What's in this set
AngelMatch's pitch deck database currently holds 6 pitch decks tagged to computer vision, a small but genuinely useful set for founders trying to see how vision and perception startups have pitched investors. It is worth saying plainly that this is a small sample, not a comprehensive library, so treat it as a starting point rather than the whole picture. The rounds run from pre-seed through Series A, and the years on record stretch from 2014 to 2017, so most of these decks were built before computer vision became a label startups added to every AI deck.
Two of the six decks are pre-seed, two are Series A, one is seed, and one lists no round at all. That split means this set skews toward companies proving early traction rather than scaling already-proven demand, which tracks with how thin the venture market for standalone vision plays still was in the mid-2010s. If you want a starting structure before you build your own outline, AngelMatch's fundraising templates cover the standard slide sequence founders use at this stage.
Three decks are worth opening first. Magic Leap's Series A deck raised $50M in 2014 and is the largest raise in the set, useful for seeing how a hardware-heavy vision company frames a massive round before it has shipped a consumer product. Faception's Series A deck raised $500K in 2016 and shows how a facial analytics company handles the trust and ethics questions that come up before an investor even has to ask. StackShare's pre-seed deck raised $300K in 2015 and is a good look at how a developer-tools company with a computer vision component pitches a broader software story first.
The rest of the set fills out the picture. Lumific's pre-seed deck raised $100K in 2014 for a photo editing and curation product. Numina's seed deck from 2017 does not disclose an amount raised, and one deck in the set, from GPU Eater, lists an undisclosed round with no amount or year on record. Undisclosed and missing figures are common in older decks and get excluded from the totals below rather than guessed at.
What investors look for in a computer vision pitch deck
Computer vision decks succeed or fail on questions that differ from a typical software pitch, and the decks in this set show why.
The model has to be defensible, not just accurate. Anyone can train a model on public data today, so investors want to see where the real edge sits: proprietary training data, a hard-to-replicate pipeline, or a use case where the accuracy bar is high enough to shut out generic APIs. Faception's Series A raise in 2016 came at a point when facial analysis was still a genuinely hard problem, and its deck had to make the case that its model held an edge general-purpose vision tools did not.
Hardware-adjacent vision products need a capital plan, not just a product roadmap. When the vision system is tied to physical hardware, investors want to see how much of the raise goes to compute, sensors, or manufacturing versus software. Magic Leap's $50M Series A in 2014 is the clearest example in this set of a vision company where the hardware story and the capital story had to be told together, since so much of that round went to sensors and optics rather than a typical software burn rate. Founders in a similar spot can also study the augmented reality pitch deck set, since several computer vision companies straddle both categories.
Data pipeline and where the training data comes from matters more than the demo. A polished demo earns a second meeting, but a credible answer to where the labeled data comes from and how it keeps improving is what earns a term sheet. StackShare's $300K pre-seed round in 2015 leaned on developer usage data as its evidence, a pattern that recurs across vision and analytics-adjacent decks at that stage, where usage data often substitutes for a traditional demo.
Regulatory and trust questions surface earlier than founders expect. Facial recognition, surveillance, and biometric products draw scrutiny investors want addressed on the page, not left for due diligence later. This shows up directly in Faception's positioning as a facial personality profiling company and is worth building into a computer vision deck rather than treating as an afterthought once term sheets are already on the table.
Pre-seed and seed vision decks lean on a working prototype over a market-size slide. Lumific's $100K pre-seed round in 2014 and Numina's seed round from 2017 both closed on the strength of a functioning product rather than a total-addressable-market slide, which tracks with how early-stage vision investors evaluate risk. They would rather see the model work on real footage or real street data than read a market-size calculation built on assumptions.
Beyond the decks themselves, the broader pattern holds at every stage: a working demo of the model on real inputs, a plain answer to where the training data comes from, and a clear line between the vision system and the product it powers. That is true whether the deck is chasing a pre-seed check or a $50M Series A, and it repeats across every deck in this set regardless of round size. Once your own deck is in shape, AngelMatch's pitch deck hosting gives you a trackable link to send investors instead of an email attachment.
Find computer vision investors
Six computer vision decks live in this set today, and the strongest ones lean on a working demo and a defensible model story rather than a slide full of claims about artificial intelligence. If you are building your own round, the fastest next step is finding people who already fund vision and perception startups. AngelMatch's investor database lists 2,400+ investors tagged to computer vision, and you can filter that list by stage and check size to find computer vision investors who write pre-seed and seed checks.
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