Overeasy

What do they actually do

Overeasy builds an open-source framework for constructing multi-step computer-vision workflows. Developers chain together zero-shot models to auto-annotate images and stand up end-to-end pipelines for tasks like detection and classification without first collecting large labeled datasets (GitHub).

The system uses "Agents" and "Workflows" to structure image-processing steps and pass outputs between them, so teams can script, iterate, and visualize pipelines quickly with examples and docs to get started (docs.overeasy.sh). The team also offers IRIS, an AI agent focused on automated annotation via prompting and iterative correction to speed dataset creation (YC launch).

Who are their target customer(s)

• ML engineers at startups building custom vision features: Manual annotation and dataset engineering slow iteration and block launches. They want auto-annotation and an end‑to‑end workflow without building an annotation pipeline from scratch (YC launch; GitHub/docs).
• Data-ops or labeling managers with large image collections: Annotation costs and turnaround time grow with dataset size. They need tooling that can synthesize or auto-label at scale instead of relying solely on manual vendors (YC launch).
• CV researchers and rapid prototypers: Setting up experiments is slow because task‑specific data collection/annotation is time-consuming. They want a framework to chain zero‑shot models and iterate on annotations quickly (GitHub/docs; docs site).
• Small product teams/early-stage companies without large ML budgets: They lack resources for large labeled datasets. They want open, composable tooling to get usable vision models without expensive data collection (GitHub README; YC launch).
• Applied product owners in safety, retail, manufacturing, etc.: Off‑the‑shelf models miss product‑specific classes and retraining needs labeled examples. They need an auto-annotate → correct → train workflow to produce deployable models faster (docs/examples; YC launch).

How would they acquire their first 10, 50, and 100 customers

• First 10: Directly recruit early adopters from YC startups, GitHub stargazers/issue authors, and YC launch responders; run hands-on free pilots where Overeasy sets up the first workflow and measures time/cost savings, then capture testimonials and a case study (YC launch; GitHub).
• First 50: Publish ready-to-run Colab notebooks, 1‑click templates, and vertical workflows (e.g., PPE) with short how-to videos and weekly office hours; drive trials from technical communities and the README/docs, converting demo bookings into pilots (docs; examples).
• First 100: Offer a formal pilot program and integrations with labeling vendors/MLOps/model-hosting partners so data-ops teams can try Overeasy in their stack; price pilots to cover engineer time and collect KPIs, and publish 3–5 case studies to present at CV/industry meetups to influence buyers (YC launch; docs).

What is the rough total addressable market

Top-down context:

The global data collection and labeling market—the core spend Overeasy targets—is estimated at roughly $4.9B in 2025 and projected to reach about $17.1B by 2030 (Grand View Research). The broader computer‑vision market is ~$19.8B in 2024 and projected to exceed $58B by 2030, though that includes hardware and services beyond Overeasy’s scope (Grand View Research).

Bottom-up calculation:

Illustratively, if ~10,000 active CV teams globally adopt automated annotation/workflow software at an average of $15,000 per year, that implies a bottom‑up TAM of ~$150M for software-first auto‑labeling and workflow tooling. Expanding to larger enterprises and heavier usage would increase the figure.

Assumptions:

• ~10,000 teams worldwide are actively building or maintaining CV features and are viable software buyers.
• Average annual software spend on auto-annotation/workflow tools of ~$15k per team (SaaS + usage).
• Excludes hardware and most one-off services; focuses on software-first auto-labeling and dataset/workflow tooling.

Who are some of their notable competitors

• Roboflow: Developer-focused vision platform with dataset management, model‑assisted/foundation‑model auto‑labeling (Autodistill/Grounding DINO/SAM), training, and deployment. Competes on fast auto‑annotation and end‑to‑end workflows; more of a hosted all‑in‑one than a composable open-source pipeline (Auto Label docs).
• Labelbox: Enterprise data‑labeling platform with AI‑assisted pre‑labeling, human QA, and managed services. Competes when teams need scalable, audited labeling operations and vendor‑managed review workflows (product · AI assistance).
• Scale AI: Service‑heavy data engine combining human‑in‑the‑loop labeling, programmatic pipelines, and GenAI tooling. Overlaps when customers need high‑volume, vetted annotation or full data‑engineering support rather than a lightweight self‑service toolkit (overview · labeling guide).
• Datagen: Synthetic‑data provider that generates fully annotated, photorealistic images/videos to bypass manual labeling. An alternative to auto‑labeling when teams are willing to train on synthetic datasets (platform).
• CVAT: Open‑source Computer Vision Annotation Tool with semi/auto‑annotation plugins (e.g., SAM) and self‑hosting. Low‑cost, composable option for teams willing to stitch together their own pipelines (AI tools · overview).