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CASE STUDIES · RESEARCHED 28 OCTOBER 2026

LlamaIndex AI Case Studies 2026: Real Customer Results

What happens when real teams connect documents, databases and business knowledge to AI? This evidence-led collection examines LlamaIndex implementations across insurance, research, HR, software and more—separating documented results from marketing claims and unmeasured outcomes.

Explore 12 stories Understand the technology
ILLUSTRATIVE · DOCUMENT TO AI
Raw
documents
AI
processing
Structured
knowledge
Useful AI
apps

A conceptual pipeline. Individual customer projects use different components and configurations; the visual is not a claimed deployment diagram.

12Documented customer accounts
11Industry categories in sample
9Accounts with reported numeric outcomes
12Case-specific primary story URLs
Inside this research report
Case libraryIndustriesCustomer storiesReported resultsArchitectureBefore / afterComparisonInsightsFAQsSources
01 · THE CUSTOMER LIBRARY

Explore real-world implementations

Find a customer by industry, technology or strength of published evidence. Every named organization below links to its own official LlamaIndex customer story.

12 case studies match. “Level A” means the customer or vendor reports a number; it does not mean independent auditing.

InsuranceLEVEL A

Pathwork

Underwriting records become usable context

Pathwork replaced a brittle medical-record parsing pipeline with a more scalable ingestion layer for life-insurance underwriting.

LlamaParse RAG
8×Read study
AviationLEVEL A

Jeppesen

One reusable platform replaces siloed chatbot builds

Jeppesen’s engineering team assembled a unified agent framework based on LlamaIndex event-driven workflows.

Workflows AI Agents
512 → 64hRead study
Financial ServicesLEVEL A

Experian

A faster first response for AI customer-support agents

Experian describes customer-service agents using LlamaIndex for retrieval and production development.

RAG AI Agents
8 → 1sRead study
Enterprise SoftwareLEVEL A

StackAI

Parsing becomes infrastructure for document agents

StackAI uses LlamaParse in its enterprise agent builder to transform mixed customer files into retrieval-ready content.

LlamaParse RAG
1M+Read study
Sales AutomationLEVEL A

11x.ai

Onboarding context for autonomous sales agents

11x integrated LlamaParse to help its sales agents ingest company material and generate more relevant outbound content.

LlamaParse AI Agents
3 daysRead study
Life SciencesLEVEL A

MavenBio

Making scientific figures searchable

MavenBio routes visual-heavy biomedical publications and regulatory materials through LlamaParse for downstream research intelligence.

LlamaParse RAG
10–20×Read study
Real EstateLEVEL A

CondoScan

From condo-paperwork overload to searchable risk reports

Calgary-based CondoScan parses condominium records and uses agent workflows to help buyers assess property risks.

LlamaParse RAG
Weeks → minutesRead study
Data InfrastructureLEVEL B

SkySQL

Natural-language questions grounded in database schemas

SkySQL uses LlamaIndex to orchestrate agents that translate conversational questions into SQL for operational data.

RAG AI Agents
Outcome not quantifiedRead study
Creator TechnologyLEVEL B

Delphi

Cleaner source content for AI creator “minds”

Delphi parses creator PDFs, transcripts and spreadsheets into a knowledge pipeline supporting personalized AI mentor experiences.

LlamaParse RAG
Outcome not quantifiedRead study
HR TechnologyLEVEL A

Netchex

Employee answers grounded in HR handbooks

Netchex built AskHR and Netchex AI to answer workforce queries against employers’ own HR policies and benefits documents.

LlamaIndex Framework RAG
~1 monthRead study
AI ConsultancyLEVEL A

Scaleport.ai

Less ingestion engineering, faster client prototypes

Scaleport.ai says LlamaParse simplified building custom AI applications for clients across several industries.

LlamaParse RAG
50–60%Read study
Enterprise SoftwareLEVEL B

Lyzr

Context retrieval inside a broader agent-building platform

Lyzr uses LlamaIndex components for access to customer data and contextual retrieval within its autonomous-agent platform.

LlamaIndex Framework RAG
Outcome not quantifiedRead study
02 · SECTOR VIEW

Where are these customers applying AI?

Bars reflect only the twelve cases selected for this article—not market share, adoption by all LlamaIndex users, or global sector rankings. Choose a sector to filter the library.

Case studies by industry

HOW TO READ THIS SAMPLE

Different problems, a shared foundation.

The stories cover visual-heavy parsing, policy-document retrieval, natural-language database queries and orchestration of business agents. Their architectures differ significantly.

  • Parsing: often appears where scanned or complex documents hold the valuable information.
  • RAG: supports answers grounded in documents, indexed knowledge or databases.
  • Workflows: helps teams organize multi-step or stateful applications, not simply summarize a PDF.

Clicking a bar changes the customer filter. The counts update only when the editorial case list is changed.

Open filtered library ↗
03 · INSIDE THE DEPLOYMENTS

The problem, the implementation, the evidence

Each story separates the business challenge, described technical design, reported outcomes and what the source does not establish. Photos and diagrams appear only when sourced from the official customer story and are never cropped.

CUSTOMER STORY 01 / 12

Pathwork: Underwriting records become usable context

InsuranceLevel A · reported measurable result

Pathwork replaced a brittle medical-record parsing pipeline with a more scalable ingestion layer for life-insurance underwriting.

01

01 / The challenge

Insurance cases arrive with medical records, handwritten notes, lab reports and older scanned PDFs. Pathwork’s original page-by-page model pipeline was hard to maintain and could not keep up with rising document volume.

02 / What the team built

LlamaIndex-supported ingestion parses incoming medical records into information used by downstream underwriting systems. The company also parses carrier guidelines and refreshes retrieval-based underwriting knowledge.

03 / Publicly described architecture

Reported inputs include medical PDFs and underwriting manuals; parsing and downstream RAG are documented. The customer does not publish a complete system diagram of all models and infrastructure.

04 / Implementation caveat

Weak scans from the 1970s and mixed layouts required more robust processing. The published account does not specify a controlled parsing-accuracy benchmark.

8×Reported capacity increase5,000 → 40,000 weekly units; source mixes “documents” and “pages”.
Pathwork workflow diagram from LlamaIndex customer story
Official customer-story architecture diagram · Source: LlamaIndex customer story. Diagram represents that customer’s published system, not all LlamaIndex deployments.
RESULT & LESSON

Pathwork reports an eightfold increase in weekly processing capacity. Its impact section describes 5,000 to 40,000 documents per week, while other sections call the 40,000 figure pages per week.

What to learn: Parsing reliability is an operational constraint, not just a model-quality issue. Verify units before copying throughput claims.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 02 / 12

Jeppesen: One reusable platform replaces siloed chatbot builds

AviationLevel A · reported measurable result

Jeppesen’s engineering team assembled a unified agent framework based on LlamaIndex event-driven workflows.

02

01 / The challenge

Teams had independently built chatbots, duplicated integrations and navigated compliance checks repeatedly. Creating an agent from scratch reportedly required about 512 engineering hours.

02 / What the team built

A five-to-seven-person team built its Unified Chatbot Framework with reusable templates, connectors, policy controls, configurable models and vector stores. LlamaIndex workflows coordinate agent state and execution.

03 / Publicly described architecture

The customer names GraphQL, SQL, REST, Neo4j and Databricks connectors, several model providers, and Azure AI Search or Qdrant retrieval options. These are supported choices, not a claim that every agent uses each.

04 / Implementation caveat

Individual teams needed a shared compliance and deployment standard. The published article does not explain its hours-saved measurement or audit method in full.

512 → 64hReported effort per agent1,792 hours reported already saved; ~4,900 hours/year only forecast.
RESULT & LESSON

Jeppesen reports a development-effort reduction from 512 to 64 hours per agent, with 1,792 collective hours already saved. Approximately 4,900 annual hours is only a projection.

What to learn: A reusable, policy-aware agent platform can eliminate repeated setup work across enterprise teams.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 03 / 12

Experian: A faster first response for AI customer-support agents

Financial ServicesLevel A · reported measurable result

Experian describes customer-service agents using LlamaIndex for retrieval and production development.

03

01 / The challenge

Support agents need relevant information quickly enough to maintain conversational responsiveness and deliver useful answers.

02 / What the team built

The official customer listing credits LlamaIndex with retrieval improvements and faster deployment. The full technical write-up is not available as text on the linked page.

03 / Publicly described architecture

LlamaIndex-based customer-support agent and retrieval are stated; exact model, index, database and infrastructure are not publicly specified in the accessible source.

04 / Implementation caveat

Latency depends on model configuration, infrastructure and streaming. The accessible public material does not disclose test population or measurement conditions.

8 → 1sTime to first tokenCompany-published comparison; test methodology not supplied. NPS increase unquantified.
RESULT & LESSON

LlamaIndex’s customer listing reports time to first token falling from eight seconds to one second. It also says NPS improved but does not publish a specific change in NPS.

What to learn: Do not confuse initial response latency with total answer time or customer-satisfaction uplift.

Evidence: 28 Jan 2026 (listing) · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 04 / 12

StackAI: Parsing becomes infrastructure for document agents

Enterprise SoftwareLevel A · reported measurable result

StackAI uses LlamaParse in its enterprise agent builder to transform mixed customer files into retrieval-ready content.

04

01 / The challenge

Enterprise clients uploaded scanned insurance forms, finance reports, spreadsheets and other inconsistent files. Standard PDF readers and OCR results varied too much for dependable downstream agents.

02 / What the team built

StackAI placed the LlamaParse API at its core ingestion step, connecting its structured outputs to knowledge bases used by downstream AI agents.

03 / Publicly described architecture

The case confirms LlamaParse API integration and knowledge-base ingestion. It does not publish all private embedding, storage, model or evaluation configurations.

04 / Implementation caveat

Small upstream parsing errors can undermine downstream answers. Customers’ input quality and processing-volume needs are highly variable.

1M+Documents processedCumulative customer-reported volume; reporting period not disclosed.
RESULT & LESSON

StackAI reports more than one million documents processed for enterprise customers. Accuracy improvements are described qualitatively, without a published accuracy percentage or study period.

What to learn: When an agent product spans many industries, a consistent parsing interface reduces one-off engineering.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 05 / 12

11x.ai: Onboarding context for autonomous sales agents

Sales AutomationLevel A · reported measurable result

11x integrated LlamaParse to help its sales agents ingest company material and generate more relevant outbound content.

05

01 / The challenge

Sales customers supplied PDFs, presentations, website information and other content; manually reading and rewriting that context made onboarding harder to scale.

02 / What the team built

The company added document parsing to the agent knowledge-base experience, choosing parsing fidelity according to content complexity and connecting processing through its TypeScript workflow.

03 / Publicly described architecture

The customer explicitly mentions TypeScript SDK, asynchronous webhook retrieval, varied documents and configurable parsing-depth selection. Its complete agent orchestration stack is not detailed.

04 / Implementation caveat

11x tried a home-built OCR approach but found maintenance, engineering effort and quality difficult. SDK issues were addressed during testing according to the customer.

3 daysPrototype to productionReported integration timeline, not customer ROI or a controlled onboarding study.
RESULT & LESSON

The customer reports moving the parsing integration from prototype to production in three days. The case title references ramp-time shrinking to days, but gives no uniform before/after SDR ramp-time trial.

What to learn: Document-ingestion capability can remove a product-onboarding bottleneck without proving every sales KPI improved.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 06 / 12

MavenBio: Making scientific figures searchable

Life SciencesLevel A · reported measurable result

MavenBio routes visual-heavy biomedical publications and regulatory materials through LlamaParse for downstream research intelligence.

06

01 / The challenge

Traditional Python PDF parsers could capture standard text but missed plots, posters, figure legends and other scientific visual evidence.

02 / What the team built

Visual-rich documents are classified and sent to LlamaParse. Descriptions and structured text are then enriched and connected to MavenBio’s biomedical knowledge and retrieval layers.

03 / Publicly described architecture

The customer documents file routing, LlamaParse visual description, model-selection tuning, size optimization and asynchronous webhooks. The end-to-end platform also uses domain ontologies and LLM enrichment.

04 / Implementation caveat

Complex charts and figures must retain their scientific context. No independent document-level accuracy benchmark or quality baseline is published.

10–20×Reported analytical workflow speedBroad platform outcome; not a controlled LlamaParse-only improvement.
MavenBio official biopharma evidence interface screenshot
Official customer-provided product screenshot · Source: LlamaIndex customer story. Diagram represents that customer’s published system, not all LlamaIndex deployments.
RESULT & LESSON

MavenBio reports overall analytical workflows 10–20 times faster for users. That end-to-end speedup is attributed to its wider platform, not isolated to LlamaParse.

What to learn: Visual parsing matters most when charts and diagrams carry decision-critical scientific evidence.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 07 / 12

CondoScan: From condo-paperwork overload to searchable risk reports

Real EstateLevel A · reported measurable result

Calgary-based CondoScan parses condominium records and uses agent workflows to help buyers assess property risks.

07

01 / The challenge

Property review can involve 10–40 records, budgets and meeting minutes spanning hundreds of pages. Manual expert reviews may take around two weeks.

02 / What the team built

LlamaParse prepares records for retrieval while LlamaIndex supports ingestion, reasoning and question-answering across condo documents and relevant regulations.

03 / Publicly described architecture

The published customer architecture identifies Google Cloud Storage, OCR, LlamaParse, Pinecone, MongoDB, FastAPI, Cloud Run and agent workflows. The diagram is customer-provided and may change.

04 / Implementation caveat

Information can be legally and financially consequential. The source does not publish a controlled risk-detection accuracy evaluation.

Weeks → minutesReported document review changeUncontrolled customer claim; human oversight remains essential for property decisions.
CondoScan customer-published multi-stage technology architecture
Official customer-story technical architecture · Source: LlamaIndex customer story. Diagram represents that customer’s published system, not all LlamaIndex deployments.
RESULT & LESSON

CondoScan says its production solution can reduce reviews from weeks to minutes. That is the vendor/customer description, not an independently verified comparison.

What to learn: Retrieval and agents can make large document sets easier to inspect, but should not replace professional due diligence.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 08 / 12

SkySQL: Natural-language questions grounded in database schemas

Data InfrastructureLevel B · documented use

SkySQL uses LlamaIndex to orchestrate agents that translate conversational questions into SQL for operational data.

08

01 / The challenge

Complex schemas and weak table relationships can cause inaccurate joins, stale assumptions and misleading database answers.

02 / What the team built

SkySQL combines retrieved schema context, query generation, expert correction and response evaluation to guide its text-to-SQL agents.

03 / Publicly described architecture

The case names LlamaIndex’s SQL Table Retriever Engine, human context editing, LLM-as-judge review and MariaDB Vector. SkySQL describes in-database execution and fallback mechanisms.

04 / Implementation caveat

Schema evolution, permissions, cost and accuracy need continuing attention. Published claims of better SQL correctness lack a numerical benchmark.

Not reportedNo attributable numerical outcomeQualitative findings remain useful but are not a quantitative benchmark.
RESULT & LESSON

SkySQL reports improved query quality and developer experience, but does not disclose comparable quantitative production outcomes.

What to learn: Text-to-SQL needs schema grounding, explicit evaluation and a route for expert corrections.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 09 / 12

Delphi: Cleaner source content for AI creator “minds”

Creator TechnologyLevel B · documented use

Delphi parses creator PDFs, transcripts and spreadsheets into a knowledge pipeline supporting personalized AI mentor experiences.

09

01 / The challenge

Malformed PDFs, tables and unusual encodings introduced ingestion failures and weakened source citation quality.

02 / What the team built

Delphi adopted LlamaParse, including balanced parsing modes, to create readable Markdown that feeds the platform’s storage and knowledge representation.

03 / Publicly described architecture

The story explicitly mentions a downstream S3 data lake and knowledge graph as well as markdown-first parsing. Exact citation evaluation metrics are not disclosed.

04 / Implementation caveat

Citation fidelity depends on retaining source text and document structure. A numerical benchmark or cost comparison is not provided.

Not reportedNo attributable numerical outcomeQualitative findings remain useful but are not a quantitative benchmark.
RESULT & LESSON

Delphi describes fewer manual ingestion fixes and better citation fidelity, but publishes no measured accuracy or hours-saved figure.

What to learn: Poor parsing can affect citation trust even when the language model behaves as designed.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 10 / 12

Netchex: Employee answers grounded in HR handbooks

HR TechnologyLevel A · reported measurable result

Netchex built AskHR and Netchex AI to answer workforce queries against employers’ own HR policies and benefits documents.

10

01 / The challenge

Small HR teams were handling recurring employee questions while handbooks and benefits information remained difficult to search.

02 / What the team built

LlamaIndex ingests Office files and PDFs, chunks policy content and retrieves relevant excerpts before drafting cited employee answers.

03 / Publicly described architecture

The story names Azure OpenAI embeddings, customizable retrievers, citation to source pages, query engines and semantic routing across document sets.

04 / Implementation caveat

HR policy information is sensitive and can become stale. The public article does not present a formal answer-accuracy test or reduction in staff workload.

~1 monthReported build timelineTwo engineers; time to build is not measured employee-hours savings.
RESULT & LESSON

Netchex says two engineers built the system in around one month, with approximately 20% of their time devoted to LlamaIndex-related work. Business productivity gains remain unquantified.

What to learn: Document citations and routing matter when answers must trace back to company-specific HR policies.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 11 / 12

Scaleport.ai: Less ingestion engineering, faster client prototypes

AI ConsultancyLevel A · reported measurable result

Scaleport.ai says LlamaParse simplified building custom AI applications for clients across several industries.

11

01 / The challenge

Custom ingestion pipelines and inadequate OCR made proofs of concept laborious and delayed demonstrations of value.

02 / What the team built

The firm used LlamaParse plus LlamaIndex indexing and retrieval tooling so teams could spend less effort writing document-ingestion plumbing.

03 / Publicly described architecture

Its customer story describes use of document parsing, OCR, indexing and retrieval, without disclosing client-specific infrastructure or a public evaluation dataset.

04 / Implementation caveat

The reported comparison applies to one client project and a prior equivalent application; the source does not disclose a normalized labor audit.

50–60%Fewer development hoursSelf-reported, single client comparison; baseline hours not published.
RESULT & LESSON

Its CTO reports roughly 50–60% fewer development hours for one client compared with an equivalent earlier project. This should not be generalized across all deployments.

What to learn: The most defensible automation ROI sometimes starts with developer effort rather than flashy model metrics.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
CUSTOMER STORY 12 / 12

Lyzr: Context retrieval inside a broader agent-building platform

Enterprise SoftwareLevel B · documented use

Lyzr uses LlamaIndex components for access to customer data and contextual retrieval within its autonomous-agent platform.

12

01 / The challenge

Agents serving different use cases must work across heterogeneous customer documents, connectors and retrieval configurations.

02 / What the team built

Lyzr describes LlamaIndex connectors and retrieval features behind custom data access and its AutoRAG selection process.

03 / Publicly described architecture

The customer story specifies data connectors, tailored chunk settings and retrieval optimization. Proprietary full-agent deployment architectures are not disclosed.

04 / Implementation caveat

Commercial growth has many causes, from distribution to product fit; technical dependency does not prove an individual library generated revenue.

Not reportedNo attributable numerical outcomeQualitative findings remain useful but are not a quantitative benchmark.
RESULT & LESSON

A LlamaIndex story reports Lyzr’s ARR increasing from approximately $100,000 to $1.5 million in under 60 days. No causal attribution to LlamaIndex is established, so the figure is not treated as a LlamaIndex performance metric.

What to learn: Treat revenue growth in a technology vendor’s customer story as background, not proof of direct platform ROI.

Evidence: Date not disclosed · Reviewed 28 Oct 2026 · Customer/vendor-published, not independent auditOriginal customer story ↗
04 · REPORTED BUSINESS IMPACT

What do the published numbers actually say?

These figures come from customer accounts hosted by LlamaIndex. They use different units, periods and methods; they must not be averaged into a single AI “performance score.” Select a category to focus the dashboard.

ScaleA
8×

Pathwork

Reported capacity increase

5,000 → 40,000 weekly units; source mixes “documents” and “pages”.Read original evidence ↗
SpeedA
512 → 64h

Jeppesen

Reported effort per agent

1,792 hours reported already saved; ~4,900 hours/year only forecast.Read original evidence ↗
SpeedA
8 → 1s

Experian

Time to first token

Company-published comparison; test methodology not supplied. NPS increase unquantified.Read original evidence ↗
SpeedA
3 days

11x.ai

Prototype to production

Reported integration timeline, not customer ROI or a controlled onboarding study.Read original evidence ↗
SpeedA
10–20×

MavenBio

Reported analytical workflow speed

Broad platform outcome; not a controlled LlamaParse-only improvement.Read original evidence ↗
SpeedA
Weeks → minutes

CondoScan

Reported document review change

Uncontrolled customer claim; human oversight remains essential for property decisions.Read original evidence ↗
Build effortA
~1 month

Netchex

Reported build timeline

Two engineers; time to build is not measured employee-hours savings.Read original evidence ↗
Build effortA
50–60%

Scaleport.ai

Fewer development hours

Self-reported, single client comparison; baseline hours not published.Read original evidence ↗

Caution: reported metrics include customer self-assessments, implementation timelines and end-to-end platform outcomes. Details and attribution limits are included in each card.

05 · UNDER THE HOOD

How does a document-powered AI application work?

A clickable conceptual architecture, not a reconstruction of a particular customer’s private system. Click any step to understand its role; some deployments skip or combine steps.

SELECTED STAGE

Data sources

Organizations start with records, manuals, spreadsheets, databases or other business information. Permission and data quality checks matter before AI processing begins.

Educational diagram · real deployment components vary.
MINI INTERACTIVE LAB

One document, five representations

Illustrative sample insurance intake only. There is no live LlamaIndex API connection and the synthetic content does not describe a real customer.

INTAKE FORM · SYNTHETIC SAMPLE\nPolicy: EXAMPLE-014\nClaimant: Alex Example\nReceived: 18 Oct 2026\nAttached: 4-page scanned report\nRequested: Confirm whether key documents are present.
06 · EXPLAINING THE CHANGE

Before and after: what could change?

These are conceptual education scenarios, not measured results from a named customer. Select a workflow and compare a manual or limited process with an AI-assisted alternative.

Select an application

CONCEPTUAL · BEFOREManual reading across scanned records

An analyst opens PDFs one by one, locates the relevant fields and manually checks whether important attachments are present.

Illustration only · no measured customer baseline.
07 · SIDE-BY-SIDE EVIDENCE

Compare implementations without mixing their metrics

Use the same library filters above; this table updates with them. Sort names alphabetically or expand a row to inspect its most important caveat.

IndustryChallengeTechnologyPublished outcomeLevelMore
PathworkInsuranceUnderwriting records become usable contextLlamaParse, RAG8× — Reported capacity increaseA
JeppesenAviationOne reusable platform replaces siloed chatbot buildsWorkflows, AI Agents512 → 64h — Reported effort per agentA
ExperianFinancial ServicesA faster first response for AI customer-support agentsRAG, AI Agents8 → 1s — Time to first tokenA
StackAIEnterprise SoftwareParsing becomes infrastructure for document agentsLlamaParse, RAG1M+ — Documents processedA
11x.aiSales AutomationOnboarding context for autonomous sales agentsLlamaParse, AI Agents3 days — Prototype to productionA
MavenBioLife SciencesMaking scientific figures searchableLlamaParse, RAG10–20× — Reported analytical workflow speedA
CondoScanReal EstateFrom condo-paperwork overload to searchable risk reportsLlamaParse, RAGWeeks → minutes — Reported document review changeA
SkySQLData InfrastructureNatural-language questions grounded in database schemasRAG, AI AgentsNot quantifiedB
DelphiCreator TechnologyCleaner source content for AI creator “minds”LlamaParse, RAGNot quantifiedB
NetchexHR TechnologyEmployee answers grounded in HR handbooksLlamaIndex Framework, RAG~1 month — Reported build timelineA
Scaleport.aiAI ConsultancyLess ingestion engineering, faster client prototypesLlamaParse, RAG50–60% — Fewer development hoursA
LyzrEnterprise SoftwareContext retrieval inside a broader agent-building platformLlamaIndex Framework, RAGNot quantifiedB
08 · CROSS-CASE INTERPRETATION

Practical lessons for enterprise teams

These themes are editorial inferences from the selected customer stories—not claims about the entire AI industry. Successful integrations still need validation, maintenance and governance.

01

Start with the data

Messy PDFs, charts and inconsistent encodings can break downstream retrieval. Test parsing on representative production documents, not ideal examples.

02

Measure more than latency

Time to first token, full response time, retrieval quality, citation precision and user satisfaction answer different questions.

03

Design for review

Insurance, financial, medical and property decisions can be consequential. Provide clear sources, confidence processes and escalation to specialists.

04

Respect data governance

Before sending documents to hosted AI services, assess sensitive information, security controls, retention and region-specific obligations.

05

Track actual costs

Include parsing credits, model tokens, embedding updates, storage, retries and human quality checks. “Faster development” is not the same as lower total cost.

06

Test the whole workflow

Build an evaluation set containing varied layouts and real user questions; rerun it after changes to prompts, retrieval settings and models.

What the evidence cannot prove

The stories are primarily hosted by the technology vendor and frequently contain customer statements. They establish that named organizations publicly described particular implementations; they do not automatically establish third-party validation, controlled experiments or the causal share of LlamaIndex in every business outcome. Level A flags reported numbers, not independently audited numbers.

09 · QUICK ANSWERS

Questions readers ask about LlamaIndex deployments

Which real organizations use LlamaIndex?

The twelve documented examples in this report include Pathwork, Jeppesen, Experian, StackAI, 11x, MavenBio, CondoScan, SkySQL, Delphi, Netchex, Scaleport.ai and Lyzr. Each has a named customer story linked below.

What is the difference between LlamaIndex and LlamaParse?

LlamaIndex is a framework for connecting data to language-model applications, including retrieval and workflows. LlamaParse is a document-parsing service for transforming complex inputs into more usable machine-readable content. Customer systems may use one or both.

Does Level A mean independently verified ROI?

No. It means a named customer, an implementation, and a publicly reported numerical outcome appear in traceable material. Here, most numbers remain vendor- or customer-reported without a disclosed independent audit.

Can LlamaParse process complex scientific PDFs?

MavenBio’s customer story describes using LlamaParse for plots, figures and visually dense publications. Quality depends on document conditions and needs testing for each use case.

Is RAG the same thing as an AI agent?

No. Retrieval-augmented generation (RAG) supplies relevant external context for generating answers. An agent may decide which tools to call, sequence tasks or maintain state. Agents often use retrieval, but not every RAG application is an agent.

Can a company copy these customer architectures directly?

Only if the necessary components, data permissions and operational requirements fit. Many published stories omit private infrastructure or evaluation details, so diagrams should be treated as educational references rather than turnkey blueprints.

Are there independently measured accuracy improvements?

The selected public stories offer qualitative quality claims, but few disclose standardized test sets, accuracy baselines or independent quality evaluations. Reproduce evaluation with your own representative datasets.

How should developers begin?

Choose a narrow use case, prepare representative source documents, prototype ingestion and retrieval, measure grounded-answer quality, add human review for consequential decisions and define deployment/security costs before scaling.

10 · SOURCE DESK

Sources, evidence and research notes

Reviewed 28 October 2026. The references below are direct customer-specific pages on LlamaIndex’s official website; the technology documentation and customer directory are separately linked. Publication dates are not assigned when the public page does not clearly expose them.

  1. Pathwork — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  2. Jeppesen — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  3. Experian — official LlamaIndex customer account ↗Primary customer/vendor account · 28 Jan 2026 (listing) · Level A
  4. StackAI — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  5. 11x.ai — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  6. MavenBio — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  7. CondoScan — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  8. SkySQL — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level B
  9. Delphi — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level B
  10. Netchex — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  11. Scaleport.ai — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level A
  12. Lyzr — official LlamaIndex customer account ↗Primary customer/vendor account · Date not disclosed · Level B
RESEARCH CONCLUSION

The real story is the work between the model and the data.

Across this sample, the strongest operational lessons are about preparing usable context, standardizing workflows and measuring the right outcomes. The evidence is promising—but its limits should stay visible alongside its successes.