Standard "AI" lists are full of GPT-wrapper apps and marketing agencies. Our AI agents verify model weights (Hugging Face), GPU usage (H100 clusters), and arXiv paper citations to find verified Foundation Model Labs, MLOps Platforms, and Computer Vision Startups.
Why keyword filters fail for deep learning sales.
Every SaaS company now says they use "AI." If you sell GPU cloud compute, data labeling services, or vector databases, you need the teams training models, not just calling APIs.
You need to filter out the prompt engineers and find the Machine Learning Engineers building proprietary weights.
| Metric | Standard "AI" List | Our ICP Database |
|---|---|---|
| Classification | Keyword: "AI" | LLM vs. CV vs. RL vs. Audio |
| Compute | Unknown | H100 / A100 Cluster Detection |
| Tech Stack | None | PyTorch, TensorFlow, LangChain |
| Research | None | ArXiv Paper & Hugging Face Match |
Target labs by their model architecture.
OpenAI/Anthropic competitors. Training massive LLMs. Targets for H100 clusters, data labeling, and RLHF.
Weights & Biases/Arize clones. Targets for cloud orchestration and model monitoring.
Autonomous driving/Medical imaging. Targets for video annotation and edge hardware.
Midjourney/Runway types. Targets for high-speed storage and rendering farms.
ElevenLabs competitors. Targets for low-latency audio processing and telecom APIs.
Pinecone/Weaviate ecosystem. B2B targets for enterprise search and memory.
Protein folding models. Targets for wet lab automation and chemical data.
Copilot competitors. Targets for repository indexing and secure enterprise deploy.
Red-teaming LLMs. Targets for consulting contracts and adversarial datasets.
CoreWeave/Lambda clones. Targets for data center cooling and high-speed networking.
Retrieval Augmented Generation. Targets for knowledge base connectors and compliance.
Inworld AI types. Targets for Unity/Unreal integration and server scaling.
AutoGPT/LangChain builders. Targets for browser automation and API hubs.
Contract review models. Targets for legal datasets and OCR tech.
Personalized learning. Targets for curriculum alignment and student safety.
Virtual try-on / Recommendations. Targets for 3D asset generation and headless commerce.
Running models on-device. Targets for TinyML software and low-power chips.
Algorithmic trading and fraud. Targets for tick data and low-latency colocation.
Physical AI (Covariant). Targets for simulation environments (NVIDIA Isaac).
Suno/Udio competitors. Targets for copyright filtering and DAW integration.
In AI, the "Parameter Count" and "Compute Spend" define the enterprise. A lab training a 70B parameter model has a cloud bill in the millions and needs enterprise-grade data infrastructure.
We extract these "Training Signals" to help you find the true innovators.
If you sell inference optimization, filter for labs with popular Hugging Face models but slow API response times.
Pitch: "Reduce your token latency by 40% and cut cloud costs with our specialized quantization engine..."
We distinguish between companies that call OpenAI API and companies that train models.
This ensures you pitch to the infrastructure buyers, not just the application layer.
Get the data that powers the world's most advanced AI research.
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