A multi-agent enterprise search case with lower cost, faster responses, higher satisfaction and an important evidence caveat.
The reported outcome
Siemens operates in more than 130 countries and employs more than 300,000 people. Its global website search draws from 15 to 20 sites, business units and ecommerce portals. Users often needed technical documents but had to move through marketing pages and complex product structures.
Siemens built a natural-language, multi-agent search system and switched from an unnamed earlier AI model to Amazon Nova 2 Lite. According to the AWS customer case, the production system achieved:
Reported Production Results
70% lower than previous models
Model cost
Reported in the cited source.
300% improvement
Search speed
Reported in the cited source.
11% increase
User satisfaction
Reported in the cited source.
| Outcome | Reported result |
|---|---|
| Model cost | 70% lower than previous models |
| Search speed | 300% improvement |
| User satisfaction | 11% increase |
| No-result searches | Eliminated |
| Time from concept to deployment | Less than one year |
The combination matters. A model change that only lowers cost can damage the product. Siemens reported improved economics, speed and user experience together.
Search was decomposed into agents
The system does not send a query to one general-purpose model and hope for a useful answer. It separates the workflow:
- A validation agent checks whether the query is valid and appropriate.
- A classification agent identifies intent and the relevant knowledge source.
- A targeted search backend retrieves evidence.
- A summarization agent condenses the results.
- Guardrail agents filter disallowed topics, non-Siemens products and legally sensitive requests.
AWS Lambda orchestrates the process, while Amazon Bedrock provides access to models. Siemens described choosing the best-fitting model for each agent. The architecture can optimize a classifier for classification, a summarizer for concise synthesis and guardrails for policy enforcement instead of making every stage pay for identical capability.
That is a practical example of enterprise AI model routing.
Why Nova 2 Lite was enough
Enterprise search is often framed as a reasoning contest. Much of the real workload is narrower: validate a request, classify an intent, search a bounded corpus and summarize retrieved documents. The quality comes from workflow structure and source data as well as model capability.
The model still needs multilingual support, instruction following and reliable summarization. It does not need to carry every answer in its parameters because the search layer supplies the relevant Siemens information.
This is where a well-designed enterprise RAG system changes the model requirement. Strong retrieval, routing and guardrails allow a faster model to answer a bounded question with evidence.
The evidence caveat
The AWS case says Siemens first used another model and then switched to Nova 2 Lite. It does not identify the earlier model, pricing basis, request volume, quality evaluation or infrastructure cost. The 70 percent result therefore supports model-fit optimization, but it does not prove that Siemens replaced GPT-4 or another frontier model.
It is also a vendor-supported customer story. The figures should be treated as reported company outcomes and reproduced through a local pilot before they enter an enterprise budget.
This distinction is why the Frontier Model Downshift Index separates direct migrations, comparative architectures and model-portfolio cases.
A replication framework
An enterprise search team can test the Siemens pattern with a task-level scorecard:
| Stage | Quality gate | Cost metric |
|---|---|---|
| Validation | unsafe and irrelevant query recall | cost per screened query |
| Classification | routing accuracy by intent | cost per correctly routed query |
| Retrieval | recall at k, citation coverage | cost per useful evidence set |
| Summarization | factual support and completeness | cost per accepted answer |
| Guardrails | severe policy miss rate | cost per safely handled query |
Run the incumbent and candidate models against identical traffic in shadow mode. Evaluate languages, business units, long technical queries and no-answer cases separately. Then use the smallest model that clears the relevant gate at each stage.
Related production evidence includes Dropbox's relevance judge, Pinterest's internal retrieval models, Replit's router and the AI cost-reduction playbook.
Building a Production-Ready System
Conscious Engines builds enterprise search and RAG systems as measured workflows. We map the corpus, create query and relevance evaluations, select or adapt the model for each stage, add guardrails, and deploy the stack within the enterprise's security boundary.
The aim is the Siemens pattern: better answers and faster search at a lower cost, with an architecture that can change models without rebuilding the whole product.