LlamaIndex Development Services

Build context-aware AI agents grounded in your enterprise documents.

Vstorm builds enterprise-grade RAG and agentic applications with LlamaIndex and LlamaParse — from PoC to production deployment with ongoing optimization. Delivered by our team that has shipped 30+ LLM projects since 2017.

30+ LLM projects shipped
2017 Building since
25 AI specialists
RAG Pipeline
enterprise_rag.py
# Build a context-aware agent from llama_index import VectorStoreIndex from llama_parse import LlamaParse   docs = LlamaParse( result_type="markdown", parsing_instruction="...", ).load_data("./contracts/")   index = VectorStoreIndex.from_documents(docs) agent = index.as_query_engine( similarity_top_k=8, response_mode="tree_summarize", )   # Query grounded in your data agent.query("Q3 renewal risks?")
The Framework

Why leading companies build with LlamaIndex

LlamaIndex is an open-source data framework that connects large language models to private, domain-specific data through ingestion, indexing, retrieval, and agent orchestration. Rather than relying on what the model learned during training, LlamaIndex lets organizations ground AI applications in their own documents, databases, and knowledge sources — enabling accurate retrieval, agentic reasoning, and reliable AI workflows over the messy real-world content their business actually runs on.

80%
of enterprise data is unstructured — locked in PDFs, contracts, slide decks, and emails.
40%
of RAG responses can hallucinate when ingestion and retrieval are not engineered properly.
26%
of companies use mostly automated methods to analyze their content — the rest rely on manual review.
$3.1T
annual US cost of poor data quality through lost productivity and operational inefficiency.

LlamaIndex addresses the upstream half of this problem: turning unstructured enterprise content into high-quality, retrievable context that AI agents can actually use.

Talk to our team

Not sure where LlamaIndex fits in your stack?

A 30-minute call is usually enough to scope your use case and recommend the right entry point.

What we do

Our LlamaIndex services

Five engagement phases — from validating feasibility to running optimized pipelines in production. Pick the entry point that matches where you are today.

Ready to start?

Bring us your toughest document workflow.

We'll tell you in one call whether LlamaIndex is the right tool — and what it would take to ship it.

Why Vstorm

Three reasons mid-market and enterprise teams pick us

We've been building production LLM systems since 2017. We know which decisions in a LlamaIndex pipeline matter, and which ones can wait.

01 / Experience

30+ projects in LlamaIndex and document AI

Deep expertise deploying LlamaIndex, LlamaParse, and adjacent document intelligence tools across enterprise RAG, agent, and extraction pipelines. Our 25 AI specialists deliver custom, scalable solutions tailored to complex document workflows.

02 / Stack

Specialized, production-ready tooling

We combine LlamaIndex with a curated stack of ingestion, retrieval, and orchestration tools — LlamaParse, vector databases, and custom evaluation frameworks — for accurate, efficient solutions on every project.

03 / Support

End-to-end ownership

Full support from consultation and proof of concept through deployment, monitoring, and ongoing optimization — ensuring scalable, secure, and future-ready document processing pipelines.

Common Questions

Frequently asked questions about LlamaIndex

What teams typically ask before starting a LlamaIndex engagement.

LlamaIndex is an open-source data framework — available in Python and TypeScript — for building context-aware AI agents and RAG applications. It connects large language models to your private, domain-specific data through ingestion, indexing, retrieval, and orchestration components, letting LLMs reason over information that wasn't part of their training data.
LlamaIndex is the broader framework for building agentic and RAG applications; LlamaParse is one component within that ecosystem, focused specifically on parsing complex documents (PDFs, scans, slides, spreadsheets) into clean, LLM-ready output. You can use LlamaParse standalone for document ingestion or combine it with LlamaIndex to build full retrieval and agent pipelines.
Both frameworks help developers build LLM applications, but their emphasis differs. LlamaIndex specializes in context augmentation — high-quality data ingestion, indexing, and retrieval over private documents — while LangChain leans more toward general agent orchestration and tool chaining. In practice, the two are often combined, but teams building document-heavy RAG pipelines tend to start with LlamaIndex.
The most common enterprise use cases include knowledge assistants over internal documents, agentic RAG for research and analysis, automated report generation, customer support agents grounded in product documentation, financial due diligence, contract intelligence, invoice processing, and technical document search across complex internal corpora.
Agentic RAG extends traditional retrieval-augmented generation by adding agents that can plan queries, choose retrieval strategies, call tools, and reason across multiple knowledge bases. LlamaIndex provides the abstractions for both prebuilt agents and fully custom agentic Workflows, plus retrieval modes (including auto-routing) that intelligently select the right index and search strategy per query.
Yes. LlamaIndex is model-agnostic and integrates with OpenAI, Anthropic, Mistral, Cohere, Google, AWS Bedrock, Azure OpenAI, and open-source models served locally or via Hugging Face. The same applies to embedding models and vector stores — the framework treats these as pluggable components, so you can swap providers without rewriting your pipeline.
Open-source LlamaIndex is the developer framework you self-host and configure end-to-end. LlamaCloud is the managed, enterprise-grade platform layered on top — it includes LlamaParse, hosted indexing and retrieval (LlamaCloud Index), document agents, and enterprise features like multi-tenant scheduling, access control, and permissioned ingestion that are non-trivial to build from scratch.
LlamaIndex pairs with LlamaParse to extract clean structure from complex documents, then chunks, embeds, and indexes them through configurable strategies. For mixed corpora (contracts, reports, transcripts, scanned forms in one knowledge base), it supports multiple specialized indices with parsing settings tuned per document type, plus agentic routing that sends each query to the right index.
Yes — it's used in production by Salesforce Agentforce, Boeing-owned Jeppesen, and other enterprise teams. For production-scale deployments, most teams combine the open-source framework with LlamaCloud, which addresses the operational concerns that arise at scale: noisy-neighbor multi-tenancy, document-level access control, robust failure handling, and permissioned ingestion across enterprise data sources.
The open-source framework is free. LlamaCloud uses a credit-based pricing model (1,000 credits = $1.25), with 10,000 free credits per month for new users. Costs vary by parsing tier, indexing volume, and retrieval activity. Most projects also incur underlying LLM and embedding API costs, which are typically the largest line item in a production RAG system.
LlamaIndex offers full SDKs in Python and TypeScript, with first-class integrations for FastAPI, Next.js, Streamlit, and AWS/Azure/GCP environments. It connects to 150+ data sources via LlamaHub, including S3, SharePoint, Google Drive, Notion, Slack, Salesforce, and most major databases and vector stores.
LlamaIndex is the strongest fit when your application depends on retrieving accurate context from a large body of private documents — especially complex formats like PDFs, contracts, financial filings, or technical manuals — and when you need agents that can reason over that content rather than just answer simple questions. For purely conversational use cases without document grounding, lighter-weight frameworks may be sufficient.
Get started

Ready to build your
LlamaIndex solution?

Whether you're validating a use case, scaling a pilot, or replacing a brittle OCR pipeline, our team can help you move from concept to production with confidence.