What kind of AI solutions does Rakri AI build?

We build four types of AI solutions: Custom AI Systems (end-to-end), Self-Hosted Private Models (fine-tuned LLMs on your infrastructure), AI SaaS Applications (full product development), and AI Workflow Automation (automating repetitive business processes).

Do I own the code and models Rakri AI builds?

Yes, 100%. Everything we build — code, models, data pipelines — is fully owned by you. No vendor lock-in, no recurring license fees for what we build.

How long does a typical AI project take?

Most projects are delivered within 3-6 weeks, depending on complexity. We scope honestly upfront so there are no surprises.

Is my data kept private?

Absolutely. We specialize in self-hosted and private AI deployments. Your data never leaves your infrastructure unless you choose otherwise.

Case Studies / Mijn AI Omgeving

Suri Autonomous Content Engine — Publishing Without Human Review

Client

Mijn AI Omgeving, Netherlands

Context

Publishing content at scale with AI is easy. Publishing it without a human checking every piece before it goes live is a different problem — one most companies avoid because the risk of a hallucinated piece reaching real users is too high.

The problem

Mijn AI Omgeving wanted genuine autonomy: a system trustworthy enough to run unattended, daily, and still meet a real quality bar — not a system that merely generates content for a human to approve.

Approach

We engineered a 10-step autonomous agentic pipeline running as a daily CRON job, built around the assumption that any single generation could go wrong — and designed to catch it before it does.

DeepSeek V3 for generation, with Azure AI Search grounding content in real, current information before writing.
Retrieval-augmented generation against verified sources, followed by a dedicated factual verification stage.
A 6-layer hallucination defense gate purpose-built to handle the non-determinism inherent in LLM outputs at production scale.
An automated 0-100 quality score on every generated piece, giving the system a consistent, measurable publish bar instead of a subjective gut check.

The entire pipeline runs at approximately $0.006 per article — a cost structure that makes genuinely autonomous, high-volume publishing economically viable in a way manual or human-reviewed AI content isn't.

Architecture

Daily CRON trigger→Web grounding→RAG retrieval (Azure AI Search)→DeepSeek V3 generation

Factual verification→6-layer hallucination gate→0-100 quality score→Publish

Outcome

$0.006

Operating cost per article

6-layer

Hallucination defense gate

Human review steps, by design

Runs fully autonomously at up to 50 articles/day, with no human review step by design.

Stack

DeepSeek V3Azure AI SearchRAG retrievalAutomated factual verificationCRON-based scheduling

Metrics reported by client and Rakri AI engineering logs as of the current engagement period.

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Approach

We engineered a 10-step autonomous agentic pipeline running as a daily CRON job, built around the assumption that any single generation could go wrong — and designed to catch it before it does.

DeepSeek V3 for generation, with Azure AI Search grounding content in real, current information before writing.

Retrieval-augmented generation against verified sources, followed by a dedicated factual verification stage.

A 6-layer hallucination defense gate purpose-built to handle the non-determinism inherent in LLM outputs at production scale.

An automated 0-100 quality score on every generated piece, giving the system a consistent, measurable publish bar instead of a subjective gut check.