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Atlas of AI

Specialized models for every business job, ranked and ready to build.

Find AI worth owning

580 Specialist Models, one per business decision.

48 of 580 models

#49 overall

Attack Path

Which chain of identity, network, and cloud weaknesses would let an attacker reach a crown-jewel asset from a given entry point?

Owned by CISO, VP Security Engineering

Software
Build it: Attack PathRead the guide: Attack Path

#64 overall · #3 in Retail

Cloud Exposure

Which of our cloud assets are actually reachable from the public internet, and which of those are genuinely at risk?

Owned by Chief Information Security Officer, VP Security Engineering

Financial services
Build it: Cloud ExposureRead the guide: Cloud Exposure

#74 overall · #3 in Cross-industry

Expense Audit

Should this expense report or payment be approved, held for review, or escalated as suspected fraud or policy violation?

Owned by Controller, VP Finance Operations

Cross-industry
Build it: Expense AuditRead the guide: Expense Audit

#118 overall · #10 in Banking

KYB

Should this business be approved for onboarding, and at what risk tier?

Owned by Chief Compliance Officer, Head of BSA/AML

Banking
Build it: KYBRead the guide: KYB

#128 overall · #9 in Retail

Promotion Abuse

Is this promotional redemption, coupon use, or referral legitimate, or does it represent abuse that should be blocked, clawed back, or investigated?

Owned by VP Trust & Safety, Head of Growth Marketing

Retail
Build it: Promotion AbuseRead the guide: Promotion Abuse

#143 overall

Incident RCA

What was the technical root cause of this software/infrastructure incident, and what should be fixed to prevent recurrence?

Owned by VP Engineering, Head of Site Reliability Engineering

Build it: Incident RCARead the guide: Incident RCA

#160 overall · #9 in Cross-industry

Benchmark Coverage

Which real-world capability gaps in an AI system are NOT covered by its current benchmarks/eval sets, and what new eval cases should be added to close those gaps?

Owned by Head of AI, ML Platform Lead

Cross-industry
Build it: Benchmark CoverageRead the guide: Benchmark Coverage

Choose a model you can own.

Model leaderboards are a starting point. The decision is your task, your output contract, your hardware, and your right to deploy. This directory turns that into a short list you can test.

  • Qwen

    Alibaba Cloud · China
    Qwen is Alibaba Cloud’s open-weight model family. It spans small models that are practical to fine-tune through larger mixture-of-experts releases.
    Active in Oumi

    Profile: Qwen

  • DeepSeek

    DeepSeek · China
    DeepSeek publishes open-weight language models, including large mixture-of-experts systems. The family is often shortlisted where reasoning and coding matter.
    Listed by Oumi

    Profile: DeepSeek

  • Llama

    Meta · United States
    Llama is Meta’s open-weight model family. Its wide ecosystem makes it a familiar baseline for teams evaluating or fine-tuning their first custom model.
    Listed by Oumi

    Profile: Llama

  • Mistral

    Mistral AI · France
    Mistral is a European model family spanning open-weight and commercial releases. The right starting point depends on the exact release and deployment constraints.
    Listed by Oumi

    Profile: Mistral

  • Gemma

    Google · United States
    Gemma is Google’s open-weight model family. It includes compact checkpoints that can be useful when hardware limits and deployment footprint drive the decision.
    Listed by Oumi

    Profile: Gemma

Oumi benchmark data is coming. We’ll add reproducible task benchmarks once the evaluation recipe, model version, dataset, hardware, and scoring criteria are published.

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Questions about the Atlas

  • What is the Atlas of AI?

    The Atlas of AI is a directory of specialist-model opportunities organized around business decisions and tasks. It helps you explore where a focused model may create value, then move toward evaluation and ownership.

  • How do I find a Specialist Model for my business task?

    Start with the business decision or repeated workflow you want to improve, then use Atlas filters and rankings to narrow the relevant category. Treat the result as a discovery hypothesis and validate it against your data, evaluation criteria, and deployment needs.

  • Which open-weight model should I start from?

    Start with the open-weight model that fits your task, quality target, context needs, license, hardware, and deployment constraints. There's no single best model for every workflow, so use Atlas evidence and your own evaluation set.

  • What evidence supports each Specialist Model listing?

    A listing is a structured starting point grounded in the task, model, benchmark, or other evidence shown with it. Review the evidence and run your own evaluation before treating a listing as a production recommendation.

  • Can I build a Specialist Model directly from the Atlas?

    The Atlas helps identify a promising task and starting model. From there, use Oumi's build and evaluation workflow to define behavior, assemble data, test the model, and decide whether to train and deploy it.

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2026 State of Owned AI Report

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Murali Minnah, webinar speaker

The Business Case for Owning Your AI

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