Building foundation models for
AI sovereignty.
Data sovereignty is national sovereignty.
Magibu turns Turkish foundation and embedding models into secure, measurable AI systems that run inside your organization. We invite every organization that values its data to join this journey.
Fewer tokens mean lower cost and lower energy.
When Turkish text is represented closer to the structure of the language, the same meaning can travel through fewer pieces. That chain connects directly to inference cost, energy use, and secure systems that can run inside the organization.
Fewer tokens
A tokenizer approach that treats Turkish suffixes and meaning units more carefully.
Lower cost
Less processing for the same text and more predictable API/infrastructure cost.
Lower energy
Shorter context and less compute create a measurable energy advantage.
Data stays inside
Efficient models make on-prem and private cloud deployments more practical.
Turkish tokenizer comparison
If the demo does not load, open the comparison on Hugging Face.
Where do you want to start?
We run Magibu across three arms: measurable products for institutions, an academy that teaches the work, and the open-source community that produces it.
Try what we have shipped.
Test our models, tokenizers, and embeddings in live demos. Every link points to an open-source release or a published product.
Magibu Q3
Our foundation model for Turkish text generation and understanding. Try it in the live chat interface.
Start chattingTR-MMLU
Open MMLU list with 6200+ questions across 57 domains. Compare Turkish models side by side.
Open full listTurkish Tiktokenizer
Morphological tokenizer built for Turkish. Compare suffix handling and token efficiency live.
Open demoTR-MTEB Scoreboard
Open embedding leaderboard across 26 datasets and 6 tasks. Compare models side by side.
ScoreboardRetrieval Platform three layers.
A full stack from embedding to AI system to in-house deployment. Take one layer, or run all three together.
Magibu
Embed API
An OpenAI-compatible, high-performance embedding API optimized for Turkish and underrepresented languages. Superior semantic representation with long-context support.
Magibu
Search Kit
Production-ready retrieval infrastructure for AI applications. Automated document ingestion, semantic chunking, vector database connectors, and query evaluation tools.
Magibu
Private AI
An isolated AI architecture running fully on-premises or within your private cloud. Domain-adapted search, source-grounded answers, SSO/LDAP integration, and security audit logs.
Magibu
Q3 Foundation
Our foundation model optimized for Turkish text generation and comprehension. Integrated with enterprise security standards during pilot deployments.
Measure first, then deploy.
Magibu Retrieval Audit is a 2-week measurement package before pilot. We compare models and architectures on your data together.
"Let's measure which model works better on your documents; then deploy a secure in-house AI system."
Not a sales pitch - a way of working. You can't be sure of progress in a system you haven't measured; we set direction with metrics like recall, precision, and MRR, not guesswork. Before buying a large system, we measure on your own data. Most organizations skip this step and come back months later. We start here.
Data Sampling and Analysis
We select a representative subset of your documents together. Data stays inside your environment.
User Test Scenarios
30–100 real user questions with expert-labeled correct passages.
Model & Architecture Benchmark
Magibu, OpenAI, Cohere, Voyage, and E5 measured on the same data. Chunking strategies compared side by side.
Comprehensive Metrics Report
recall@5, precision@10, MRR, nDCG@10, and latency. Top 5 wins + 5 critical failures with case studies.
Topology Recommendation
One-page technical and financial rationale for model, chunking, reranker, vector DB, and deployment topology.
Roadmap & Decision
Continue or stop for pilot. Audit delivers value on its own; not required before pilot.
Research output turned into product evidence.
Our technology is built on open benchmarks, doctoral research, and peer-reviewed or openly published work. Instead of naming committee members on the homepage, we surface verifiable publications and model outputs.
Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation
arXiv:2501.00593
Open paper ↗Tokenization Standards for Linguistic Integrity: Turkish as a Benchmark
arXiv:2502.07057
Open paper ↗Adapting Multilingual Embedding Models to Turkish via Cross-Lingual Tokenizer Surgery and Offline Distillation
arXiv:2605.29992
Open paper ↗Three arms, one evidence culture.
Company ships systems inside organizations; Academy teaches the work; Community grows open measurement and open science.
Products
behind your firewall.
We build AI systems for security-sensitive organizations, starting with measurement and moving toward private cloud or on-prem deployment.
- 01Retrieval AuditModel and architecture comparison on your own data
- 02Retrieval PlatformEmbed API · Search Kit · Private AI
- 03Private DeploymentOn-prem / private cloud · SSO · audit logs
- 04Enterprise TrainingArchitecture design, data security, and model evaluation
Knowledge
turned into practice.
We turn LLM, embedding, RAG, and evaluation expertise into production-oriented training where participants build instead of only watching.
- 01Builder-first programsLearning through GitHub and Hugging Face outputs
- 02Enterprise trainingAI architecture for technical teams and executives
- 03Certificate disciplineVerifiable achievement and portfolio, not attendance only
- 04Open-source connectionTraining outputs connect back to community projects
Open measurement
and open science.
Our community branch grows open model, tokenizer, dataset, and benchmark work for Turkish and low-resource languages.
- 01Transparent DevelopmentGitHub Issues + PRs · open contribution flow
- 02Open BenchmarkTR-MTEB · TR-MMLU · domain-specific eval kits
- 03Models & DataOpen model and dataset work on Hugging Face
- 04Community EventsMeetups, hackathons, webinars, and the weekly digest
Built with the community.
Open projects advancing Turkish language technology. Open issues on GitHub, send a PR, or apply to join the team.
Turkish Morphological Tokenizer
ActiveA modern tokenizer that splits text into morphological units faithful to Turkish phonetics and can recombine them.
Language-Native Embeddings
ActiveAn open methodology compiling methods and steps for anyone to build efficient tokenizers and embedding models for their own language and domain.
Building together.
Organizations and communities we collaborate with. Click a logo to visit their site.
Latest announcements
applications and partnerships.
Apply for a Retrieval Audit, API access, investment, or research collaboration. Our team will respond within the shortest possible time.
"Data sovereignty is national sovereignty."
- → dev.magibu.ai · Embedding API
- → TR-MTEB · 26 datasets
- → On-prem / Private AI