About ModelBrew AI

Built by a practitioner, not a lab.

Near-zero catastrophic forgetting — validated on Mistral-7B and Gemma-2-9B across 5 sequential domains (3 seeds). ModelBrew AI makes continual fine-tuning practical, accessible, and pilot-ready.

Company

ModelBrew AI

Based in Frederick, Maryland. We build mathematically constrained fine-tuning technology that lets AI teams train on new data without losing what their models already know. Our platform runs on serverless GPUs — no infrastructure to manage, no MLOps team required.

Team

Founders

Healthcare, engineering, and research practitioners who built CRMA after watching fine-tuned models forget critical knowledge with every training run. Detailed bios publishing soon.

KN
Kiran Nayudu
Co-founder

Healthcare practitioner. Built CRMA after running into catastrophic forgetting on clinical fine-tunes.

AN
Aswini Nutakki
Co-founder

Full bio publishing soon.

VN
Vinay Naidu
Co-founder

Full bio publishing soon.

AS
Ashwin Shanmugasundaram
Co-founder

Full bio publishing soon.

Blog

Learn about fine-tuning and continual learning.

Technical articles about stable fine-tuning and why it matters for practical AI.

Featured

Why RAG Falls Short — And What Happens When You Bake Knowledge Into the Model

Everyone is building RAG pipelines. We took a different path: train knowledge directly into the model weights, across sequential domains, with near-zero forgetting.

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Comparison

DPO vs SimPO in 2026: Which Preference-Tuning Method Should You Use?

Side-by-side comparison of Direct Preference Optimization and SimPO — when each works, the trade-offs, and how ModelBrew picks the right one for your dataset.

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Guide

What Is Fine-Tuning? Why It Matters and How It's Changing AI

Fine-tuning explained for a broader audience — real-world use cases in healthcare, legal, code, and finance.

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Technical

What Are LoRA and QLoRA? A Practical Guide to Efficient Fine-Tuning

How LoRA and QLoRA made fine-tuning possible on consumer GPUs — and the stability problems they don't solve.

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Product

How CRMA Solves Continual Learning

Stable backbone, swappable domain adapters, near-zero forgetting. No replay buffers, no growing memory.

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Analysis

Catastrophic Forgetting: The Silent Killer of Fine-Tuned Models

Why every fine-tuning run destroys prior knowledge, and what the research says about fixing it.

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Comparison

CRMA vs LoRA: What's the Difference?

Side-by-side comparison of standard LoRA and CRMA — when you need each, and what happens when you don't use CL.

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Business

The Cost of Forgetting: Why Retraining From Scratch Is Unsustainable

The real-world compute, time, and quality costs of not having continual learning in your ML pipeline.

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Background

Catastrophic Forgetting and Continual Learning, Explained

The shared-parameter dilemma. Why dropout, regularization, and learning-rate tricks don't fix forgetting at the architectural level.

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Impact

Real-World Impact of Continual Learning

Where continual learning changes the outcome — multi-specialty clinical NLP, multi-practice law firms, cross-asset finance.

Read more →
Contact

Get in touch.

Questions about CRMA, enterprise pricing, on-premises deployment, or fine-tuning? Reach out.

Reach us directly

✉kirann@modelbrew.ai
𝕏@MBrew26730
🤗HuggingFace
💬Reddit

ModelBrew AI
Frederick, Maryland

Vision

Roadmap

Where ModelBrew is today, where it's going.

Live

Fine-Tuning

Live

Continual Learning

Future

Agent Training

Future

Real-Time CL

LegalDisclaimers & Legal Notices ▼

No Warranty. CRMA is provided "AS IS" without warranties. Not guaranteed to be uninterrupted or error-free.

Benchmarks. All metrics are from internal experiments under controlled conditions. Results are not guarantees — individual results vary by dataset, model, and configuration. Academic comparisons use different benchmarks.

AI Outputs. Fine-tuned models may produce inaccurate or harmful outputs. Users are responsible for validation. Not for medical, legal, or financial decisions without human review.

Liability. ModelBrew AI's total liability shall not exceed amount paid in the preceding 12 months. No liability for indirect or consequential damages.

IP. CRMA is protected by U.S. provisional patent (filed Feb 2026). Third-party names used for identification only.

Data. Your training data is used only for your job, stored temporarily, deleted after completion. We never train on your data. See Privacy Policy.

Research. Papers are pre-publication drafts, not yet peer-reviewed. Some experiments are single-seed.

Third-Party Services. Built on Modal, Stripe, and Hugging Face. We're not responsible for their outages. Stripe handles payments — we never see your card.

Governing Law. State of Maryland, USA. Exclusive jurisdiction: Frederick County courts.

By using CRMA you agree to these disclaimers, our Terms, and Privacy Policy. Contact: info@modelbrew.ai.