RAG. Dataset Generation. Fine-Tuning.
All running on your hardware. No cloud. No subscriptions. No compromises.
// zero telemetry · air-gap compatible · you own every byte
MokingBird Node sits at the center, connecting RAG, DataGen, and Fine-Tuning into one seamless local workflow.
MokingBird Node acts as the central hub, connecting all specialized tools into one coherent local workflow. Node is the wrapper layer; RAG, DataGen, and FT are the core product modules.
▸ MokingBird Node (Hub Layer)
Orchestration, scheduling, model management, and coordinated pipeline execution.
🔍 RAG · ⚡ DataGen · 🧬 FT
Modular tools. Download full Node or install each tool separately based on your setup.
Click a product below to explore its capabilities.
The unified hub that orchestrates your entire local AI workflow. One interface to manage models, pipelines, and all MokingBird tools — all on your hardware.
A comprehensive RAG framework with 6-level contextual retrieval. From basic keyword search up to graph-augmented retrieval — all local, all private.
The crown jewel of MokingBird. GPRO-Hybrid Reinforcement Learning drives quality-controlled dataset synthesis — generating training data that actually makes your models better.
Universal fine-tuning platform supporting 7+ training frameworks. LoRA, QLoRA, DPO, ORPO and more — your fine-tuned models are yours alone, stored locally.
Every tool is production-grade, privacy-first, and designed to run on your own hardware indefinitely.
The all-in-one control plane for your local AI stack. Manage models, schedule pipelines, monitor resources, and orchestrate RAG, DataGen, and FT — from one unified interface.
Comprehensive RAG with 6-level contextual retrieval hierarchy. Outperforms standard cloud RAG by 30–50% — no SaaS subscription needed, no data leaving your network.
The jewel of the suite. Generate high-quality AI training datasets locally using GPRO-Hybrid Reinforcement Learning. Quality-controlled, diverse, and at any scale — on your hardware.
Universal fine-tuning platform with 7+ frameworks. From LoRA adapters to full fine-tuning to RLHF — train the model you need, own the weights, export anywhere.
Designed for anyone who takes AI privacy seriously.
Generate custom datasets, fine-tune open models, and run reproducible RAG experiments — all offline, all verifiable.
Build RAG systems over sensitive internal docs. No cloud. No data leaks. No compliance headaches. Air-gap ready.
Use MokingBird's Python API to power local AI features in your products. Keep your users' data off the cloud by design.
Deploy in air-gapped environments. MokingBird never phones home. Zero-telemetry local stack you control end-to-end.
Generate domain datasets with DataGen, then fine-tune with FT across 7+ frameworks. Evaluate, iterate, and export — all locally.
Run large-scale AI experiments on institutional hardware. No per-token costs, no data agreements with cloud providers.
No telemetry. No data leaving your machine. No subscriptions. No vendor lock-in. Your AI, your rules.
All core workflows run on your own hardware. Air-gap compatible by design.
No hidden call-home behavior. You control data movement and integration boundaries.
You own model artifacts, datasets, and operational lifecycle from training to deployment.
Downloads are being finalized. Join the notify list and we will email you as soon as MokingBird AI launches.
⬇ Download Full Node for FreeMokingBird AI is the desktop AI ecosystem developed by MokingBird — a suite of three powerful, production-grade tools for working with large language models, entirely on your own hardware. No cloud accounts. No data leaving your machine. No subscriptions required to get started.
The suite is organized around the MokingBird Node — a desktop hub application that brings mbRAG, mbDataGen, and mbFT together in one place. Use them as an integrated system through the Node, or run each tool independently as a standalone application.
Domain: ai.mokingbird.xyz
Company: MokingBird Oy, Business ID: 3615646-1, Finland
Your models. Your hardware. Your data. Your rules.
Local-first AI infrastructure for builders, researchers, and teams who need professional capability without cloud lock-in or data exposure.
MokingBird AI is an umbrella for our local AI infrastructure products. The core architecture is:
MokingBird Node (Desktop Hub)
├── mbRAG — Retrieval-Augmented Generation framework
├── mbDataGen — Synthetic dataset generation platform
└── mbFT — Universal fine-tuning platformEach tool can be installed and used standalone, or together through the Node. The Node is the hub — a PySide6 desktop application (Python) that surfaces all three tools in a unified interface and exposes local FastAPI endpoints for programmatic access.
Platforms: Windows, macOS, Linux
Architecture: 100% local execution — all AI operations run on your machine
Internet: Optional (for connecting to cloud LLM providers using your own API keys)
mbRAG is a production-ready, stable alternative to LangChain. It implements every major RAG approach in a single unified system — without the breaking changes, vendor lock-in, or opacity that plagues other frameworks.
At a glance:
mbDataGen solves one of the most persistent problems in applied ML: getting high-quality, domain-specific training data. Instead of scraping, labeling manually, or accepting noisy public datasets, mbDataGen generates clean, validated synthetic data from your own documents.
At a glance:
Total Reward = 0.7 × Field/Process Reward + 0.3 × Outcome/Overall RewardmbFT makes fine-tuning accessible without hiding what's actually happening. The Smart Config Engine handles the complexity of choosing hyperparameters and memory-efficient configurations, while keeping you in full control.
At a glance:
The cloud AI model has a hidden cost: your data. When you send documents to an API endpoint to answer questions, generate datasets, or fine-tune a model, those documents leave your control. Even with privacy-protecting terms of service, the fundamental architecture means your data travels.
MokingBird AI is built on a different premise: the model comes to your data, not the other way around.
This isn't just a privacy positioning. It's an architecture choice that also means lower latency, no per-query costs for local models, and no dependency on service uptime. Critical workflows don't become unusable when an external API is down.
We believe powerful AI tools should not require:
MokingBird AI is our contribution toward democratizing serious AI infrastructure — the kind of RAG, data generation, and fine-tuning capability that has historically been available only to well-funded research teams or large enterprises.
MokingBird AI is developed by MokingBird Oy — The Everything Lab. We also build MB Viewer, Sortify, Jogg, and Jogg Mini.
Learn more about MokingBird at mokingbird.xyz/about.
MokingBird Oy | Business ID: 3615646-1 | Finland
Last updated: April 2026
This policy covers all MokingBird AI products and websites, including:
MokingBird AI is a suite of desktop applications developed by MokingBird Oy. This Privacy Policy describes how our AI products handle your data. It applies to:
All of the above are desktop applications that run locally on your device. They were designed from the ground up with a core architectural commitment: your data stays on your machine.
This is not a privacy policy that describes what we do with data we've collected about you. It is a policy that explains why we collect essentially none, and what limited data interactions do exist.
Contact us for privacy inquiries at [email protected].
MokingBird AI products are built on the principle that the model comes to your data, not the other way around.
When you use mbRAG to query your documents, those documents are processed on your hardware. When you use mbDataGen to generate training data from your corpus, that corpus stays on your machine. When you use mbFT to fine-tune a model, the resulting model is yours — we have no visibility into it.
This is not a product feature. It is the architecture. There are no MokingBird servers receiving your documents, queries, datasets, or model outputs during normal application operation.
During normal operation of any MokingBird AI application: none.
Specifically, the following are never transmitted to MokingBird servers:
There is no background analytics process. There is no telemetry SDK. There is no crash reporter sending data to our servers. If you want to verify this, you can monitor the application's network activity — you will find nothing from the app itself during normal operation.
Many users connect MokingBird AI tools to external LLM providers (OpenAI, Anthropic Claude, Google Gemini) or embedding providers (Cohere, HuggingFace API). To do so, you provide API keys.
Your API keys:
MokingBird Oy has no access to your API keys.
When you feed documents into mbRAG, generate data with mbDataGen, or provide training data to mbFT:
The only exception is if you have configured a cloud LLM provider: in that case, queries sent to that provider (e.g., OpenAI API) are governed by that provider's own privacy policy. MokingBird has no control over, or access to, what you send to those providers.
MokingBird AI supports both local LLMs (via Ollama, llama.cpp, HuggingFace local, vLLM) and cloud LLMs (OpenAI, Anthropic, Google Gemini).
When using local LLMs:
All queries stay entirely on your device. No internet connection is required. Complete privacy end-to-end.
When using cloud LLMs:
Your queries are sent to the provider's API servers (e.g., api.openai.com). This is governed by OpenAI's, Anthropic's, or the relevant provider's privacy policy — not ours. MokingBird is a client that calls the API on your behalf using your key. We do not see, log, or store those queries.
We recommend reviewing the privacy policy of any cloud LLM provider you connect to MokingBird AI tools.
MokingBird AI applications may offer optional checks for software updates. When an update check occurs:
You can disable automatic update checks in the application Settings.
For local-first product operation, we do not require or intend to collect:
This is architectural, not just a policy statement — the app has no mechanism to transmit these data types in local mode.
Our applications contain no:
We do not know how you use the software. We do not track which features you use, how many documents you process, or what models you fine-tune. This is intentional.
The MokingBird AI website may use privacy-friendly, cookieless web analytics (such as Plausible or Cloudflare Web Analytics) to understand aggregate traffic patterns. If analytics are in use, they:
We will update this section if our analytics setup changes.
MokingBird Oy is based in Finland, within the EU. Where infrastructure services process data outside the EU/EEA, we apply required GDPR safeguards including standard contractual clauses or adequacy decisions where applicable.
As a user in the European Union (or anyone protected by GDPR-equivalent legislation), you have the following rights:
Since our desktop applications collect no personal data, most of these rights are satisfied by design. For any data held through website contact forms, newsletter subscriptions, or support tickets, contact [email protected]. We respond within 30 days.
| Data type | Retention |
|---|---|
| Website server logs | 30 days |
| Contact form submissions | 90 days after resolution |
| Newsletter subscriptions | Until you unsubscribe |
| Support ticket history | 1 year after resolution |
Application data (documents, models, configurations, datasets) is stored only on your device. We have no copies.
MokingBird AI products are professional developer and researcher tools. They are not intended for use by children under 13. We do not knowingly collect personal information from children under 13. Since the applications collect no personal data during operation, there is no such data at risk. If you believe a child has used these applications in a way that has generated personal data, contact [email protected].
We will update this policy when our data practices change. Material changes will be noted on the website and in the application's release notes. The "Last updated" date at the top of this document will always reflect the most recent revision.
MokingBird Oy
Business ID: 3615646-1
Finland
MokingBird Oy | Business ID: 3615646-1 | Finland
Last updated: April 2026
Note: Pricing and subscription tier details are subject to change. Current pricing is available at ai.mokingbird.xyz/pricing.
By downloading, installing, or using any MokingBird AI application — including the MokingBird Node, mbRAG, mbDataGen, and mbFT (collectively "the Software" or "the Products") — you agree to these Terms of Service ("Terms"). If you do not agree, do not download or use the Software.
These Terms constitute a legal agreement between you ("User") and MokingBird Oy, a company registered in Finland (Business ID: 3615646-1).
These Terms apply to all MokingBird AI products:
These Terms also apply to the MokingBird AI website (ai.mokingbird.xyz) and any associated APIs or services provided by MokingBird Oy in connection with the above products.
MokingBird AI products are available for free download and use. The Free Tier grants you a non-exclusive, non-transferable license to:
The Free Tier includes the core functionality of mbRAG, mbDataGen, and mbFT with certain limitations on advanced features, pipeline configurations, or compute-intensive operations.
Premium subscribers pay a monthly or annual subscription fee (pricing available at ai.mokingbird.xyz/pricing) and receive:
Premium subscriptions are billed in advance. You may cancel at any time; cancellation takes effect at the end of the current billing period.
Enterprise licenses are available for organizations requiring:
Enterprise pricing is available on request at [email protected].
Certain compute-intensive features within mbDataGen and mbFT may be offered on a usage basis — charged per job, per generated record, or per training run — in addition to or in place of subscription tiers. Usage-based pricing will be clearly indicated before you initiate a chargeable operation.
MokingBird Oy may offer one-time purchase options for specific feature sets or perpetual licenses. Details will be available at ai.mokingbird.xyz/pricing.
You may use MokingBird AI products to:
You may not:
Users are responsible for:
MokingBird Oy may update, add, modify, or retire features at any time to improve safety, reliability, or product quality. For subscription users, material reductions in functionality will be communicated in advance.
For Premium and Enterprise tiers, and for any usage-based charges:
To request a refund, contact [email protected].
Your data is yours. MokingBird Oy does not claim any rights to:
All output produced by the Software using your data belongs to you. See our Privacy Policy for details on how we handle data.
MokingBird Oy retains all intellectual property rights in the Software, including its source code, algorithms, interfaces, and branding.
MokingBird AI tools may be configured to connect to third-party LLM providers (OpenAI, Anthropic, Google, etc.), embedding services, or vector store APIs. Your use of those services is governed by their respective terms and privacy policies. MokingBird Oy is not responsible for the availability, accuracy, or data practices of third-party services.
The Software is provided "as is" and "as available" without warranties of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, or non-infringement.
MokingBird Oy does not warrant that:
AI outputs are probabilistic in nature. Always validate AI-generated content before using it in production systems or making decisions based on it.
To the maximum extent permitted by Finnish and EU law, MokingBird Oy shall not be liable for:
Your license terminates immediately upon breach of these Terms. MokingBird Oy may suspend or terminate your access to subscription services with reasonable notice for non-payment or material breach.
To terminate a Premium subscription, cancel through your account settings or contact [email protected]. To uninstall the Software, delete the application files from your device.
These Terms are governed by the laws of Finland and applicable European Union law. Any disputes shall be subject to the jurisdiction of Finnish courts. EU consumer protection law applies where applicable.
We may update these Terms from time to time. Material changes will be communicated through the website and in application release notes. Continued use of the Software after changes constitutes acceptance of the updated Terms.
MokingBird Oy
Business ID: 3615646-1
Finland
MokingBird Oy | Business ID: 3615646-1 | Finland
Last updated: April 2026
MokingBird AI products are built on a local-first architecture — meaning the primary security model is architectural rather than perimeter-based. By running AI operations entirely on your device, we eliminate the largest attack surface present in cloud AI systems: a central server holding your data.
This document explains our security approach across all MokingBird AI products (Node, mbRAG, mbDataGen, mbFT), what data is protected, how, and how to report security issues.
The most significant security property of MokingBird AI is that we have no servers holding your data.
In cloud AI systems, your documents, queries, and model outputs are transmitted to and stored on provider servers. This creates:
With MokingBird AI, your documents stay on your machine. Your vector stores are local. Your model checkpoints are local. Your datasets are local. There is no MokingBird server that, if breached, would expose your data — because we don't have your data.
This is not a claim about perfect security. It is a statement about threat model: the primary threats are local (your device's security) rather than remote (a provider's server breach).
When you connect MokingBird AI tools to external LLM or embedding providers, you provide API keys. These keys:
Best practices for API key security:
Files you process through mbRAG, mbDataGen, or mbFT are processed in memory on your machine and, where persistence is needed, written to local disk.
Local file storage:
Recommendations:
MokingBird AI applications make network requests in the following controlled circumstances only:
| Request | When | Contains | Does not contain |
|---|---|---|---|
| Update check | Optional, user-initiated | App version, OS type | Any personal data, documents, keys |
| LLM API call | When you make a query using a cloud LLM | Your query (governed by provider's policy) | Document contents unless you include them |
| Embedding API call | When using a cloud embedding provider | Text chunks you submit | API keys (sent in headers per provider standard) |
All API calls to third-party providers use HTTPS. We enforce TLS for all outbound connections.
When using local LLMs (Ollama, llama.cpp, local HuggingFace models), zero network requests are made for inference. The application is entirely air-gapped from an LLM perspective.
MokingBird AI desktop applications are built using PySide6 (Python) and follow application security best practices:
Local API security:
The FastAPI endpoints exposed for programmatic access listen on localhost only. If you expose them to a network interface for integration purposes, you are responsible for securing that endpoint (authentication, firewall rules, VPN).
When downloading models (e.g., from Hugging Face) for use with mbFT or local inference:
.safetensors, .gguf) from unverified sources may contain malicious payloads — apply the same scrutiny you would to any executableMokingBird Oy is not responsible for security issues arising from third-party model files you download and use with our tools.
Because MokingBird AI runs locally on user-controlled hardware, users are responsible for:
Recommended practices for teams deploying MokingBird AI:
We take security vulnerabilities seriously and respond promptly.
To report a security vulnerability:
Please do not:
We are working toward a formal bug bounty program. Until that is live, we appreciate responsible disclosure and will acknowledge contributors publicly (with their permission) in release notes.
MokingBird Oy releases security patches as part of our regular update cycle. For critical security vulnerabilities, we release out-of-band patches as quickly as possible.
We strongly recommend:
Security advisories are published in our GitHub repository release notes.
Planned security maturity improvements include:
This is a first-version security overview. It will be refined with formal security review cycles.
MokingBird Oy
Business ID: 3615646-1
Finland
Note: Pricing structure is coming soon. Download free to get started today.
Exact pricing for Premium and Enterprise tiers will be announced at launch.
Last updated: April 2026
MokingBird AI is free to download and use. No credit card required.
€0 — Free to download and use
Get started immediately with the core features of the entire MokingBird AI suite.
Includes:
Best for: Individual researchers, developers, students, and teams evaluating the platform.
Pricing coming soon — per user/month (monthly or annual billing)
Everything in Free, plus:
mbRAG — Advanced:
mbDataGen — Advanced:
mbFT — Advanced:
Support:
Best for: Professionals, researchers, and teams with regular AI workflows.
Custom pricing — contact us
Everything in Premium, plus:
Contact [email protected] for a quote.
Best for: Organizations, enterprises, and research institutions with large-scale AI infrastructure needs.
Certain compute-intensive operations in mbDataGen and mbFT may be available on a usage basis — charged per job or per batch — for users who need occasional high-compute runs without a full Premium subscription. Pricing will be available at launch.
We are exploring a perpetual license option for users who prefer one-time payment over subscriptions. Details will be announced.
Depending on your needs, MokingBird AI may be available in the following configurations:
Is the free tier really free?
Yes. Free to download and use forever for core features. No credit card required. No time limit.
Do I need an internet connection?
No. All core features work offline. Internet is only needed for: optional update checks, and queries to cloud LLM providers (if you choose to use them).
Can I use the free tier for commercial work?
Yes. Free and Premium tiers both permit commercial use.
What happens if I cancel Premium?
Your account reverts to the Free tier at the end of your billing period. Your data, configurations, and locally generated files are unaffected.
Is there a student or academic discount?
We plan to offer academic discounts. Contact [email protected] with your institutional email for availability.
Browse all articles about MokingBird AI and the Node ecosystem.
April 2026 · MokingBird Team
There's a version of AI adoption that looks something like this: your team starts using an AI assistant to query internal documents. It works well. Then someone asks: where exactly are those documents going? The answer is "to a cloud API." That answer starts a conversation with legal, then compliance, then security. Three months later, the project is on hold.
This isn't a hypothetical. It's the pattern many serious AI deployments hit.
MokingBird AI was built to remove that friction — not by making privacy features a checkbox, but by making local-first execution the architecture.
Cloud AI APIs are genuinely impressive. They're easy to start with and require no hardware investment. But for organizations and researchers doing real work with sensitive data, the cloud model has structural problems:
Your data leaves your control. When you send documents to an API for RAG, generate training data through a cloud service, or fine-tune using uploaded datasets, your data is on someone else's server. Even with strong privacy terms, you're trusting a third party.
Per-query costs scale unpredictably. Embedding thousands of documents for a vector store, generating large synthetic datasets, or running extensive fine-tuning experiments can produce surprising API bills.
Internet dependency creates fragility. If the API is down, your pipeline is down. If you're in an air-gapped environment, you're out of luck. Critical workflows can't afford that dependency.
Failure resilience disappears. With cloud infrastructure, a single provider outage can make your entire AI pipeline unavailable. Local-first architecture means your tools work whether or not any external service is reachable.
Vendor lock-in is real. Switching from one LLM provider to another — even if you want to — often requires significant pipeline rework.
MokingBird AI solves all of these, not by being clever about cloud architecture, but by running on your hardware.
MokingBird AI is organized around the MokingBird Node — a desktop application that serves as a hub for three standalone tools:
MokingBird Node
├── mbRAG — Retrieval-Augmented Generation
├── mbDataGen — Synthetic Dataset Generation
└── mbFT — Fine-Tuning PlatformYou can install the Node to get all three tools in one place, or install any tool individually. Each tool also exposes a local FastAPI REST endpoint for programmatic access — so you can integrate them into existing pipelines without changing your workflow and without forcing a full platform migration.
Everything runs on your machine. Windows, macOS, or Linux.
mbRAG is a production-ready Retrieval-Augmented Generation framework — a comprehensive, stable alternative to LangChain, built from the ground up to be reliable and transparent.
It implements every major RAG approach in a unified system: sparse retrieval, dense retrieval, hybrid ensemble methods, multi-query expansion, parent document retrieval, and more. No swapping libraries when you need a different strategy. Everything is in one place.
Four pipeline levels let you trade latency for accuracy:
| Level | Latency | Best for |
|---|---|---|
| L1 Basic | ~0.8s | Fast lookup, simple Q&A |
| L2 Enhanced | ~1.5s | Improved recall, standard workflows |
| L3 Smart | ~2.2s | Context-aware, complex documents |
| L4 Advanced | ~3.5s | Maximum accuracy, research-grade |
Document support covers 17 formats — PDF (with three parser engines and automatic fallback), DOCX, Excel, CSV, JSON, Markdown, PowerPoint, Email, Images with OCR via Tesseract and EasyOCR, Web content, and more.
The 8-Step RetrievalOrchestrator processes every query through: document loading → chunking strategy selection → embedding → vector store retrieval → reranking → context enhancement → response generation → quality validation.
The signature innovation: 6-Level Contextual Retrieval. Traditional RAG loses context when it splits documents into chunks — a chunk about "the agreement" doesn't know what "the agreement" refers to unless the surrounding context is preserved. MokingBird's Contextual Retrieval enriches every chunk with up to 6 levels of surrounding context before indexing, so retrieval doesn't lose the thread of meaning. This alone is responsible for much of the 40–50% accuracy improvement over naive RAG implementations.
You can connect mbRAG to 10 LLM providers — cloud (OpenAI, Anthropic Claude, Google Gemini) or local (Ollama, llama.cpp, vLLM, HuggingFace local) — and 8 embedding providers. Use it fully offline with Ollama for complete air-gap operation, or connect to cloud APIs using your own keys.
Getting high-quality training data is one of the most persistent bottlenecks in applied ML. Public datasets are often noisy, domain-mismatched, or not structured for your specific task. Manual labeling is expensive and slow. mbDataGen is a different approach: generate clean, validated synthetic data from your own documents.
The 5-phase pipeline:
GPRO-Hybrid RL is mbDataGen's core innovation — an original reward learning approach that generates K=4 candidate outputs per data point and scores them:
Total Reward = 0.7 × Field/Process Reward + 0.3 × Outcome/Overall RewardThis means the system simultaneously optimizes for field-level accuracy (is this specific field correct?) and overall quality (does this example make sense as a whole?), producing data that passes both micro and macro quality checks.
The 5-stage validator then filters every generated record through: Schema validation → Distribution checking (does the generated dataset match realistic distributions?) → Deduplication → Grounding (can claims be traced back to the source?) → Novelty (does this add value over existing data?).
Every output includes a HMAC-signed RunManifest — a cryptographically verifiable provenance record that documents exactly how each data point was generated, what sources it came from, and what validation scores it received. This matters when you need to audit or certify training data.
Hardware requirements: minimum 6GB VRAM, 8GB recommended.
Fine-tuning a language model well requires expertise across multiple dimensions: choosing the right technique for your use case, selecting the right framework, estimating memory requirements, setting hyperparameters. mbFT's Smart Config Engine handles this complexity while keeping you in full control.
16 techniques across three categories:
7 supported frameworks — Unsloth (speed-optimized), Axolotl, LLaMAFactory, Hugging Face Transformers, DeepSpeed, FSDP, TRL — with automatic selection recommendations based on your hardware and task.
VRAM pre-simulation is the feature that saves the most time. Before you start a training run, mbFT estimates your memory requirements based on model size, technique, batch size, and sequence length. You see the estimate, compare it to your available VRAM, and decide whether to proceed — without paying for the 20-minute run that would have OOM'd on step 3.
Hybrid GRPO is MokingBird's original contribution: a fine-tuning method that combines reward model signals with rule-based signals, allowing you to shape model behavior with both learned preferences and hard constraints simultaneously. It's particularly useful for domain-specific applications where you have both preference data and hard rules to enforce.
The 6-tier hardware classification system automatically classifies your hardware and adjusts defaults accordingly — from laptop-class GPUs to multi-GPU research workstations.
Researchers working with proprietary or sensitive datasets who cannot send data to cloud APIs. mbRAG for literature review, mbDataGen for generating domain-specific training data, mbFT for adapting foundation models to specialized tasks.
Developers building LLM-powered applications who need reliable, controllable RAG infrastructure and the ability to fine-tune models for specific behaviors without outsourcing the process.
Enterprises with compliance requirements, air-gapped environments, or data residency obligations that preclude cloud AI.
ML engineers who want production-grade tooling — not a tutorial notebook — for RAG, data generation, and fine-tuning.
An important part of the MokingBird AI design is composability. Each tool exposes a local FastAPI REST endpoint — meaning you can integrate mbRAG, mbDataGen, or mbFT into your own pipelines, internal tooling, or product prototypes without changing your existing workflow and without forcing a full platform migration.
Teams rarely operate in completely isolated environments. Local APIs make MokingBird AI infrastructure that integrates with what you already have.
MokingBird AI is free to download. No account required. No credit card. The core features of all three tools are available on the Free tier.
Advanced features — full pipeline levels, all frameworks, all document formats — are available in Premium. Enterprise licensing is available for organizations.
Download from ai.mokingbird.xyz for Windows, macOS, and Linux.
Your data stays yours. It always has.
Your AI should run on your terms.