Measurable Turkish AI infrastructure
for your organization.
We build models, embeddings, and evaluation layers for Turkish and underrepresented languages. Our open benchmarks inform RAG, semantic search, and enterprise assistants that run without sending your data outside your environment.
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 demoembeddingmagibu-200m
Turkish-first embedding model · TR-MTEB #1. Live semantic similarity demo.
Open demoTR-MTEB Scoreboard
Open embedding leaderboard across 26 datasets and 6 tasks. Compare models side by side.
ScoreboardFive structural barriers in Turkish.
What makes reliable AI on enterprise Turkish documents hard today - and how Magibu addresses each barrier with measurable infrastructure.
Keyword matching is insufficient for Turkish search.
Morphological complexity, suffix structures, synonyms, and long context windows leave traditional keyword search inadequate for real-world user queries.
General-purpose models fail to capture domain terminology.
Generic embedding models used in legal, healthcare, or corporate operations fail to understand specialized terminology, leading to superficial answers.
AI systems are deployed without performance measurement.
Decisions are often based on cherry-picked demos that 'look like they work'. Implementations are deployed without evaluating recall, precision, or MRR.
Data privacy and regulatory compliance (KVKK/GDPR) are non-negotiable.
Critical corporate data is frequently sent to public APIs without control. Data residency, local deployment options, and detailed audit trails are missing.
Answers without references fail to build corporate trust.
Unless LLM outputs are directly mapped to specific source documents and paragraphs, they cannot be safely used in high-stakes decision-making.
Retrieval 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. Most organizations skip measurement and come back months later. We start with this step.
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.
Dual structure.
Magibu Community grows open measurement and open science; Magibu Enterprise turns that knowledge into measurable, secure products inside organizations.
Open
science layer.
Our open-science community branch that fosters industry-academia collaborations and contributes open source value. Our development process is fully transparent.
- 01Transparent DevelopmentGitHub Issues + Kanban · Open PRs for all
- 02Community EventsMeetups, hackathons, webinars · 400+ video experience
- 03Data & Training CodeWikipedia-40, legal/medical dialogue · pre-train & fine-tune scripts
- 04Open Benchmark & EvalTR-MTEB · TR-MMLU · domain-specific eval kits
Product
that ships.
Our commercial enterprise arm that turns AI research into products, offering on-premise and private cloud solutions for security-sensitive organizations.
- 01Investor PartnershipFounding partners with financial strength · stakeholder model
- 02Retrieval PlatformEmbed API · Search Kit · Private AI
- 03Private AI Pilot4-week on-prem deployment · AI system + audit
- 04Training & ConsultingArchitecture design, data security, model optimization
Building together.
Organizations and communities we collaborate with. Click a logo to visit their site.
applications and partnerships.
Apply for a pilot, API access, investment, or research collaboration. Our team will respond within the shortest possible time.
"Let's measure which model works better on your documents; then deploy a secure in-house AI system."
- → magibu.dev · Embedding API
- → TR-MTEB · 26 datasets
- → On-prem / Private AI