Generative AI

RAG (Retrieval-Augmented Generation) Development

LLM answers grounded in YOUR data, with citations. No hallucinations, no generic responses.

Solution
RAG Pipelines
Timeline
3–6 weeks for an MVP RAG, 8–14 weeks for permission-aware enterprise RAG.
Built On
Pinecone · Weaviate · pgvector / Qdrant · LangChain / LlamaIndex
Quick Answer

What is Dictode's RAG (Retrieval-Augmented Generation) Development service?

LLM answers grounded in YOUR data, with citations. No hallucinations, no generic responses.Production-grade RAG pipelines that ground LLMs in your private documents, databases and knowledge bases — accurate, citation-backed and hallucination-resistant. Engagements include discovery, architecture, development, testing, deployment, observability and ongoing optimization. Typical timeline: 3–6 weeks for an MVP RAG, 8–14 weeks for permission-aware enterprise RAG. Available worldwide in 60+ countries, billed in any major currency, with 24/7 follow-the-sun support.

Free discovery call· proposal back in 24 hours
3–6 weeks for an MVP RAG, 8–14 weeks for permission-aware enterprise RAG.
Worldwide delivery· 60+ countries served
Who It's For

Who Needs RAG (Retrieval-Augmented Generation) Development

If any of these sound like your team, this is the right service.

Enterprises with thousands of internal docs nobody reads
Customer support teams wanting AI that knows your KB
Legal / compliance teams needing accurate cited answers
Sales teams wanting deal-history + competitive-intel chat
Research teams searching across PDFs and reports
Capabilities

What You Get With RAG (Retrieval-Augmented Generation) Development

Multi-source ingestion (PDFs, Word, Confluence, Notion, SharePoint, S3, databases)

Smart chunking + embedding strategies

Hybrid search (semantic + keyword + reranker)

Citation generation with source links

Permission-aware retrieval (only show what user can see)

Eval framework to measure accuracy + recall

Tech Stack

Built On Production-Hardened Tech

The exact tooling we use to deliver RAG (Retrieval-Augmented Generation) Development — picked for stability, not novelty.

PineconeWeaviatepgvector / QdrantLangChain / LlamaIndexOpenAI / Cohere embeddingsAnthropic ClaudeReranker models (Cohere, Voyage)
How We Work

The RAG Pipelines Engagement Process

From first conversation to live production, here is how a Dictode RAG (Retrieval-Augmented Generation) Development project runs.

1

Discovery call

30-minute free call to understand your goals, current systems and constraints. No sales pitch.

2

Proposal in 24h

Written scope, milestones, timeline and pricing — fixed-price or T&M, your choice.

3

Architecture + design

We share the architecture, prompts, data flow and design system before writing production code.

4

Iterative delivery

Weekly working demos. Production-ready code from sprint one, not just at the end.

5

Launch + observability

Cost monitoring, error tracking, evals and alerts in place from day one of production.

6

Ongoing optimization

We stay with you — tuning prompts, adding capabilities, optimizing costs as usage grows.

FAQ — RAG Pipelines

RAG (Retrieval-Augmented Generation) Development — Frequently Asked Questions

What is included in Dictode's RAG (Retrieval-Augmented Generation) Development service?

Production-grade RAG pipelines that ground LLMs in your private documents, databases and knowledge bases — accurate, citation-backed and hallucination-resistant. Engagements include discovery, architecture, development, testing, deployment, observability and ongoing optimization.

How long does a RAG (Retrieval-Augmented Generation) Development engagement take?

3–6 weeks for an MVP RAG, 8–14 weeks for permission-aware enterprise RAG. You will get a transparent proposal with milestones and pricing within 24 hours of your discovery call.

Which technologies does Dictode use for RAG (Retrieval-Augmented Generation) Development?

Our RAG (Retrieval-Augmented Generation) Development stack includes Pinecone, Weaviate, pgvector / Qdrant, LangChain / LlamaIndex, OpenAI / Cohere embeddings, Anthropic Claude, Reranker models (Cohere, Voyage). We pick the right tool for your use case, not whatever is trending.

Does Dictode offer RAG (Retrieval-Augmented Generation) Development services globally?

Yes. Dictode delivers RAG (Retrieval-Augmented Generation) Development engagements in 60+ countries across North America, Europe, the UK, the Middle East, Africa, APAC, ANZ, India and Latin America. We bill in any major currency and run 24/7 follow-the-sun support.

What does RAG (Retrieval-Augmented Generation) Development cost?

Pricing depends on scope, integrations and the model / infrastructure you choose. We share a transparent fixed-price or T&M proposal within 24 hours of your discovery call — no hidden costs, no surprise invoices.

Can RAG (Retrieval-Augmented Generation) Development integrate with our existing systems?

Yes. Our RAG (Retrieval-Augmented Generation) Development engagements integrate with your existing CRM, ERP, helpdesk, knowledge base, databases, email, Slack, Teams, WhatsApp and 5,000+ apps via REST, GraphQL and Zapier / Make / n8n connectors.

Ready to ship RAG Pipelines?

Free 30-minute discovery call. Written proposal in 24 hours. Production-grade code, every time.

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