AI Frameworks & Agent Orchestration - LlamaIndex
Your documents are in the system.
Your AI still can't find the right answer.
We build and operationalize LlamaIndex retrieval pipelines, agent workflows, and data connectors on your actual corpus - contracts, CRM exports, product docs, financial records - so the answers your team gets are accurate and traceable.
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Operators and teams we've worked with
Most LlamaIndex builds stall between proof-of-concept and production use
LlamaIndex gives you a powerful set of primitives - data loaders, index types, query engines, and agent abstractions - but the gap between a notebook demo and a system your team trusts daily is wide. The failure modes are specific: naive chunking that splits context at the wrong boundaries, default top-k retrieval that returns plausible but wrong passages, embedding models chosen by convenience rather than domain fit, and no re-ranking step to catch noise before it reaches the LLM. Add multi-document reasoning, structured sources like your CRM or ERP, or agentic tool use, and the surface area for silent failure grows fast. Most teams hit a wall moving beyond a single clean PDF corpus.
Revenue Institute comes in at the architecture level. We audit your index design, chunking and overlap settings, retrieval pipeline, and prompt construction against your actual documents and the questions your users ask. We implement the components LlamaIndex provides - RouterQueryEngine for multi-index routing, SentenceWindowNodeParser or HierarchicalNodeParser for context-aware chunking, Cohere or cross-encoder re-rankers, and SubQuestionQueryEngine for multi-hop queries - configured for your data, not a tutorial dataset. The result returns defensible answers with source citations, not confident hallucinations.
What we do with LlamaIndex
What we build inside your LlamaIndex stack
Corpus audit and index architecture
Before writing code we map your document types, sizes, update frequency, and query patterns, then choose between VectorStoreIndex, SummaryIndex, KnowledgeGraphIndex, or a hybrid based on what your users actually ask. This decision alone determines whether retrieval is useful or noise.
Chunking and parsing strategy
Default fixed-size chunking destroys context in contracts, financial reports, and technical specs. We configure LlamaIndex's node parsers - SentenceWindowNodeParser, HierarchicalNodeParser, or custom ones - to preserve semantic units, tuning chunk size, overlap, and metadata against your specific corpus.
Retrieval pipeline and re-ranking
Top-k cosine similarity alone is rarely enough for production. We build multi-stage retrieval using LlamaIndex's RetrieverQueryEngine with hybrid BM25 plus vector search, then add a cross-encoder or Cohere re-ranker to filter before the LLM sees context - cutting hallucination on ambiguous queries.
Multi-agent and tool-use workflows
LlamaIndex's agent abstractions let you build systems that call APIs, run SQL queries, and reason across multiple indexes in a single turn. We implement these workflows - ReActAgent, FunctionCallingAgent, or custom loops - with proper tool definitions, error handling, and fallback logic.
Structured and unstructured data integration
Most real business questions span both a PDF and a database. We connect LlamaIndex's NLSQLTableQueryEngine or PandasQueryEngine alongside your vector indexes, route queries to the right source via RouterQueryEngine, and synthesize answers across both. This is where most DIY builds break.
Observability, evaluation, and iteration loop
We instrument your LlamaIndex pipeline with tracing via LlamaTrace, then run structured evaluation using its built-in modules - faithfulness, relevancy, context precision - against a golden question set from your actual users. You get a repeatable process for improving retrieval quality.
Our framework
How a LlamaIndex engagement runs
Discover and diagnose
We spend the first week with your data, your existing pipeline, and the people who use the output, documenting corpus characteristics, query taxonomy, and failure modes. Any working prototype is run against adversarial and edge-case questions to find where it breaks.
Build and validate
We implement the target architecture in your environment - your vector store, your cloud, your security constraints. Every retrieval and agent component is tested against your golden question set, not a generic benchmark. We iterate on chunking, retrieval, and prompts until scores pass.
Deploy and hand off
We deploy to your production environment with monitoring, logging, and a documented evaluation cadence so your team can detect drift when your corpus changes. We write the runbooks, train the internal owners, and stay available for the first production issues.
Why LlamaIndex is the right foundation for enterprise retrieval - and where it goes wrong in practice
LlamaIndex was designed for a specific problem: making large language models useful over private, domain-specific data. That focus shows in its architecture - a structured layer between your documents and the LLM, with node parsers that control how text is segmented, index types that determine how it is stored and retrieved, query engines that handle retrieval-to-synthesis, and agent abstractions for multi-step reasoning. For mid-market companies whose advantage lives in institutional knowledge - client contracts, product documentation, internal processes, financial history - this is the right tool category. The question is whether the implementation matches the ambition.
The failure mode we see most often is not a wrong tool choice but an underbuilt implementation. Teams use the default VectorStoreIndex with fixed 1024-token chunks, run a simple similarity search, and pass the top five results to the LLM. That works in demos where the corpus is clean and questions are predictable. In production - where documents have tables, headers, cross-references, and inconsistent formatting, and users ask questions the demo never anticipated - default settings produce confident wrong answers. Using the components correctly requires understanding both the library's architecture and your specific data characteristics at once.
What production-grade LlamaIndex looks like inside a real business operation
A production deployment involves several layers a prototype skips. At ingestion, documents go through a parser chosen for their format - PDFs with complex layouts need different handling than clean markdown or JSON exports from your CRM. Chunks are sized and overlapped to the semantic structure of the content, not a round number. Metadata - date, source system, type, author - is attached to every node so retrieval can filter before it ranks. At the retrieval layer, hybrid search combining dense vector similarity with sparse BM25 handles both semantic and keyword-dependent queries. A re-ranker sits between retrieval and synthesis to catch cases where cosine similarity returned a topically adjacent but contextually wrong passage.
For organizations with multiple data sources - a document corpus alongside a SQL database or live API - RouterQueryEngine routes each query to the appropriate index before synthesis. SubQuestionQueryEngine breaks complex multi-part questions into sub-queries, retrieves against each, and synthesizes a unified answer. These are not exotic features; they separate a system that answers simple lookups from one that handles the messy, multi-part questions real employees ask. Getting there requires deliberate architecture work, evaluation against real queries, and iteration - exactly what Revenue Institute brings to a LlamaIndex engagement.
We're vendor-agnostic
Other AI Frameworks & Agent Orchestration platforms we specialize in
Not sure LlamaIndex is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
LlamaIndex questions, answered
We already have a LlamaIndex prototype that mostly works. Do we need a full rebuild?
Usually not. We start with an audit of what you have - chunking config, index type, retrieval parameters, prompt templates - and identify the specific components causing failures. Most prototypes have two or three fixable problems rather than a fundamentally broken design. We fix those first and only recommend a rebuild if the underlying architecture is genuinely incompatible with your production requirements.
How does LlamaIndex compare to LangChain for our use case?
LlamaIndex is purpose-built around data indexing and retrieval. Its node parsers, index types, and query engine abstractions are more mature for RAG-heavy applications than LangChain's equivalent components. LangChain has broader tool integrations and a larger community for general agent workflows. If your primary problem is making AI answer questions accurately from your documents, LlamaIndex is usually the better fit. We work with both and will tell you honestly which one fits your situation.
What vector stores does LlamaIndex work with?
LlamaIndex has native integrations with most major vector stores - Pinecone, Weaviate, Qdrant, Chroma, pgvector, Milvus, and others. We work within whatever you already have or help you choose based on your scale, latency requirements, and infrastructure constraints. The vector store choice matters less than the chunking and retrieval design sitting on top of it.
How do we know if retrieval quality is actually improving?
We build a golden question set from real queries your users ask or would ask, then run LlamaIndex's built-in evaluation modules - faithfulness, answer relevancy, and context precision - against it before and after changes. This gives you a repeatable, quantifiable measure of improvement rather than a subjective sense that it feels better. We set this up as part of the engagement so you can run it yourself going forward.
Can LlamaIndex handle our data that updates frequently?
Yes, but it requires explicit design for it. LlamaIndex supports incremental ingestion and index updates, but you need a pipeline that detects new or changed documents, re-chunks and re-embeds them, and updates the index without a full rebuild. We design and implement that ingestion pipeline as part of the engagement, including handling deletions and document versioning, which most initial builds ignore entirely.
What does a typical engagement cost and how long does it take?
Scope drives both. A focused audit plus retrieval pipeline fix on an existing prototype can run a few weeks. A full build covering ingestion, multi-index routing, agent workflows, and evaluation infrastructure takes longer. We scope after the discovery conversation when we understand your corpus size, use case complexity, and internal team capacity. We do not quote before we know what we are actually building.
Do we need a dedicated ML engineer on our side to work with you?
No, but you need someone who owns the system after we leave. That person does not need to be an ML specialist - a strong backend engineer or a technically capable RevOps or data lead is enough. We write documentation and runbooks designed for the person who will maintain this, not for someone with a PhD. The goal is a system your team can operate and iterate on without us.
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