Ten companies building the infrastructure layer of AI. Seed through Series A.
San Francisco, CA
LLM Inference Infrastructure — Building the serving layer that sits between raw model weights and production traffic at scale.
New York, NY
AI Agents for Enterprise — Orchestration platform for multi-step agentic workflows inside Fortune 500 operations teams.
Seattle, WA
MLOps & Model Lifecycle — Continuous training, automated evaluation, and deployment pipeline tooling for production ML teams.
Berkeley, CA
AI Safety & Alignment Research — Interpretability tooling and red-teaming infrastructure for AI labs and regulated industries.
Austin, TX
Vertical AI for Recruiting — Domain-specific models that automate technical screening and sourcing for engineering-heavy companies.
San Francisco, CA
Foundation Model Tooling — Fine-tuning, RLHF pipeline, and adapter framework for enterprise teams customizing base models.
Toronto, Canada
Training Data Curation & Pipelines — Data quality infrastructure that filters, deduplicates, and structures datasets for pre-training and fine-tuning.
Remote
AI-Native Workflow Automation — No-code agent builder for business processes that need real decisions, not just templates.
San Francisco, CA
Vector DB & Embedding Infrastructure — High-throughput vector database built for retrieval-augmented generation at production scale.
Boston, MA
Vertical AI for Healthcare Documentation — Ambient AI clinical documentation that reduces physician administrative burden by 60%.
We write checks at Pre-Seed and Seed, with follow-on through Series A. Typical first check: $500K–$3M. We lead or co-lead, and take an active role — especially on technical diligence, hiring, and infrastructure partnerships.
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