Datasaur

Fully automating an end-to-end process in just 3 months ?? We recently worked with a client to transition their data extraction workflow from 100% tedious, manual work to a semi-automated human-in-the-loop (HITL) process. We then leveraged OCR techniques and state-of-the-art LLMs to release a fully automated model that beats the HITL process on accuracy. How? It turns out even humans make mistakes when reviewing and validating projects. Want to transform your back-office operations with guaranteed accuracy? Let’s talk!

Our engineering lead Steven Ihan “vibe coded” a Chrome extension in 10 minutes. Use Datasaur’s LLM Labs to quickly build your own tool!

Amazon Web Services (AWS) just published a straightforward, practical guide to building a chatbot using AWS Bedrock and Datasaur's LLM Labs. It shares: - how to narrow down a list of hundreds of candidate models to evaluate - determining evaluation criteria for success - running a manual, anecdotal evaluation - scaling that into an automated, recurring evaluation It's a clear, no-frills guide that can have your chatbot up and running in an afternoon.

We're honored to have been nominated for the 2025 Product Awards, presented by Products That Count. These awards celebrate the most influential and innovative products leading the global digital revolution. It's a privilege for LLM Labs to be part of this distinguished group, and for our innovations in design and GenAI development to be recognized and rewarded. #2025productawards #productawards #productmanagement

Datasaur's new "Show and Hide Bounding Box" feature allows users to toggle the visibility of labels, reducing visual clutter and improving focus and efficiency when working with overlapping or complex bounding boxes.

???????????????? ???? ?????????????????? ???? ?????? ???????? The DeepSeek model is now available in the LLM Labs Sandbox! ?? ?? DeepSeekChat-V3 has been making waves, as they claim training took <$6m - a small fraction of what other foundation models cost. If this becomes the norm, it could upset the entire industry. You can test DeepSeek’s performance, speed, and cost alongside popular models such as GPT-4(o), Llama 3.2, Gemini 1.5 Pro, Claude 3.5 Sonnet v2, and more – all for free!

Looking to Improve Task Distribution? Let Self-Assignment Do the Work #nlplabeling #datasaur #namingentityrecognition #llm

Building a custom Slack Chatbot is simpler and more useful than you might think ????. With Slack’s accessible API and tools like LLMs (e.g., Claude, Llama, Mistral), you can create a tailored AI-powered chatbot without deep technical expertise. Common Misconceptions ? Specialized ML knowledge is required. ? Preconfigured LLM applications make it easy to deploy. Why Slack? Slack AI proves the value of AI-powered features, but building your own chatbot can be more cost-effective and customized for your needs. Steps to Build Your Chatbot 1?? Set up an LLM Application: Use Datasaur’s LLM Labs to create and deploy the "brain" of your chatbot. 2?? Configure the Slack App: Quickly set up app settings using a manifest file. 3?? Build & Deploy: Clone our repository to handle communication between Slack and the LLM. 4?? Connect & Test: Link your chatbot to Slack with API endpoints and keys. With these steps, your chatbot will summarize threads, answer FAQs, and assist with Slack tasks seamlessly. Plus, you can expand this approach to platforms like WhatsApp or Discord ??.

Labeling text in complex documents used to feel like walking a tightrope—too much content captured, or key details missed. ??? That’s why we’re excited about Text Selection: a game-changing feature that lets you highlight and label specific words or phrases directly in your documents. It’s faster, more intuitive, and precise, giving you full control over every label—whether it’s dates, names, or any critical data point. ?? Read our article to see how it’s transforming text labeling.

80% of corporate AI projects fail ???—and after a year in the trenches helping companies rescue their initiatives, we've seen why. Here are the biggest challenges: ?? Talent Gap: AI needs specialized expertise, but practical experience in deploying scalable solutions is scarce. ?? Data Issues: Fragmented data and inconsistent formats slow progress. Seamless integration and expert involvement are often missing. ?? Unclear Metrics: Misaligned ROI goals and an overfocus on big models instead of the right models hinder success. ?? Privacy Concerns: Security risks arise when proprietary data meets third-party models, and open-source tools require careful management. ?? Operational Hurdles: Model drift, scalability issues, and low user adoption complicate deployment. Failures aren’t the end—they’re opportunities to learn ?. Every success we’ve helped achieve started by understanding past missteps. Let’s share these lessons and move forward together ??