# From Data Silos to Knowledge Sources

Every day, companies create large volumes of data from invoices, emails,
CRM systems, time tracking, and support tickets. Most of that data stays
spread across disconnected tools, which turns valuable knowledge into
isolated fragments.

## Data lake and data mesh as the foundation

To unlock this potential, organizations need a clear data architecture.

- A data lake centralizes structured and unstructured data in raw form.
- A data mesh assigns domain ownership, so business teams manage their own
  data products with clear responsibility and quality.

Combined, both models provide reliable and current data for AI workloads.

## LLMs and SLMs: broad capability plus precision

LLMs are strong general-purpose language models that support writing,
summarization, contextual email handling, and code or report generation.

SLMs are specialized models trained for specific domains where accuracy is
critical, such as technical diagnostics, internal knowledge systems, or
clinical documentation.

Using both together gives companies flexibility and precision at the same
time.

## Agentic AI: from reading information to executing work

AI agents use LLMs and SLMs to retrieve company data and complete tasks
autonomously.

- Invoice matching against ERP purchase orders and payment terms
- Automated customer support responses grounded in CRM and wiki data
- Project controlling with early risk signals from budget and milestone data
- Onboarding support tailored to each role across HR and internal tools

This turns AI from a passive assistant into an operational teammate.

## Local hosting or external hosting

Local hosting offers stronger data sovereignty, compliance control, model
customization, and often lower latency for internal workflows.

External cloud hosting is faster to start and easier to scale, but can add
privacy concerns, lock-in risk, and less control over adaptation.

For sensitive use cases, local deployment is often the more sustainable
strategy.

## Conclusion

Organizations that combine local AI models, robust data architecture, and
agent-based automation gain faster decisions, better efficiency, and a real
innovation advantage.

If you want to consolidate data sources and deploy practical AI agents,
jaraco gmbh can support your team from architecture to implementation.