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 Support Copilot — A RAG-Based AI Chatbot Built on the Suri Ecosystem

Client

Mijn AI Omgeving, Netherlands

Context

Built on top of the Suri ecosystem, this chatbot represents Mijn AI Omgeving to its own users across several SaaS product domains simultaneously — as shared, reusable infrastructure, not a single-tenant demo.

The problem

A chatbot that answers confidently but incorrectly is worse than no chatbot at all, especially when it needs to hold real multi-turn conversations while staying grounded in retrieved information rather than the model's own assumptions.

Approach

We built a Retrieval-Augmented Generation chatbot combining orchestration, retrieval, and full observability into one production system.

FastAPI, LangChain, and LangGraph handle orchestration, with Azure AI Search handling retrieval.
Conversations stream to the user over WebSocket connections rather than waiting for a full response to generate.
Multi-turn memory via LangGraph's MemorySaver means the assistant remembers conversation context instead of treating every message as isolated.
Full LangSmith tracing on every single agent turn gives the team real visibility into what the model retrieved, reasoned over, and responded with at each step.

Outcome

Multi-tenant SaaS domains live in production

100%

Agent turns traced via LangSmith — not sampled

That's the difference between a chatbot you hope is working and one you can actually debug and improve over time.

Stack

FastAPILangChainLangGraphAzure AI SearchWebSocket streamingLangSmith tracing

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

Have a similar rag chatbot problem?

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Response within 24 hours. No commitment required.

Approach

We built a Retrieval-Augmented Generation chatbot combining orchestration, retrieval, and full observability into one production system.

FastAPI, LangChain, and LangGraph handle orchestration, with Azure AI Search handling retrieval.

Conversations stream to the user over WebSocket connections rather than waiting for a full response to generate.

Multi-turn memory via LangGraph's MemorySaver means the assistant remembers conversation context instead of treating every message as isolated.

Full LangSmith tracing on every single agent turn gives the team real visibility into what the model retrieved, reasoned over, and responded with at each step.