Enterprise LlamaIndex Engineering
The premier data framework for connecting private databases and unstructured documents with LLMs. We design, optimize, and deploy production-grade architectures utilizing the full capabilities of the LlamaIndex ecosystem.
LlamaIndex
Production Certified
LlamaIndex is our primary tool for building advanced Retrieval-Augmented Generation (RAG) structures. It connects LLMs to private corporate data sources, parsing unstructured text, and generating responses.
Our LlamaIndex Engineering Services
We deliver highly specialized, production-ready systems tailored to your technical requirements.
Hierarchical Document Indexing
We structure large PDFs and manuals into hierarchical nodes, allowing LLMs to search parent-child summaries.
Multi-Source Data Ingestion
We connect LlamaIndex to SQL, S3, Notion, and Slack databases, consolidating inputs into a central search layer.
Semantic Search Optimizations
We configure hybrid retrieval, using dense embeddings and sparse keyword BM25 indexes to retrieve relevant text.
Metadata Filtering Systems
We extract and index metadata (dates, authors, departments), restricting LLM access to authorized files.
LlamaIndex Tooling & Stack Integrations
We operate across the entire modern ecosystem surrounding LlamaIndex, deploying optimized dependencies and configurations.
LlamaIndex Core
Core packages used to parse documents and manage nodes.
Query & Ingestion
Retrievers and custom splitters structuring content blocks.
Storage Adapters
Database connectors routing text structures to indexes.
Production-Grade Engineers
Top 1% Seniority
Specialist data engineers designing custom RAG pipelines.
Immediate Velocity
Optimized query speeds utilizing hierarchical indexing and node parsing.
Compliance Native
Reliable metadata tagging and extraction for secure, compliant search.
Proven Results with LlamaIndex
Explore how we leverage LlamaIndex to build business-critical platforms and achieve operational milestones.
Enterprise Knowledge Base Handles 10k Daily Staff Queries
We built an internal Q&A RAG engine using LlamaIndex, connecting it to Notion databases and S3 files.
Clinical Document Parser Extracts Patient Medical Structures
Designed a medical record parser using LlamaIndex node filters, extracting patient histories for physician reviews.
Flexible Engagement Models
Scale your engineering capacity dynamically. We integrate seamlessly into your operations with three battle-tested engagement models.
Staff Augmentation
Inject senior AI and MLOps engineers directly into your active squads. Rapidly scale resources with dedicated support under your management.
Dedicated Team
A self-governing team of engineers, project managers, and QA specialists built specifically to design, build, and support your proprietary AI pipelines.
Full Build & Deliver
Fixed-scope or milestone-driven development. We take ownership from requirements definition and MVP design to final production handoff.
Compliance-First Deployment Standards
We integrate safety controls natively. Every LlamaIndex application is architected to satisfy strict global compliance and privacy policies.
SOC 2 Type II
Logical isolation & logs
HIPAA PHI
PHI data de-identification
ISO/IEC 27001
International safeguards
Common LlamaIndex Questions
Why use LlamaIndex instead of LangChain for RAG? expand_more
What is a Node in LlamaIndex? expand_more
How do you prevent the LLM from hallucinating answers? expand_more
Do you support semantic search across languages? expand_more
How do you evaluate RAG accuracy? expand_more
Accelerate Your AI Project with LlamaIndex
Schedule an architectural blueprint review session with our senior LlamaIndex engineers to map your database, compliance, and MLOps strategies.