AI Development

RAG Development Services for Grounded, Citable AI

Custom Retrieval-Augmented Generation pipelines built with the vector databases, hybrid retrieval, and agentic patterns your workload actually needs.

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Overview

Retrieval-Augmented Generation built for grounded, citable answers

Our RAG development services design and ship production-grade RAG applications. Custom ingestion, vector database setup, hybrid retrieval, agentic RAG, LLM integration and fine-tuning - built to ground LLM output in your data with citations, observability, and guardrails. As a rag application development company delivering custom rag development services, we treat retrieval quality and eval as first-class, not afterthoughts.

Years in business: 12
Team members: 65+
Global clients: 30+
Yr avg. client retention: 4+

Who this is for

Teams building internal knowledge-base assistants for support, sales, or operations.
Product teams adding citable AI search and Q&A to customer-facing applications.
Organizations whose general-purpose LLM is hallucinating on domain-specific questions.
Founders building AI-native products that need to answer from their corpus, not the open web.

What you get

RAG architecture document - source inventory, ingestion strategy, chunking plan, embedding model, vector store, retrieval strategy, generation layer, eval suite.
Custom ingestion and processing pipelines - source connectors, content cleaning, chunking, metadata enrichment, embedding generation.
Vector database setup - Pinecone, Weaviate, pgvector, Qdrant, Milvus, or your existing store. Indexed, tuned, and monitored.
Hybrid retrieval - combined keyword (BM25) and semantic retrieval, re-ranking layers, and metadata filters for precision.
Agentic RAG - multi-step retrieval, query decomposition, self-correction, and tool calling where the workflow needs it.
LLM integration and fine-tuning - selection across OpenAI, Anthropic, Google, and open-source models. Fine-tuning when retrieval alone is not enough.
Evaluation and monitoring - precision, recall, faithfulness, hallucination rate, latency, measured continuously.

How we work

01 Step

Audit

Map the questions the RAG needs to answer, the corpus, the consumers, and success metrics (faithfulness, answer rate, latency).
02 Step

Plan

Pick chunking strategy, embedding model, vector store, retrieval strategy, LLM, and eval framework.
03 Step

Build

Ship ingestion, retrieval, and generation layers in increments. Each increment runs against a golden eval set.
04 Step

Test

Offline eval for precision, recall, and hallucination. Online eval against real user queries before scale.
05 Step

Scale

Harden monitoring, add agentic patterns where needed, layer fine-tuning if retrieval hits a ceiling.

Tools & stacks we use

The platforms our team is fluent in for this practice. Most engagements span a few of these, picked for the actual problem rather than for the demo.

OpenAI text-embedding-3
Cohere
Voyage
BGE
E5
GTE
Pinecone
Weaviate
pgvector
Qdrant
Milvus
Chroma
OpenAI GPT-4o
Anthropic Claude
Google Gemini
Llama
Mistral
LangChain
LlamaIndex
Haystack
Cohere Rerank
RAGAS
Trulens
Langfuse
LangSmith
Phoenix

Need dedicated experts?

Hire a specialist embedded with your team

Pre-vetted senior talent for this practice - hourly, retainer, dedicated FTE, or Micro-GCC. Vetted in 48 hours, managed end-to-end by H4H operations.

AI / ML Engineer Hire a AI / ML Engineer Prompt Engineer Hire a Prompt Engineer

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Frequently asked questions

Still have a question? Talk to a real human on our team - we usually reply within one business day.

Talk to a specialist

What is RAG and when do I need it?

Retrieval-Augmented Generation pairs an LLM with a retrieval layer over your corpus. You need it when the model has to answer from your data - not the open web - with citations and grounding.

How does H4H run a RAG development engagement?

Audit, Plan, Build, Test, Scale. We define faithfulness and answer rate targets upfront, build against a golden eval set, and only ship to production after offline and online eval pass.

How much do RAG development services cost?

Project bands typically start in the mid-five figures and scale with corpus size and integration complexity. Retainer and dedicated FTE quoted separately.

How long does a RAG pipeline take to build?

A focused RAG build runs 6-14 weeks. Agentic and fine-tuned variants run longer.

Which vector database should I pick?

Depends on existing infrastructure, scale, and latency targets. Pinecone for managed simplicity. pgvector for Postgres-native stacks. Qdrant or Milvus for open-source self-hosted at scale. Weaviate for hybrid retrieval out of the box.

Do you fine-tune models or only use RAG?

Both, when warranted. Fine-tuning earns its place when retrieval alone cannot cover style, domain language, or task structure. RAG usually goes first - fine-tuning layers on if needed.

How do you handle hallucination?

Three layers - retrieval quality (so the right context lands), prompting and constraints (so the model is told what to refuse), and evaluation (so we measure faithfulness continuously).

Can RAG be combined with agentic workflows?

Yes. Agentic RAG (multi-step retrieval, query decomposition, tool calling, self-correction) is part of our standard toolkit.

Proof points

Related case studies

What we have shipped for clients with adjacent problems. Each one is sourced and attributable.

LegalWiz.in - Professional Services / LegalTech

Production platform serving 10,000+ clients across 100+ services

Full-stack platform for a leading professional services company. Process and service builder, CRM, billing engine, AI-powered chat and knowledge library, dynamic questionnaire with upsell, role-driven access.

Read case study

Related services

Ready to put your data to work?

Book a free audit and we will map the problem, the metrics, and the smallest first build that proves value.

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