrieval-Augmented Generation — combines a vector search layer with a large language model. When a user asks a question, the system retrieves the most relevant passages from your documents, then passes them to the LLM as context for generating an answer.
The result: accurate, cited answers grounded in your actual data — not hallucinations. The model can only draw on what your documents say, so it stays honest and specific. Every answer can be traced back to a source chunk.
RAG is the technology powering the most reliable AI knowledge systems in production today. Use cases include internal knowledge bases, customer-facing Q&A, contract analysis, support automation, and compliance checking.
How RAG works
User asks a question
In natural language
Vector search retrieves relevant chunks
From your indexed documents
Chunks passed to LLM as context
Grounded, not guessed
Accurate answer returned
With source citations
Used for
What we deliver
Every RAG engine we build includes four core components — production-grade from day one.
Hybrid Search
Vector embeddings combined with keyword search for maximum recall. We find the right chunks even when users phrase queries differently from your documents.
Multi-language Reranking
Works in English, Italian, and beyond. Our reranking layer scores and orders retrieved passages for accuracy regardless of the query language.
Knowledge Graph
We map entities and relationships across your corpus — not just keywords. The system understands that 'GDPR' connects to 'data controller', 'consent' and 'DPA'.
Production-ready API
Secure, scalable REST or streaming API that connects to your existing stack — Slack, your website, your internal tools. We handle auth, rate limiting, and monitoring.
4 weeks
Average delivery time
10M+
Documents indexed in production
<200ms
Median query latency
5+
Languages supported
From documents to answers in 4 weeks
A lean, sprint-based delivery with clear milestones. You see working software at the end of every phase.
Audit your data sources
We inventory every document store — SharePoint, Notion, Google Drive, PDFs, databases — and assess data quality, structure, and sensitivity.
Build ingestion pipeline
Custom ingestion, chunking strategy, and embedding pipeline tuned for your document types. Automatic re-indexing when documents update.
RAG API + interface
We deploy the retrieval layer, LLM integration, and a query interface — whether that's a Slack bot, web chat, or API endpoint for your team.
Monitoring & updates
We track retrieval quality, answer accuracy, and latency. Regular updates to the knowledge base keep your RAG engine current as your business evolves.
Your documents stay yours. We deploy on your infrastructure or a private cloud — your data never leaves your control.
Ready to unlock your institutional knowledge?
Book a free 15-minute call — we’ll assess your documents and show you what a production RAG engine would look like for your business.
No commitment. global@agentispro.com