Dynamiq.ai
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Dynamiq is a platform built for engineers and data scientists to build, deploy, test, monitor and fine-tune Large Language Models for any use case the enterprise wants to tackle.
🛠️ Workflows: Build GenAI workflows in a low-code interface to automate tasks at scale
🧠 Knowledge & RAG: Create custom RAG knowledge bases and deploy vector DBs in minutes
🤖 Agents Ops: Create custom LLM agents to solve complex task and connect them to your internal APIs
📈 Observability: Log all interactions, use large-scale LLM quality evaluations
🦺 Guardrails: Precise and reliable LLM outputs with pre-built validators, detection of sensitive content, and data leak prevention
📻 Fine-tuning: Fine-tune proprietary LLM models to make them your own
⛑️ Air-gapped Solution: Dynamiq specializes in enabling clients that manage highly sensitive data to leverage LLMs while maintaining ironclad security thank to stringent security controls.
🕹️ Vendor-Agnostic: Through integration capabilities, our clients can build GenAI applications using a variety of models from providers such as OpenAI and have the flexibility to switch to other providers if needed.
🧲 All-In-One Solution: We cover the entire GenAI development process from ideation to deployment
🏋️ AI Assistants: Equip your team with custom AI assistants that streamline tasks, enhance information access, and boost productivity
🧠 Knowledge Base: Build a dynamic AI knowledge base with our platform that streamlines decision-making, enhances productivity and allows employees to spend less time navigating through extensive company documents, files, and databases
🎢 Workflow Automations: Design powerful, no-code workflows that leverage your enterprise's knowledge to enhance content creation, CRM enrichment, and customer support
ModelRed is a security platform that helps teams test and monitor large language models before deployment. It runs both universal probes, which expose common risks like jailbreaks, data leakage, and biased responses, and domain-specific probes tailored to sensitive areas such as finance, healthcare, legal, and government. This dual approach provides a complete view of how models behave in general use and in high-stakes contexts.
ModelRed supports leading providers including OpenAI, Anthropic, AWS Bedrock, AWS Sagemaker, Google Vertex, and Hugging Face, as well as custom REST endpoints. A key feature is the ModelRed Score, a benchmark that summarizes an LLM’s security posture across different categories. Teams can run automated evaluations, receive detailed reports that explain issues in clear terms, and track results over time to compare models, demonstrate compliance, and show improvements, giving customers confidence that their LLMs are safe, reliable, and ready for critical applications.
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Dynamiq.ai's answer
Heads of: Data Science, Engineering, Innovation at big enterprise companies
ModelRed's answer:
Our primary audience is teams building and deploying large language models who need to ensure safety, reliability, and compliance. This includes AI startups bringing new products to market, research labs testing frontier models, and security or compliance teams at larger organizations. These users care about understanding vulnerabilities, proving trustworthiness, and avoiding costly failures before their models are widely adopted.
Dynamiq.ai's answer
Kubernetes, OpenAI, Anthropic, Cohere, Golang, Python
ModelRed's answer:
ModelRed is built as a microservices-based platform using Go and TypeScript for backend services, with PostgreSQL for data storage and AWS S3/SQS for distributed job handling. The frontend is built with Next.js and React, with Prisma for data access. We integrate with major LLM providers including OpenAI, Anthropic, AWS Bedrock, AWS Sagemaker, Google Vertex, and Hugging Face, as well as custom REST endpoints. NVIDIA GPUs and related tools are planned for accelerating large-scale adversarial probe execution and model evaluations.
ModelRed's answer:
ModelRed is unique because it combines universal probes that catch common risks with domain-specific probes that uncover issues in sensitive fields like finance, healthcare, legal, and government. It also provides the ModelRed Score, a benchmark that makes it easy to compare models and track improvements over time. With support for OpenAI, Anthropic, AWS Bedrock, AWS Sagemaker, Google Vertex, Hugging Face, and custom endpoints, ModelRed delivers comprehensive and automated red-teaming across the AI stack.
ModelRed's answer:
A person should choose ModelRed because it offers both universal and domain-specific probes, giving a more complete picture of how large language models perform in real-world and high-stakes scenarios. The ModelRed Score makes results easy to understand and compare across models, while automated reports help with compliance and ongoing monitoring. ModelRed also works across all major providers and custom endpoints, so teams can evaluate their entire AI stack in one place instead of relying on fragmented tools.
ModelRed's answer:
ModelRed was born from the realization that large language models are being deployed faster than they are being secured. Early in our work with AI platforms, we saw that most models went live without rigorous testing for jailbreaks, data leakage, or misuse. At the same time, enterprises and startups alike were asking how they could prove their models were safe for real-world use. We created ModelRed to close that gap by offering automated red-teaming, domain-specific evaluations, and a standardized security benchmark. Our goal is to make security and trust a natural part of every LLM deployment, just as cloud and network security became essential in earlier technology waves.
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