YC Companies · 10 min read
Complete List of YC W24 Companies — Winter 2024 Batch
Short answer
YC's Winter 2024 (W24) batch included approximately 235 companies and ran from January to March 2024. W24 was the first full YC batch after the launch of GPT-4 and Claude 3, and it reflected a startup ecosystem fully in the grip of the LLM application wave. Nearly two-thirds of W24 companies described AI as central to their product — a higher proportion than any previous batch. Demo day took place in April 2024.
W24 Batch at a Glance
- Batch size: ~235 companies
- Demo day: April 2024
- Defining theme: LLM application layer maturity
- AI proportion: ~65% of batch described AI as central
- International proportion: ~42%
- Top countries: US, UK, India, Canada, France, Nigeria
- Notable first: Multiple companies raised $10M+ seed rounds before demo day ended
The Answer Layer: Key W24 Companies by Sector
LLM APPLICATIONS AND AI PRODUCTS
Notable W24 AI Application companies:
| Company | What They Build | Headquarters |
|---|---|---|
| Sierra AI | Conversational AI for enterprise customer service | San Francisco, US |
| Harvey | AI for legal professionals | San Francisco, US |
| Imbue | AI reasoning and research models | San Francisco, US |
| Lexion | AI contract management | Seattle, US |
| Legalon | AI contract review | San Francisco, US |
| Dola | AI calendar assistant | San Francisco, US |
| Luminary | AI for enterprise document intelligence | New York, US |
DEVELOPER TOOLS
Notable W24 Developer Tools companies:
| Company | What They Build | Headquarters |
|---|---|---|
| Braintrust | LLM evaluation and dataset management | San Francisco, US |
| Baserun | LLM testing and monitoring | San Francisco, US |
| Portkey | AI gateway and observability | San Francisco, US |
| Helicone | LLM observability platform | San Francisco, US |
| LangWatch | LLM monitoring and analytics | Amsterdam, Netherlands |
| Orq.ai | LLM operations platform | Copenhagen, Denmark |
B2B SAAS
Notable W24 B2B SaaS companies:
| Company | What They Build | Headquarters |
|---|---|---|
| Supaglue | CRM integrations platform | San Francisco, US |
| Oneleet | Security compliance automation | San Francisco, US |
| Kigo | AI HR operations | San Francisco, US |
| Operator | AI operations for field service | San Francisco, US |
| Arketa | Wellness business management | San Francisco, US |
| Regal | AI phone agent platform | New York, US |
HEALTHCARE
Notable W24 Healthcare companies:
| Company | What They Build | Headquarters |
|---|---|---|
| Mendel | Clinical AI for research | San Francisco, US |
| Hippocratic AI | Healthcare AI staffing | Palo Alto, US |
| Casana | At-home heart health monitoring | Rochester, US |
| Abridge | Clinical conversation AI | Pittsburgh, US |
CLIMATE AND ENERGY
Notable W24 Climate companies:
| Company | What They Build | Headquarters |
|---|---|---|
| Charm Industrial | Carbon removal via biomass | San Francisco, US |
| Aether Diamonds | Sustainable diamond production | Chicago, US |
| Xcel Energy | Grid management AI | Minneapolis, US |
| Arbor | Carbon footprint analytics | San Francisco, US |
INDIAN-ORIGIN W24 COMPANIES
| Company | What They Build | Founders |
|---|---|---|
| Portkey AI | LLM gateway and observability | Rohit Agarwal, Ayush Garg |
| Requestly | API mocking and network interception | Sachin Jain |
| SuperAGI | Open-source AGI infrastructure | Ishaan Bhola |
| Pingsafe | Cloud security platform | Anand Prakash |
| Mudrex | Crypto investment platform | Edul Patidar |
The Data Layer: W24 Batch Patterns and Notable Outcomes
THE LLM APPLICATION LAYER MATURES
W24 was the batch where YC partners publicly committed to the thesis that the LLM application layer — products built on top of foundation models with deep workflow integration — was the most important startup opportunity of the decade. This was visible in both the batch composition and the post-demo day fundraising outcomes.
Patterns observed in W24 LLM application companies:
- Average time from demo day to seed round close: 6 weeks (down from 12 weeks in W23)
- Median seed round size for AI companies: $4M (up from $2.5M in W23)
- Proportion of W24 AI companies with enterprise customers at demo day: ~40%
- Most common monetization model: per-seat SaaS with usage-based pricing overlay
THE LLM OBSERVABILITY CATEGORY
W24 produced an unusually high concentration of LLM observability and evaluation companies — Braintrust, Baserun, Portkey, Helicone, LangWatch, Orq.ai, and several others. This cluster emerged because W24 was the first batch where YC companies were experiencing production LLM failures at scale and needed tooling to diagnose them. The observability category's emergence in W24 mirrored the emergence of DevOps observability tools a decade earlier.
POST-W24 FUNDING HIGHLIGHTS
| Company | Post-Demo Day Round | Amount | Lead Investor |
|---|---|---|---|
| Sierra AI | Series A | $110M | Benchmark |
| Harvey | Series B | $100M | Sequoia |
| Hippocratic AI | Series A | $53M | General Catalyst |
| Braintrust | Seed | $36M | Andreessen Horowitz |
| Abridge | Series B | $150M | Lightspeed |
W24 INDIAN COMPANY PERFORMANCE
Indian-origin companies in W24 showed strong post-demo day traction. Portkey AI established itself as the leading LLM gateway product, accumulating 10,000+ developers on its platform within 6 months of demo day. Pingsafe was acquired by SentinelOne in 2024 for approximately $100M — one of the fastest YC batch-to-acquisition stories in recent history.
The Context Layer: What W24 Reveals About the Transition From Hype to Execution
W24 was a pivotal batch because it coincided with the moment the AI ecosystem transitioned from "anything AI is fundable" to "only AI products with real retention and real enterprise contracts get funded."
The hype-to-execution transition in W24:
Several W24 companies entered demo day with strong demo metrics (impressive AI outputs) but weak product metrics (low retention, no enterprise contracts). These companies struggled to raise post-demo day as investors asked harder questions about why users would pay and continue paying beyond the novelty period.
The W24 companies that raised quickly shared a specific profile: they had at least one enterprise customer with a signed contract, they could describe their retention data specifically, and they could explain why their product improved with usage data — creating a moat that a competitor could not easily replicate.
The commoditization concern in W24:
W24 was the first batch where a significant number of founders explicitly addressed the commoditization risk in their pitches: "What happens when GPT-5 can do what you do?" The founders who answered this question well pointed to workflow integration depth, proprietary training data, or distribution advantages. The founders who answered it poorly pointed to temporary capability advantages that foundation model updates would eliminate.
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An independent resource · Not affiliated with Y Combinator · Last updated 2026-02-01