VectorShift

When I was at Harvard, everyone wanted a prestigious job at McKinsey or Goldman Sachs. At least 3 hours of my day as a McKinsey analyst was extracting data from reports. But it takes <30 minutes to build a data extraction tool using VectorShift. From reports, GPT4o instantly: - Extracts and formats key financial figures - Identifies trends in the market, competitors, and management commentary - Analyzes potential risks and forecasts

A personal injury law firm used to have 15 paralegals doing routine research tasks They now leverage AI search through VectorShift to increase productivity and reduce non-billable hours The search engine: 1. Finds similar cases and searches through case documents 2. Identify inconsistencies between documents 3. Identify case risks

A YC edtech company had 50+ course assistants answering student questions on an online forum They built an AI assistant with VectorShift that answers >80% of the questions The assistant was built in <1 hour and: + Answers questions immediately instead of waiting for a human assistant to respond + References and links to lecture notes and transcripts + Livesyncs with new lectures as they are added to Google Drive

A real estate consulting firm spent 1000s of hours answering basic client questions The average response time was 6.3 hours via email They built an AI assistant with VectorShift that now responds instantly The assistant took less than 1 week to build and: + References their proprietary research articles + Pulls live transaction data from their database + Is SOC 2 type 2 compliant with no training by LLM providers on their data

A YC company was quoted $43k for an employee HR copilot They built it in 1 hour instead of hiring additional headcount With VectorShift, employees now receive answers to questions about: 1. Onboarding and offboarding procedures 2. Benefits and HR policies 3. Number of remaining PTO / sick days

A YC series C company spent 10+ hours a week searching through 1000s of customer contracts An SDR built an AI search engine with VectorShift and automated the entire process The tool: - Live-syncs to both Google Drive and Dropbox - Answers search queries based on retrieved contracts - Finds and chats with specific contracts

A recruiting agency receives 15k+ resumes per month Recruiters used to spend 3 hours daily 1) inputing candidate data into a database and 2) searching through resumes to send to clients We automated this entire process on VectorShift in <20 minutes: - Applicants upload their resumes onto a form - Claude 3.5 Sonnet extracts data from the resumes and inputs into a Google sheet - Recruiters use a natural language search tool to find candidates

During YC, people laughed at VectorShift. But here’s an unpopular belief: if you want to build a successful startup, work on DUMB ideas. VectorShift is a no-code platform to help anyone build AI workflows. Essentially, a “horizontal” solution to build custom AI tools. However, in our batch, “vertical” AI solutions were all the rage. Our batchmates (and a lot of VCs) told us: “Enterprises are already using vertical AI agents to replace entire teams” “Every unicorn SaaS company will have a vertical AI unicorn equivalent” In fact, YC just made a video titled: Vertical AI Agents Could be 10x bigger than SaaS. However, Harj, our YC group partner defended our approach in the video (clip below): “Enterprises are a little unsure about exactly what agents they need and one approach I've seen is letting Enterprises spin up custom agents. It's something I've seen from one of my companies called VectorShift that I funded about a year ago.” We couldn’t agree more (and thanks for the shoutout Harj!) When I look back at recent startups that have succeeded, I’m reminded of the story of Scale AI. When Scale was at YC S16, their batch mates laughed at their product entitled “Human API.” People said that human-labeled training data wasn’t a “technical” solution. That it was just “Filipinos as a service.” However, those people didn’t realize that the buyers of training data don’t care that your solution is not “technical.” They just want the data. Today, Scale is worth $14B and growing. Similarly, what people don’t understand is that most human workflows in a company are custom and buyers of LLM software just want them automated. Custom workflows need custom solutions. We think that most vertical SaaS solutions will be replaced by custom AI workflows that you can build on your own, because it is faster, cheaper, and more performant.

A YC ed-tech company used to manually input data from 15k+ student forms 20+ full time staff used to spend >50% of their day doing this We built a workflow in <2 hours that automated the entire process: - Staff now uploads the forms to VectorShift - GPT4 mini and advanced OCR extracts student information (even from messy handwriting) - VectorShift adds data directly into their ERP system of record

Enterprise AI solutions are 99% showmanship – cool internal demos without real ROI. That is the cold hard truth. <10% of enterprises have GenAI solutions in production. Most enterprises have: 1. Appointed a head of AI 2. Identified use cases across business units 3. Built proof of concepts So why are enterprises stuck before moving to production? - Uncertainty around AI investments: Companies are concerned about the implications of investing in AI solutions that may not deliver the expected results (or worse, result in negative reputational risks). - Too many choices: Firms are flooded with options and are not sure what to choose. Should I use an out of the box solution? Should I build the solution in-house? Should I outsource to a vendor? - Complex decision making structures: I have seen many enterprises form multiple committees that complicate decision-making processes, leading to delays and indecision. We built VectorShift to help enterprises move from proof of concept to production with confidence. Feel free to reach out - happy to share best practices: albert@vectorshift.ai