Spotlight

What if AI agents could build, deploy, and manage your machine learning workflows from beginning to end?

Quick Pitch: Plexe is an AI platform that builds and deploys ML models from a simple prompt. A self-correcting team of agents handles data preparation, model selection, training, and deployment, turning complex workflows into a few clicks.

The Problem

• Time Drain: ML projects often take months to complete. Many are canceled before launch.

• Expertise Barrier: Teams rely on ML specialists to get models into production, causing delays and driving up costs.

• Resource Waste: Companies invest millions in ML efforts that deliver too late to have an impact.

Snapshot

• Industry: AI/ML automation

• Headquarters: London, UK

• Year Founded: 2024 (YC Spring 25)

• Traction: Six-figure recurring revenue, more than 2000 GitHub stars, enterprise deal with a major financial institution

Founder Profiles

• Vaibhav Dubey, Co-Founder, CEO: Ex-Proofpoint, Expedia, Imperial College London. Built chatbot serving 9M+ bank customers.

• Marcello De Bernardi, Co-Founder, CTO: Ex-AWS, Expedia, Oxford. Enterprise ML solutions processing billions of daily predictions.

Funding

• Current Round: Raising (~$2M Seed)

• Lead Investor: Y Combinator

• Total Raised: (Pre-Seed $670K)

Revenue Engine

• API Access: One-click deployment with a developer-friendly API

• Market Focus: SMBs and enterprises that need production-grade models without dedicated ML teams

What Users Love

• Deployment is 10 times faster than traditional ML workflows

• No coding required

• Automatic data preparation and model tuning

• Easy API deployment and integration

Playing Field

• DataRobot: Strong in feature engineering but requires ML expertise

• Comet ML: Focuses on experiment tracking with partial automation

• H2O.ai: Offers AutoML but still needs technical oversight

Why It Matters

The global AI and ML market is projected to reach $1.3 trillion by 2032. Yet most companies lack the internal expertise to benefit. Today’s AutoML tools still require manual oversight, leaving an $8 billion opportunity in true end-to-end automation.

What Sets Them Apart

• Agent System: Autonomous agents handle research, experimentation, evaluation, and deployment

• Speed: Deployment is significantly faster than traditional ML approaches

• No-Code Interface: Accessible to non-technical users

• Full Automation: From data connection to API deployment without handoffs

• Community Interest: Over 2,000 GitHub stars show strong developer attention

Analysis

Bulls Case 📈 

• Strong early traction with growing revenue and enterprise interest

• Experienced founding team with ML and go-to-market strength

• Addresses clear market pain point with measurable time savings

• Growing market with 36% CAGR in underserved segment

Bears Case 📉 

• Competing with established and well-funded players

• Needs to prove generalizability of the agent system

• Limited customer validation beyond early traction

• Technically complex product that requires continuous R&D

Verdict

Plexe solves a real ML bottleneck—getting models into production. Its agent-based platform offers speed and accessibility, especially for teams without ML talent. With technical depth and commercial experience, the team has a shot at shaping a new category. The challenge is scaling while keeping the system reliable and flexible as complexity grows.

Jobs @ Plexe

Forward Deployed Engineer

Founding Engineer (Full-Stack)

The Startup Pulse

• Delve — Raised $32M Series A to automate compliance processes for 500+ businesses, reducing manual tasks.

• Aidoc — closed a $150M round to scale its clinical AI platform, with backing from General Catalyst, Square Peg, and leading health systems.

• Yieldstreet — completed a $77M round to make private market investments more accessible. Led by Tarsadia and RedBird Capital.

Written by Ashher

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