Loading...
Frontier labs, model providers, open-weight checkpoints, and the applied-AI layer on top. A single page mapping who builds, who funds, and who leads in ai & machine learning.
This hub aggregates the ai & machine learning surface VC Deal Flow Signal tracks: 20 curated companies with public GitHub orgs, 36 venture funds whose published thesis covers ai & machine learning, and notable engineering leaders whose work shapes the category. AI/ML is the highest-momentum technical category in venture. The engineering signal here is contributor influx (new researchers joining the org) and language-bias drift (Python → Rust/CUDA migrations as models hit inference scale). PE operating partners use this as a bolt-on filter for portfolio software companies adopting AI features. Use it as a starting point for sourcing, diligence, or competitive scans.
56
Tracked companies
36
Active funds
2
Engineering leaders
In ai & machine learning we track four engineering-acceleration primitives across every monitored org: commit velocity (rolling 14-day vs trailing 12-week median), contributor influx (new committers in the trailing 4 weeks), repo creation pulse (new public repos shipped in the trailing 8 weeks), and language-bias drift (when a new primary language appears in production code). The six-signal panel published at /methodology is empirically tied to imminent fundraise probability — see SSRN paper 6606558.
AI/ML is the highest-momentum technical category in venture. The engineering signal here is contributor influx (new researchers joining the org) and language-bias drift (Python → Rust/CUDA migrations as models hit inference scale). PE operating partners use this as a bolt-on filter for portfolio software companies adopting AI features.
later · github.com/vercel
series c · github.com/PostHog
series b · github.com/get-convex
series b · github.com/modal-labs
series b · github.com/replicate
later · github.com/anthropics
later · github.com/openai
series b · github.com/mistralai
series c · github.com/cohere-ai
later · github.com/huggingface
public · github.com/cloudflare
series a · github.com/inngest
series a · github.com/browserbase
seed · github.com/e2b-dev
series b · github.com/fw-ai
series b · github.com/togethercomputer
series c · github.com/groq
series a · github.com/dust-tt
series b · github.com/getcursor
series a · github.com/lovable-dev
series a · github.com/remotion-dev
series b · github.com/langchain-ai
seed · github.com/crewAIInc
seed · github.com/letta-ai
seed · github.com/mastra-ai
series c · github.com/elevenlabs
series c · github.com/perplexity-ai
later · github.com/sourcegraph
series b · github.com/weaviate
series a · github.com/qdrant
series b · github.com/milvus-io
later · github.com/runwayml
series c · github.com/Stability-AI
series c · github.com/AI21Labs
series b · github.com/pinecone-io
series c · github.com/writer
series c · github.com/wandb
seed · github.com/langfuse
series b · github.com/Arize-ai
series a · github.com/braintrustdata
seed · github.com/Helicone
seed · github.com/voyage-ai
series a · github.com/jina-ai
later · github.com/anyscale
seed · github.com/vllm-project
series a · github.com/run-llama
series b · github.com/deepset-ai
series c · github.com/modularml
series c · github.com/Exafunction
seed · github.com/continuedev
series b · github.com/comet-ml
series a · github.com/bentoml
series a · github.com/outerbounds
seed · github.com/ollama
series b · github.com/predibase
later · github.com/thoughtspot
Menlo Park · seed through growth, AI and enterprise software-heavy
Menlo Park · seed through growth across multiple verticals
San Francisco · seed through growth, contrarian-thesis
Menlo Park · seed through Series B in enterprise and AI
Menlo Park · seed through growth across multiple geographies
Palo Alto · seed through Series B across multiple geographies
London / San Francisco · seed through Series B across enterprise and consumer
San Francisco · growth-stage enterprise software primarily
New York · growth through public
New York · growth-stage software (Series B through public)
Cambridge / San Francisco · seed through growth across multiple verticals
Menlo Park · seed through growth across tech and healthcare
Mountain View · seed through Series C across life sciences and tech
Menlo Park · seed through growth, deep-tech and frontier
San Francisco · pre-seed and seed
San Francisco · seed (rare Series A)
San Francisco · seed and pre-seed across multiple verticals
Palo Alto · pre-seed and seed
Distributed · pre-seed (tournament-based sourcing)
San Francisco · pre-seed accelerator (batches)
Distributed (city programs) · pre-seed accelerator
San Francisco · pre-seed and seed globally
San Francisco · pre-seed and seed across verticals
San Mateo · pre-seed accelerator with frontier-tech focus
New York · growth through pre-IPO
New York · series A through growth
Boston · seed through growth
San Francisco · seed through growth
Menlo Park · seed through growth
San Francisco · series A through growth
Menlo Park · growth through pre-IPO
Hong Kong / London · growth through pre-IPO
Seattle · series A through growth
Santa Clara · seed through growth
San Francisco · series A through growth
New York / Menlo Park · seed through growth
We currently track 20 curated ai & machine learning companies whose GitHub orgs are self-published on their homepage, devrel blog, or hiring page. The full list with per-company signal pages is at /signal — filter by sector. We do not track private orgs, leaked employee data, or LinkedIn-inferred profiles.
36 funds in our /fund/ corpus publish ai & machine learning as part of their stated thesis. Each /fund/[slug] page is an independent summary of the fund's public thesis mapped against our engineering-acceleration signal panel. The corpus is not exhaustive — it is the seed set we built around Marcus 100 (Corp Dev, PE operating partners, non-engineer tech VPs).
Two workflows. (1) Source: weekly digest of ai & machine learning companies whose engineering acceleration matches your stage and check-size filters, delivered before competitive rounds form — see /firstlook. (2) Validate: given a deal already in your pipeline, retrieve the public engineering trajectory via the public MCP server at /api/v1 or the openapi.json at /api/openapi.json.
No. This is a curated seed corpus, not a Crunchbase-scale database. We add companies, funds, and founders deliberately when they meet our public-source threshold (self-published GitHub handle, public thesis, well-documented role). For the full open-source coverage of every ai & machine learning startup we score, see /stage/[stage]/ai-ml — the scraped leaderboard.
Compute, orchestration, inference, and the serving layer underneath the model providers. A single page mapping who builds, who funds, and who leads in ai infrastructure.
IDEs, frameworks, build systems, package managers, and the productivity layer engineers actually touch. A single page mapping who builds, who funds, and who leads in developer tools.
Edge platforms, runtimes, networking, observability primitives, and the platform-as-a-service layer. A single page mapping who builds, who funds, and who leads in cloud infrastructure.
OLTP, OLAP, vector stores, embedded engines, and the storage layer underneath every modern app. A single page mapping who builds, who funds, and who leads in databases.
Logs, traces, metrics, error tracking, profiling, and the runtime-visibility surface for engineering orgs. A single page mapping who builds, who funds, and who leads in observability & monitoring.
Warehousing, transformation, BI, and the analyst-facing query surface on top of operational data. A single page mapping who builds, who funds, and who leads in data analytics.
Payments, banking infrastructure, embedded finance, fraud, and the API surface for financial workflows. A single page mapping who builds, who funds, and who leads in fintech.
Documents, collaboration, knowledge management, and the prosumer + team productivity layer. A single page mapping who builds, who funds, and who leads in productivity & knowledge work.
Game backends, multiplayer servers, server orchestration, cross-game avatars, and the live-ops layer beneath studios. A single page mapping who builds, who funds, and who leads in gaming infrastructure.
The free Acceleration Watch: five venture-backed teams accelerating on the engineering signal, translated into plain English — 21 to 47 days before the deck circulates. No code-reading, no card.