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Cake for Enterprise RAG

Securely index, retrieve, and inject internal context into LLMs with a scalable, cloud-agnostic, composable, and compliance-ready RAG stack built on open source.

 

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Overview

Retrieval-augmented generation (RAG) gives LLMs real context for better answers. But at the enterprise level, the context is massive, fragmented, and access-restricted. Moving beyond demos requires production-grade ingestion, fine-tuned access controls, and traceable responses you can defend in audits.

Cake gives you the full RAG stack: ingest data from S3, SaaS APIs, or SQL; chunk, embed, and store it in performant vector DBs like Weaviate; and orchestrate retrieval pipelines with open tooling like LangChain and LlamaIndex. Everything is cloud-agnostic, composable, and auditable for real-world enterprises.

From regulated industries to internal knowledge management, Cake makes it easy to move from pilot to production without duct tape, vendor lock-in, or surprise costs.

Key benefits

  • Deploy faster without vendor lock-in: Use open-source components and Cake’s orchestration to build quickly and own your stack. Cake ensures the stack stays up-to-date with the latest technologies, giving you the best-in-class performance.

  • Secure your data: Apply fine-grained access controls, data masking, and audit-ready workflows by default.

  • Adapt as you scale: Integrate with any data source or environment using a fully composable RAG stack.

  • Improve performance over time: Monitor retrieval quality, identify gaps, and continuously fine-tune models and prompts for better results.

  • Support real-time and batch use cases: Run low-latency RAG for chat and decision support, or schedule document processing and summarization workflows at scale.

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Increase in
MLOps productivity

 

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Faster model deployment
to production

 

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Annual savings per
LLM project

EXAMPLE USE CASES

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Composable RAG workflows built
on your terms with Cake

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Internal knowledge search

Empower employees with natural-language access to internal docs, wikis, policies, and support materials.

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Customer support intelligence

Retrieve relevant product guides, contracts, and CRM records to assist support reps or fine-tune AI responses.

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Regulated industry applications

Provide grounded answers in finance, legal, or healthcare, with full traceability and data controls.

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AI assistants for employee workflows

Equip teams with agents that can answer questions, complete tasks, or generate content using internal knowledge, streamlining day-to-day operations.

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Contract and policy analysis

Allow legal and compliance teams to search and summarize terms, clauses, or obligations across thousands of documents instantly.

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Enterprise-wide search augmentation

Upgrade traditional search portals with AI-assisted answers grounded in real-time data from SharePoint, Confluence, Notion, and more.

THE CAKE DIFFERENCE

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From quick demos to production-grade
RAG systems

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Prototype RAG

Fast to build, but not built to last: Notebook-level demos with hardcoded prompts and limited observability.

  • Hardcoded prompts and simple query-to-response loops
  • No access control, data isolation, or audit trails
  • Fails to scale across teams, tools, or data domains
  • No built-in evals or cost monitoring to improve over time

Result:

Works in a demo, breaks in enterprise environments

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Enterprise RAG with Cake

Secure, scalable, and observable by design: Cake gives you a modular stack to deploy goal-driven RAG systems across your org.

  • Deploy with access control, logging, and full traceability
  • Integrate structured and unstructured data sources
  • Run evals, tune retrieval logic, and monitor cost and latency
  • Scale across teams with multi-tenant pipelines and reusable workflows

Result:

Production-grade RAG with enterprise security, governance, and performance

BLOG

How to build a scalable RAG stack in 48 hours

See how teams go from zero to production-ready retrieval with Cake’s modular infrastructure. No glue code. No vendor lock-in. Just fast, open-source orchestration that works.

Read More >

IN DEPTH

Structure your data before you retrieve it

RAG is only as good as your inputs. Learn how Cake automates document parsing with LLMs and OCR, turning PDFs, emails, and forms into clean, queryable context.

Read More >

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“

"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

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Scott Stafford
Chief Enterprise Architect at Ping

Read The Case Study
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“

"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

Read the case study
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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

COMPONENTS

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Tools that power Cake's RAG stack

 

Frequently asked questions

What is Enterprise RAG?

Enterprise RAG (retrieval-augmented generation) combines large language models with your proprietary data to generate accurate, context-aware responses. Cake lets you build these systems with full control over orchestration, retrieval, inference, and compliance.

How does Cake support enterprise-grade RAG?

Can I use my own vector store or model with Cake?

What kind of RAG use cases can I build with Cake?

How does Cake handle security and compliance for RAG?

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