Enterprise RAG Proof-of-Concept

The Enterprise RAG workbench is an in-house AI environment: built by le dot on your existing infrastructure, operated on your own data, adapted to your own processes and under your own control. Instead of renting every AI function individually from external providers, a platform emerges in your own operation, running several applications.

AI only becomes valuable once it works on your own data and workflows, with source citations and without leakage to external model providers. The workbench carries exactly such applications, from finding knowledge through automating work to your own development agents, and the sovereign infrastructure pays off across all of them.

Typical starting points

  • an in-house, controlled AI capability is to be built up rather than buying individual AI functions from external providers: an Enterprise RAG workbench is built on your own or Swiss infrastructure
  • a concrete first use case is available to prove the value: from it emerges a working AI application with a web interface
  • sensitive data must not leave the house and the AI has to adapt to your own processes: data flows, source citations and process integration are documented in the security statement and architecture document

Outcomes

The first use case produces a working result rather than a presentation. The concrete deliverables are:

  • a working AI application on your own workbench with a web interface
  • a technical architecture document
  • a security statement

The rollout decision thus becomes fact-based: which value is confirmed, which language model fits and which further applications are worthwhile on the same workbench.

Scope of work

The workbench stands on a sovereign foundation: an AI environment on your existing or a Swiss infrastructure, designed for nFADP compliance and configured according to internal policies. On this foundation, four kinds of application are carried.

flowchart TD
    accTitle: Structure of the Enterprise RAG workbench
    accDescr: On a sovereign foundation the Enterprise RAG workbench carries four application kinds: finding knowledge, automating work, evaluating research, and code and deployment agents.
    G["Sovereign foundation<br/>own infrastructure, under own control"] --> W["Enterprise RAG workbench"]
    W --> A["Finding knowledge<br/>RAG on your own data"]
    W --> B["Automating work<br/>documents, triage"]
    W --> C["Evaluating research<br/>semantic search"]
    W --> D["Code and deployment agents<br/>your own development"]

The entry point proves a first use case; the same workbench carries several over time:

Finding knowledge (knowledge AI / RAG) An internal AI answers from your own documents, with source citations and without data leakage, for example across contracts in a protected space, files under professional confidentiality, or internal regulations.

Automating work Recurring workflows are connected to AI, from document and receipt extraction through enriching product data to triage, adapted to your existing processes rather than to an external standard product.

Evaluating research Scattered sources are searched, reconciled and substantiated, for example for semantic search across a candidate or property portfolio, or for market and subject research on your own data.

Code and deployment agents Agents support your own development and operations, from code creation to controlled deployment, with the same approach that le dot uses in its own platform.

How the A-Team builds the Enterprise RAG workbench

A controlled Enterprise RAG workbench emerges from the interplay of several factors. The A-Team brings them together, from data through models and building blocks to governance, operations and a fixed entry point.

flowchart LR
    accTitle: A-Team cause-and-effect diagram for a controlled Enterprise RAG workbench
    accDescr: An Ishikawa diagram. On the right is the result, a controlled and sovereign Enterprise RAG workbench. Six factor groups act on it, which the A-Team brings together, data, models, building blocks, governance, operations and the entry point via a fixed first use case.
    DATEN["Data<br/><small>Own sources, classification and approvals</small>"] --> WB
    MODELLE["Models<br/><small>Model gateway, local and external models</small>"] --> WB
    BAUSTEINE["Building blocks<br/><small>Sign-in, knowledge, agents; proven and replaceable</small>"] --> WB
    GOV["Governance<br/><small>Traceability, audit, EU AI Act</small>"] --> WB
    BETRIEB["Operations<br/><small>Own infrastructure, no data leakage, observability</small>"] --> WB
    EINSTIEG["Entry point<br/><small>Fixed first use case, working pilot</small>"] --> WB
    WB(["Controlled, sovereign<br/>Enterprise RAG workbench"])

Scope boundaries

The first use case is a time-boxed, fixed project, not a finished production system. The broad build-out, the connection of further sources and applications and continuous operation follow separately; Service Management then takes over productive continuous operation. The workbench uses the same sovereign approach that le dot operates in the A-Team for its own AI platform, instead of handing data to an external cloud service.

Key data

The entry point is a scoped first project at a fixed price. What it covers in a concrete case depends on the chosen use case and its depth:

  • the kind and number of connected data sources or systems
  • the complexity of the workflows
  • how strict the requirement for own or Swiss infrastructure is

A clearly defined first case fits into the package, a broad scope across several applications calls for more. What the workbench costs in a concrete case depends on exactly these factors. The price range gives the frame for your own project.

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Further information