Bucket Robotics

What do they actually do

Bucket Robotics turns a part’s CAD file into a production-ready vision model for defect inspection. Their product generates synthetic defect examples from CAD, trains a model, and packages it to run on existing cameras and common edge devices; demos show on‑prem/air‑gapped operation and edge‑ready deployment (e.g., Jetson Orin Nano) so factories don’t need to rip-and-replace hardware (Bucket — how it works, YC company profile, CAD Studio demo).

In production, the model flags and labels defects in-line and can classify defect types so operators can act; the system is refined with real defect data to reduce false positives/negatives. The team has shown at least one deployment on a live production floor and reports early work with automotive and defense customers while still converting demos and pilots into paid contracts (YouTube — live deployment, TechCrunch, YC launch post).

Who are their target customer(s)

• Quality manager at an automotive OEM or Tier‑1 supplier: Manual inspection is slow and inconsistent, and they need inspection ready quickly when new parts or tooling runs start.
• Quality/compliance lead at a defense or regulated manufacturer: Must run verifiable, on‑prem inspections under strict IT/OT rules and cannot send images offsite; needs air‑gapped deployment and integrations.
• Manufacturing/process engineer for injection‑molded, cast, or machined parts: Multiple defect modes and variable lighting/tooling break rule-based systems; needs inspections that generalize across SKUs without vast labeled defect datasets.
• Factory automation/IT lead who owns cameras and edge devices: Limited bandwidth to swap hardware or tolerate high-latency/cloud; needs models that run on existing cameras/edge and integrate with PLCs/MES.
• Operations/production supervisor at a mid‑volume plant adding new SKUs frequently: Human inspection doesn’t scale as SKUs multiply; needs fast, repeatable setups so quality checks are ready before volume launch.

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

• First 10: Close scoped, low‑risk paid pilots with known leads in automotive/defense: use their CAD, run on existing cameras/edge, operate on‑prem if needed, and set clear acceptance criteria plus reference commitments if targets are met (TechCrunch, Bucket — how it works, deployment demo).
• First 50: Codify a short sales playbook for OEMs/Tier‑1s and regulated manufacturers; hire 1–2 BD reps to run outbound and convert pilots to multi‑line contracts using ROI from early pilots and clear on‑prem/edge integration notes in collateral (TechCrunch, bucket.bot).
• First 100: Add channel partners (vision system integrators, camera/edge vendors, MES/PLC integrators) and productize onboarding (CAD templates, deployment checklists, air‑gapped install kit) to reduce per‑deal engineering and reach regional volume deals (YC jobs signals, Bucket — how it works).

What is the rough total addressable market

Top-down context:

Machine vision was about $20.4B in 2024, growing to ~$41.7B by 2030; the QA & inspection slice was ~$9.0B in 2024 (Grand View Research, Grand View — QA & inspection stat).

Bottom-up calculation:

Bucket sells software/services, not cameras. If software+services are ~39% of QA & inspection spend (hardware >61%), that’s ~0.39 × $9.0B ≈ $3.5B relevant pool; focusing on target industries at ~30–60% of QA & inspection yields an estimated SAM of ~$1.0–$2.1B (Grand View, QA & inspection $8.99B).

Assumptions:

• Use Grand View’s QA & inspection 2024 baseline (~$9.0B) and hardware>61% to estimate software+services at ~39%.
• Bucket’s target industries (automotive, defense/aerospace, metal/plastics) account for ~30–60% of QA & inspection spend.
• Bucket primarily monetizes software and deployment services, not camera/lighting hardware.

Who are some of their notable competitors

• Cognex: Incumbent in machine vision for automated quality control with AI-powered vision tools (e.g., deep learning) widely used for defect detection and inspection (Cognex resource).
• KEYENCE: Major provider of machine vision systems and components, combining AI and rule‑based tools with broad hardware offerings for in‑line inspection (KEYENCE Vision Systems).
• Landing AI: LandingLens visual inspection platform for manufacturing with flexible deployment options and workflows tailored to defect detection and QA (Landing AI — manufacturing).
• Instrumental: Manufacturing AI and data platform that detects defects and supports root‑cause analysis using real‑time image and assembly data (Instrumental).
• Elementary: AI vision inspection systems for factory lines that blend deep learning with traditional vision tools and are deployed across Fortune 500 manufacturers (Elementary).