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aprivas
04Services

AI & Intelligent Systems

Sovereign AI, RAG, fine-tuning, multi-agent architectures.

We integrate AI so that it becomes productive in real operational environments: auditable, controlled, with a governance layer.

Typische Leistungen

Was wir in diesem Feld konkret machen

  • 01Sovereign AI platforms (local, auditable, controllable)
  • 02Retrieval-augmented generation on company data
  • 03Multi-agent architectures with governance layer
  • 04Model adaptation through fine-tuning on your own data
  • 05Evaluation-driven development
  • 06Risk-graded execution (read / guarded write / critical)
Fields of work

From foundation to governance

The connecting principle is data sovereignty: processing happens in an environment you control. A typical starting point is the platform or knowledge management; the other fields follow once the foundation holds.

  • 01

    Local AI platforms

    We build language-model platforms on your own hardware: hardened baseline, clear operating procedures, no cloud dependency. We support day-to-day operation. The platform integrates into existing enterprise environments instead of existing alongside them.

  • 02

    RAG & knowledge management

    We make company knowledge findable: structured storage, search that works by meaning rather than keywords alone, and answers with source references (retrieval-augmented generation).

  • 03

    Model adaptation & fine-tuning

    When standard models are not enough, for domain language, internal document classes or fixed answer formats, we adapt them: fine-tuning on your own data, with measurable quality criteria. Model changes are documented and reversible.

  • 04

    Multi-agent systems with governance

    Agents that handle tasks autonomously need firm rules: defined approval steps, permissions graded by risk, and traceable logging. That keeps autonomy controllable — including in front of an audit.

  • 05

    Speech & media processing

    Audio transcription with speaker recognition and AI-supported media formats, processed locally. Confidential recordings can remain entirely within your environment.

  • 06

    Privacy & compliance for AI

    AI adoption and data protection are not at odds when the architecture is right. We plan data flows, deletion concepts and traceability from the start and document where which data is processed.

Nutzen

AI that becomes productive rather than stopping at the demo.

Engine room

The pattern we build multi-agent systems on

A deliberately simplified view of the architecture pattern we use to design agent systems and run them in production ourselves. The details of our systems stay internal.

run on-premises
100 %run on-premises
decision logged
Everydecision logged
in the loop
Humanin the loop

A simplified pattern without system details. We design and operate systems exactly like this for you, on your infrastructure.

From practice

Confidential recordings, processed into minutes locally

Meetings, interviews, dictation: our processing pipeline turns them into searchable minutes with speaker attribution, entirely within your environment, no cloud service.

output/meeting_0812.rttm · .json
SPEAKER meeting_0812 1   0.960  11.741 <NA> <NA> SPEAKER_01 <NA> <NA>
SPEAKER meeting_0812 1  12.480   4.120 <NA> <NA> SPEAKER_02 <NA> <NA>
SPEAKER meeting_0812 1  16.870   7.305 <NA> <NA> SPEAKER_01 <NA> <NA>
{"start": 12.48, "end": 16.60, "speaker": "SPEAKER_02", "text": "Let’s take that item into the minutes.", "avg_conf": 0.94}

Output formats from our own processing pipeline: the formats are real (open standards), content and values invented. Confidential recordings never leave your environment.

From practice

An agent fleet that monitors itself

Excerpt from the monitoring of our own agent system: base services with live health status. The excerpt is deliberately small: the full fleet and its tasks stay internal.

Excerpt from a monitoring interface: five agent tiles (Dialogue Manager, Guard Central, Session Manager, WebGUI Session, User Profiler), each with a green healthy statusOur own system
Real screenshot from our operations. Every agent reports its state, and disruptions surface before they cost work.

From practice

Agents that keep learning

The self-study scheduler of our own agent system: agents learn new tools in controlled runs, with cooldown, limited parallelism and approval before use.

Monitoring strip of a self-study scheduler: sliders for cooldown and parallelism, plus KPI tiles such as learned tools, agents with tools and recent sessionsOur own system
Real screenshot from our operations. Learning is a regulated process here: what an agent newly masters is counted, reviewed and only then put to use.
Contact

Have a concrete project in mind?

Tell us briefly the starting point, goal and constraints. In a first call we will then clarify whether we are a fit.

Your enquiry goes straight to managing director Markus Hentrich, not into a ticket system.

Request a conversationRequest specialist support