AI Stack | Musubi

Models

Reach for the right model, from fast to frontier.

Choose the right model for the job, from fast classifiers to frontier LLMs, and keep the flexibility to change your mind as the work evolves. Swap models without rebuilding the whole flow, then test each setup against your own evals and benchmarks before it goes live.

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Choosing a model for a violence policy, with the reasoning behind the score and a control to switch models

PolicyLM (Musubi Mini)

A 1B open-weights model for real-time classification, developed by Musubi. Cheap enough to run on 100% of your traffic, it scores the good stuff as readily as the harmful.

Frontier and open LLMs

The newest frontier and open safety models, added as they ship. They come matched to your use case and optimized for speed, reasoning, or safety.

Adaptive classifiers

Custom behavioral models for adversarial fraud that train on full profile data, content, and behavior. They return a risk score and the signals behind it, and retrain daily on your moderators' decisions.

Agents

Custom AI agents.

Point an AI agent at your queue and it works each case across steps, clearing the straightforward ones and handing your team the tough ones with a full reasoning trace.

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Document & ID review
Detect fraudulent IDs, check for tampering, flag AI-generated images, and cross-reference metadata.
Ad review
Open the linked landing page, parse the creative, and run brand-safety and policy checks across both surfaces.
Coordinated Abuse
Surface connected accounts, entities, and repeat actors behind a campaign.
Custom Configuration
Configure an agent for any case type your team faces.

In early deployments, automating the investigative legwork has cut manual review by around 60%, so your analysts can dig in on the cases that really get their brains going.

Evaluation

Ship AI that holds up under real conditions.

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Model Benchmarking

Compare frontier LLMs and fine-tuned safety models against your own labeled data. Check for bias, coverage gaps, and regressions before they touch production.

Evaluation sets

Build evaluation sets that surface edge cases and gaps. Stratify by category, severity, or any field your team cares about.

Policy and prompt testing

A/B test different policy versions and prompts to see which has higher accuracy, precision, recall, or F1 scores. Revert to past versions any time you need to.

Benchmarking results comparing a model against a previous version, showing agreement, percent changed, macro F1, and net correct.

Workflows

Custom workflows and decision trees.

Chain classifiers, LLMs, adaptive fraud models, and human review into one workflow that matches how your team makes decisions. Straightforward cases can resolve automatically, ambiguous ones escalate to a larger model, and high-stakes calls go to a person.

Build the flow with drag-and-drop controls, then reshape it any time without waiting on engineering.

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FAQs

Answers to questions we’re frequently asked.

Accuracy is a lever you control, not a number you inherit. Write the policies, set the threshold per category, and fine-tune on your own data when you need more. Every content decision comes back with the policy applied, the reasoning, and a confidence score; every account decision comes back with a risk score and the signals behind it. Test it on your own traffic before you deploy.

Yes. Musubi Mini scores in around 100ms and is cheap enough to run on 100% of your traffic. Route only the ambiguous calls up to a slower LLM or an agent, so you get depth where it matters without paying for it everywhere. It runs in production today at over a million requests a day.

Yes. It plugs into the console and systems your team already works in, with no rip-and-replace and no walled garden. Bring your own rules engine, data sources, and review queues, and compose our models and workflows alongside what you've built.

No. Your data runs your decisions, isn't pooled with other customers, and isn't used to train shared models. Fine-tune a model on your own team's decisions if you want, and that version is never shared.

Build the right AI stack for every T&S problem.