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 / JG Flex Recruitment

AI-Powered Candidate Matching — Intelligent Recruitment for a Multilingual SaaS Platform

Client

JG Flex Recruitment, Netherlands

Context

Recruitment is fundamentally a matching problem — the right candidate for the right role — but it's usually solved with manual, keyword-based resume scanning that misses context, nuance, and genuine fit.

The problem

JG Flex needed a platform that could handle multilingual candidate pools and match people to roles with real semantic understanding, while still giving recruiters a transparent, explainable reason to trust each match.

Approach

We designed and built the platform end-to-end — frontend, backend, AI matching layer, and infrastructure — as one system.

Next.js 16 frontend, .NET 9 backend, deployed on Azure.
Azure OpenAI embeddings combined with Azure AI Search's hybrid vector search represent candidates and job requirements semantically, capturing meaning rather than keywords.
A hybrid scoring model combines semantic matching with rule-based criteria, with the weighting between the two dynamically adjusted based on job category.
142 automated tests and a 50+ endpoint Postman collection back the platform, with full Azure infrastructure provisioned through Bicep IaC and GitHub Actions CI/CD.

What makes a strong match for a technical role and what makes one for a customer-facing role aren't the same thing — a single fixed formula would have under-served one or the other.

Architecture

Candidate + job text→Azure OpenAI embeddings→Azure AI Search hybrid vector search

Semantic score→Rule-based criteria→Category-weighted hybrid score+ hard filters →Ranked shortlist

Outcome

~200%

Increase in shortlisting speed

~50%

Reduction in manual screening effort

142

Automated tests across a 50+ endpoint API

Fully deployed end-to-end AI SaaS product — architecture, backend, frontend, AI layer, and infrastructure, delivered as one system.

Stack

Next.js 16.NET 9Azure OpenAI embeddingsAzure AI Search (hybrid vector search)Bicep IaCGitHub Actions CI/CD

Metrics reported by client and Rakri AI engineering logs as of project delivery.

Have a similar ai candidate matching problem?

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Response within 24 hours. No commitment required.

Approach

We designed and built the platform end-to-end — frontend, backend, AI matching layer, and infrastructure — as one system.

Next.js 16 frontend, .NET 9 backend, deployed on Azure.

Azure OpenAI embeddings combined with Azure AI Search's hybrid vector search represent candidates and job requirements semantically, capturing meaning rather than keywords.

A hybrid scoring model combines semantic matching with rule-based criteria, with the weighting between the two dynamically adjusted based on job category.

142 automated tests and a 50+ endpoint Postman collection back the platform, with full Azure infrastructure provisioned through Bicep IaC and GitHub Actions CI/CD.

What makes a strong match for a technical role and what makes one for a customer-facing role aren't the same thing — a single fixed formula would have under-served one or the other.