Enterprise RAG Development

Source authority, lineage, permissions, ingestion, retrieval, citations, freshness, and operating response form one path from approved content to each answer. Break that path anywhere and Enterprise RAG stops being inspectable.

A useful answer is only one part of the build. We connect approved sources to retrieval, response, citations, access controls, and operations so your team can investigate the path when the result is wrong, stale, or unsupported.

The operating owner takes over a traceable source-to-answer path, groundedness and citation findings, freshness controls, and a response plan for stale or unauthorized sources.

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Illustration of Enterprise RAG Development: a team wiring a document pipeline into a retrieval system

Some of the 500+ brands we've worked with

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  • Hyundai
  • Findeks
  • Aydem Perakende
  • Sompo Sigorta
  • Hisar
  • Sportive
  • Jollytur

Steps, gates, and who decides

How we work

We build one bounded knowledge path and keep its source, ingestion, retrieval, response, citation, access, and operating decisions visible throughout the work.

  1. Map sources and architecture

    The first design ties the priority use case to its approved sources, owners, identity rules, freshness expectations, and lineage. It also records who can add or remove a source later.

    AI assist
    The source material is grouped into candidate sources and access-rule clusters for review.
    Human gate
    Are the source authority, intended use, and owner explicit? Your knowledge owner confirms source authority and intended use.

  2. Build ingestion and retrieval

    We build the agreed source-to-answer path across ingestion, chunking, indexing, retrieval, reranking, context assembly, response, citations, and access controls. Each layer keeps its assumptions and exceptions beside the implementation.

    AI assist
    Flags exceptions and assumptions surfacing at each pipeline layer during the build.
    Human gate
    Can a source be followed from ingestion to the answer and its citation? The retrieval and citation design choices need your engineering lead's sign-off.

  3. Evaluate each layer

    Representative and adverse queries are reviewed at component level and end to end. We inspect whether generation masks weak retrieval, whether source permissions survive the path, and whether old material can still appear current.

    AI assist
    Generates adversarial queries to surface weak retrieval and lost permissions.
    Human gate
    Do the agreed retrieval, support, access, and freshness thresholds pass by critical slice? The decision owner accepts or rejects each threshold failure flagged in testing.

  4. Set operating ownership

    The operating model assigns monitoring and response for source changes, access events, stale content, and quality findings. Your owner accepts the release conditions and sets the next review point.

    AI assist
    Compiles monitoring rules and open risks into the operations runbook draft.
    Human gate
    Can the operating owner detect a stale or unauthorized source and follow the response path? The operating owner accepts the monitoring rules and review cadence.

Named artifacts you keep

What you get

The handoff combines the working RAG path with the test evidence and operating instructions needed to investigate and respond after release.

  • Architecture document

    Authoritative-source and lineage map

    The architecture ties authoritative sources to owners, identity rules, ingestion choices, indexing, and lineage.

  • Dataset

    Approved-content ingestion and indexing file

    The traceable path that prepares, chunks, indexes, and updates approved content for retrieval and reranking.

  • Test evidence

    Golden-query groundedness and citation findings

    Representative and adverse queries with results for retrieval relevance and recall, claim support, citation validity, access, and freshness.

  • Playbook

    Source-access and freshness response plan

    Ownership, monitoring, escalation, stop or rollback, correction, retest, and review steps for source, access, freshness, and quality incidents.

Scope and honest limits

When to bring us in

Use this engagement when the first RAG use case is defined and the application path now needs to be built, evaluated, and handed to an operating team.

A good fit when

  • The first knowledge use case is named, but authoritative sources and their owners are not connected to one release boundary.
  • Your representative queries and quality thresholds exist, yet freshness needs and application contracts have not been tested against them.
  • Source permissions are approved at ingestion, but nobody can show that lineage and access survive indexing, retrieval, and citation.
  • Your authoritative sources are known, yet ingestion, chunking, indexing, and lineage choices do not form one traceable source-to-answer design.
  • Retrieval and response components work separately, while reranking, citations, and access controls still break the complete knowledge path.
  • Your end-to-end answers look useful, but component tests cannot yet expose weak retrieval, stale sources, or lost permissions by critical slice.
  • Release conditions are being discussed, yet no operating owner holds the source changes, access events, freshness findings, and next review.

Better handled as other work when

  • You need the approved source set or retrieval path declared legally compliant. We hold the lineage, permission, and citation evidence, and your counsel rules on it.
  • You require a blanket promise that every answer stays accurate and grounded. The reviewed query set cannot make a probabilistic model infallible.
  • You need new source acquisition, production operation, or access remediation beyond the agreed knowledge path. Those tasks need separate scopes.

If one of these is closer to your situation, start here instead: View the parent service

Engineers who ship production AI

This is the part of Zeo that writes and ships code. Our senior engineers build agents, chatbots, and RAG pipelines, along with the automation and data work around them, and they keep operating those systems once they're live. We've worked with more than 500 brands since 2011.

Content we've produced on this topic

Articleby Burak PehlivanWhat is WebMCP? Agentic Web and Websites Interacting with AI AgentsArticleby Yiğit KonurMCP Server Architecture: State Management, Security & Tool Orchestration

Tools we use

Tools behind this work

  • LlamaIndex

    the pipeline connecting approved sources through retrieval to a cited response

  • Haystack

    the component-based pipeline structure a team can read and modify after handoff

  • Qdrant

    the retrieval engine the response and citation quality get built on top of

  • Ragas

    the groundedness score this page's own named evaluation step is built around

  • Langfuse

    the trace a team investigates when a result is wrong, stale, or unsupported

Next step

Build the first RAG use case as an inspectable system

Bring the first use case, approved sources, and representative queries. We'll define a build boundary that is useful enough to release and narrow enough to evaluate properly.
Talk to Zeo

Before you decide

Questions we hear a lot

We need the priority knowledge use cases, authoritative sources and owners, identity rules, representative queries, freshness needs, application contracts, and quality thresholds. Before sensitive sources enter the workspace, we check their purpose, access limits, confidentiality, retention, and who's responsible for them.