AlgoFly AI
The all-in-one place to build and deploy vision AI
98 followers
The all-in-one place to build and deploy vision AI
98 followers
AlgoFly is an AI consulting company that helps businesses build and deploy AI solutions.
Free
Launch Team
Render Sandboxes: a computer for every agentGive every agent its own machine. Starts in under a second
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Thanks for checking out AlgoFly AI 🚀
AlgoFly AI is an on-premise-first AI data platform built for teams working with computer vision and machine learning. It helps manage computer vision datasets, annotation workflows, quality control, and AI operations while keeping sensitive data inside your own infrastructure.
We built AlgoFly because many enterprises cannot upload proprietary data to third-party cloud platforms. Our goal is to give teams the flexibility of modern AI tooling without compromising on data ownership.
I'd love to hear your feedback, feature requests, and use cases. Every comment helps us improve the product.
Thank you for your support! 🙏
The on-premise-first positioning is compelling, especially for regulated industries. Beyond data residency, what's the biggest reason enterprises choose AlgoFly over cloud-native vision AI platforms?
@yagnaveena It's not just data residenct, on-prem deployment comes with a lot of perks. Low latency access to the resources, fast data visibility and access to dataset for iterating on the experiments quickly, we also provide python sdk so live data can be iterated and modified as per workflow-requirements. even though a lot of features resemble and comparable to robolfow or any other cv data management platform, and we had to ship that because those are really strong foundations, biggest advantage we have is small team and good foundation and the product is shaping with early user feedbacks, we keep on improving the features so everyone get's to enjoy more and more interesting ways to deal with their data.
@anubhab_paul99 We are constantly talking to the users and shaping the platform to adapt and provider better user experience and the values. For instance on user asked to annotate multiple images at once so we added a batch annotation mode directly from the images thumbnail biew page. later when improving the work-flow we encluntered that model was flagging wrong ground truth labels with high confidence so we added a filter called model agree / disagree based on that user can review the annotations, quick verification mode was added later so users can track what images they already worked on. There is a collaboration mode so that particular set of images can be assigned to particular user so people don't conflict on sam image files. alos a realtime collaboration mode so many annotators can work on the same file and each other's realtime cursor is also visible to each other so a full figma like experience. We are taking feedbacks and implementing the best features from all platforms to improve the overall quality of the product.
What kind of AI projects are you focusing on most right now, internal automation or customer facing features?
@thamibenjelloun Both