Healthcare hallucination rate
65.9%
Hallucination rates of "raw" LLMs in healthcare-related environments — before grounding and validation.
LlamaIndex development services
Build AI agents grounded in your enterprise documents .
Vstorm builds enterprise-grade RAG and agentic applications with LlamaIndex and LlamaParse — from Proof of Concept to production deployment with ongoing optimization. Delivered by a team that has shipped 30+ LLM projects since 2017.
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+11.76% orders from day 1Three-agent product advisor guiding customers through print-order configuration.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Agentic AI for operational workflow automation.
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Agentic AI in Saudi Arabia’s Ministry of Municipalities and Housing procedures.
Coming soon -
1% → 100% knowledge-inquiry response rateBilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
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Knowledge assistants over internal documents
Answer questions grounded in your wikis, policies and manuals — not the model's training data.
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Agentic RAG for research and analysis
Agents that plan queries, route across indices and reason over multiple knowledge bases.
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Contract, filing and invoice intelligence
Extract structured fields from complex PDFs, scans and tables with validation at the boundary.
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Customer support grounded in product docs
Support agents that cite accurate, up-to-date answers from your documentation.
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Technical document search across complex corpora
Semantic search over contracts, reports and transcripts in one permissioned knowledge base.
The risk without grounding
Why retrieval and validation decide whether RAG ships
These numbers reflect what happens when LLM outputs are not grounded in your data and validated at the boundary.
LlamaIndex addresses the upstream half of the problem: turning unstructured enterprise content into high-quality, retrievable context that AI agents can actually use.
of enterprise data is unstructured
80%
IDC projects roughly 80% of the world's data will be unstructured by 2025 — locked in PDFs, contracts and email your model was never trained on.
Case Studies
Document AI systems in production
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 — no pitch deck.
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.
Proof of Concept
Validate feasibility and effectiveness of a LlamaIndex-based solution against your real-world documents.
- Working prototype for your document types and target output
- Real-world testing on tables, scans, multi-column layouts and embedded visuals
- Accuracy, cost-per-page and latency captured against your requirements
- Feasibility report: parsing fidelity and ROI vs. your current approach
Consultation
Assess where LlamaIndex fits within your broader AI and data strategy before any code is written.
- Workflow audit: document formats and unstructured-data volume mapped end-to-end
- Use-case identification where LlamaIndex outperforms OCR or rule-based extraction
- Framework setup, parsing-tier and retrieval-strategy advice tuned to budget
- Integration roadmap aligned with your retrieval, agent or analytics objectives
Solution architecture
Translate validated requirements into a production-ready blueprint with timelines and dependencies.
- Source and destination mapping — ingestion triggers, vector stores, agents
- Parsing and indexing strategy with output schemas per document class
- Orchestration design: caching, error handling, version locking, reprocessing
- Complete technical specification with timelines and resourcing
Development and delivery
Build, integrate and deploy a fully operational LlamaIndex-powered solution in your environment.
- Ingestion, indexing and retrieval with schema enforcement and validation
- Integrated with your RAG systems, document platforms, ERPs and data lakes
- Monitoring: parsing accuracy, retrieval quality, throughput, cost and alerts
- End-to-end testing, security review and full technical documentation
Optimization and support
Maintain and improve performance as your document landscape and use cases evolve.
- Accuracy monitoring with prompts refined as new document types emerge
- Cost and quality tuning: tier routing kept predictable at scale
- Version-locked, controlled rollouts for LlamaIndex and LlamaParse updates
- Dedicated support with quarterly performance reviews
Why Vstorm?
Three reasons mid-market and enterprise teams pick us
We have built 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 engineers 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.
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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.
"My wish was to come to at least an 80% success rate… by the time we finished, over 95.4%, so it definitely exceeded expectations."
Lucian Puca, Digital Product Manager · Mixam
Common questions
Frequently asked questions about LlamaIndex
What is LlamaIndex?
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, letting LLMs reason over information that was not part of their training data.
How is LlamaIndex different from LlamaParse?
LlamaIndex is the broader framework for building agentic and RAG applications; LlamaParse is one component within it, focused on parsing complex documents (PDFs, scans, slides, spreadsheets) into clean, LLM-ready output. You can use LlamaParse standalone for ingestion or combine it with LlamaIndex for full retrieval and agent pipelines.
How is LlamaIndex different from LangChain?
Both help build LLM applications, but their emphasis differs. LlamaIndex specializes in context augmentation — high-quality ingestion, indexing and retrieval over private documents — while LangChain leans toward general agent orchestration and tool chaining. In practice they are often combined, but document-heavy RAG pipelines tend to start with LlamaIndex.
What's the difference between open-source LlamaIndex and LlamaCloud?
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 — hosted indexing and retrieval, LlamaParse, document agents, and enterprise features like multi-tenant scheduling, access control and permissioned ingestion that are non-trivial to build from scratch.
What is agentic RAG, and how does LlamaIndex support it?
Agentic RAG extends retrieval-augmented generation by adding agents that plan queries, choose retrieval strategies, call tools and reason across multiple knowledge bases. LlamaIndex provides abstractions for both prebuilt agents and fully custom agentic workflows, plus retrieval modes (including auto-routing) that select the right index and search strategy per query.
How does LlamaIndex handle large or messy document corpora?
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 and scanned forms in one knowledge base — it supports multiple specialized indices with parsing tuned per document type, plus agentic routing that sends each query to the right index.
Does LlamaIndex work with any LLM provider?
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. Embedding models and vector stores are pluggable too, so you can swap providers without rewriting your pipeline.
What programming languages and integrations does LlamaIndex support?
LlamaIndex offers full SDKs in Python and TypeScript, with first-class integrations for FastAPI, Next.js, Streamlit and AWS/Azure/GCP. It connects to 150+ data sources via LlamaHub, including S3, SharePoint, Google Drive, Notion, Slack, Salesforce and most major databases and vector stores.
Is LlamaIndex production-ready for enterprise workloads?
Yes — it is used in production by enterprise teams including Salesforce Agentforce and Boeing-owned Jeppesen. For production-scale deployments, most teams combine the open-source framework with LlamaCloud, which addresses multi-tenancy, document-level access control, robust failure handling and permissioned ingestion across enterprise data sources.
What does LlamaIndex cost?
The open-source framework is free. LlamaCloud uses credit-based pricing with a monthly free tier; 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.
How do I know if LlamaIndex is the right framework for my project?
LlamaIndex is the strongest fit when your application depends on retrieving accurate context from a large body of private documents — especially complex formats like contracts, filings or technical manuals — and when you need agents that reason over that content, not just answer simple questions. A 30-minute call is usually enough to confirm fit and scope the entry point.
Get started
Ready to build your LlamaIndex solution?
Whether you are 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.