Whether you’re looking to analyze your product reviews, develop a sales pipeline or analyze the latest earnings report, Reframe is your ultimate solution.
We are building building core infrastructure to simplify the process of building, deploying, and managing LLM-powered AI agents on tabular data, making it the most efficient solution available. Gone are the days of laborious and time-consuming setups; our solution is designed to be seamless and user-friendly. From conceptualization to deployment, we have carefully engineered each step to ensure a smooth and productive journey.
Our team has experience building video search & understanding, person ID, radiology AI, and semantic search for some of the largest media companies in the world, Department of Defence (DoD) and Intelligence partners. And we have deep familiarity with the challenges in the eCommerce space.
If you want to better supercharge your productivity with tabular data, we can help. Please get in touch.
5 min Read As VCs, you’re constantly on the hunt for rising startups with that special combination of solid fundamentals, standout teams, and breakthrough potential. But finding those diamond-in-the-rough startups can be like searching for needles in a haystack. This is where Reframe comes in.
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5 min Read Managing product reviews is a crucial part of any ecommerce business. It helps to understand customer feedback and improve the products and services offered to them. But with the increasing number of reviews that businesses receive on their product pages, it becomes difficult to manage them manually. This is where Generative Artificial Intelligence (AI) comes into the picture.
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5 min Read Intro: As a financial research analyst, you know that researching companies thoroughly is critical to making informed investment recommendations. However, with hundreds of companies to research, the process can be extremely time-consuming. That's where Reframe comes in. Reframe is a powerful no-code database powered by AI agents that can automate and streamline your company research process.
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5 min Read Animating still images to create realistic videos is a challenging problem in computer vision and graphics. Existing methods like video GANs can generate relatively short videos from static images, but often suffer from temporal incoherence over longer durations. In the paper "Generative Image Dynamics" published at CVPR 2023, researchers from Google propose a new approach to model scene dynamics that enables generating coherent, indefinitely long videos from single images.
Read more about Generative Image DynamicsCreative coding often involves exploratory programming to produce digital artwork. Artists typically start with a high-level semantic goal like a "stained glass filter" and then vary code parameters like shape, color, lines, and opacity to achieve visually pleasing results. However, translating semantic constructs to code syntax can be effortful. Current programming tools also do not facilitate rapid creative exploration. To address these challenges, we present Spellburst, a large language model powered creative coding environment. Spellburst provides: (1) a node-based interface for creating generative art and exploring variations through branching/merging, (2) expressive prompt-based interactions for semantic programming, and (3) dynamic prompt-driven interfaces and direct code editing to seamlessly switch between semantic and syntactic exploration. Evaluations with artists show Spellburst's potential to enhance creative coding and inform the design of computational creativity tools that bridge semantic and syntactic spaces.
Read more about Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts