Hi HN!I’m Anton, a founding engineer at Athena Intelligence. Athena is an AI data platform+agent that supports workflows for enterprise teams.Over the last two years we’ve deployed the state-of-the-art data LLM tooling to enterprise clients across several domains, zeroing in on co-piloting workflows and offloading more and more of the work to AI agents.Today we’re proud to share a demo of the first (to our knowledge) autonomous data AI agent!Athena engineering team has built several key pillars for end-to-end autonomous workflows:* Data platform: Athena connects to all structured data sources and supports embeds of all popular unstructured document types. We're built on Apache Superset and Iceberg, balancing secure multi-database queries on client warehouses with lightning-fast data pipelines for complex workflows. Building an unstructured document platform in addition to that has enabled us to offer our users granular control over their knowledge retrieval pipelines. We support a broad scope of use cases, ranging from low-latency, high-volume simple retrievals with Groq Llama 3, to involved, self-reflective RAG pipelines powered by the most capable models (Clude 3 models, GPT-4T) and adaptive architectures.* Execution platform: Athena has integrated SQL Editor and Jupyter Notebooks environments to work with data, Notion-like reports and Figma-like whiteboard to compile end knowledge work outputs, and Browse environment to explore and retrieve information from web. We are big believers in providing users with more UI options so they can experiment and find workflows that work for them. Most of our “apps” use Yjs backend, allowing live collaboration between people and agents.* Headless and Human modes: every environment and its metadata are transparent to and can be manipulated by Athena (in addition to humans through UI). That means that any item of work can be collaborated on between AI and humans in a two-way fashion, in real time.* Agentic tools and orchestration: each environment has a set of retrieval pipelines, tools, agents and agent orchestration items to support execution on specific tasks (example: find latest product catalog online) and end-to-end workflows (update our numbers and externalities, make a forecast for next months orders). We serve a wide range of models, both proprietary and open-source, through agentic architectures built with Langchain and Langgraph.As the result, you have an agent that can:* Intake real-world problems through existing workplace channels like email or Slack.* Compile and iterate on a high-level plan to solve the problem.* Execute the plan step-by-step, incorporating self-correcting and 'human-in-the-loop' behaviors.* Access data sources, including freeform browsing and querying both structured and unstructured documents.* Synthesize the final writeup for the multi-step analysis and disseminate it to platforms and colleagues downstream.You can see the agent in action in the linked video.The autonomous mode is in closed beta for now. We’re deploying it across select customers for the next month, battle-testing the architecture and taking notes. We're also excited about expanding and upgrading key parts of the architecture to unlock features such as:* “Save checkpoints” agentic workflows to go back and forth between “execution paths” and specific nodes for a given workflow.* "Time travel" capability with Apache Iceberg to enable versioning of data pipelines.* Agent-to-agent interaction: have different agents collaborate on an analytical report to populate, review, and evaluate it before a human check is required.* Asynchronous agents, autonomously executing background tasks like documenting newly connected datasets, identifying data drifts and compiling regular reports.Excited to learn about other autonomous data agents out there and looking forward to your comments!

AI-driven data management platform Over 1 million developers are using Chat2DB

An agentic AI tool designed to seamlessly interact with existing databases using an agentic AI approach. This project aims to simplify the process of querying databases, making it more intuitive and accessible for everyone.

Hey HN! I built a platform called QueryLab that offers instant AI-powered cloud database sandboxes with natural language querying, visualizations, and external data integration.Each database sandbox is paired with a powerful LLM, unlocking a range of exciting use cases!Here is a quick demo: https://www.youtube.com/watch?v=sk6LvoN9rCwAny feedback or ideas I'm all ears :)

Hey folks!I’m the founder of LogicLoop AI SQL Copilot. If you’re familiar with querying data, you’ve probably spent quite some time manually writing and debugging SQL queries. If you’re a non-technical business user, you will often need to wait and ask engineers to help you write the SQL to pull the data you need. If you’re an engineer, you might be overwhelmed by all these data pull requests from business users.With LogicLoop's AI SQL Helper Suite, you can ask your data questions using natural language. Ask AI to discover patterns, suggest, write, fix and optimize SQL queries directly on your custom data schema. You can get results on your own data instantly. Once you have your results, you can visualize them on a dashboard or set up recurring alerts and automations. AI makes data more accessible for business users, and faster to work with for engineers/analysts.Some ways LogicLoop's AI SQL Helper Suite has helped early users: - Business operations teams can find top customers to email and automate outreach - Risk analysts can discover gaps in their fraud monitoring rules to flag more bad actors - Data engineers can fix and optimize long queries to reduce costsWe don’t think this is a panacea that can replace data analysts, but we think this will make data analysis faster and more accessible to more people. Would love for you to give it a try and share any feedback. Thank you.

Hi HN Community,We're a team of software engineers who've experienced the complexity of crafting SQL queries. To address this, we've developed a tool that simplifies this process.Introducing Loofi, an AI-powered SQL Query Builder. Loofi streamlines query building and leverages AI algorithms for intelligent recommendations.We value community feedback and invite you to try out Loofi. Your thoughts and suggestions will help us improve our tool.Here's the link to Loofi: https://loofi.dev/For any questions or assistance, feel free to reach out. We're eager to engage with this community.Your valuable feedback is much appreciated!

Roe AI is a new data warehouse specializing in unstructured data that other data warehouses can't handle. It has built-in AI that makes analysis easy with natural language instead of Python scripts e.g performing a complex customer segmentation with a prompt.

Generate arduous database queries in a snap

AI-powered platform designed to simplify SQL query creation

Are you tired of struggling with SQL syntax? 🤨 Introducing an AI-powered web app to generate SQL query from plain english 📃 Try it out today and see how much time it can save you ⏳

SQLBuilder.ai is the ultimate tool for developers to effortlessly generate, optimize & validate SQL queries in a single click. Simplify query building with remarkable accuracy & efficiency! Say goodbye to SQL worries & focus on what truly matters. Try it now!

Excited to share Nexa's public beta and get feedback from the HN community!--The ProblemCompanies today spend way more time querying and analyzing data than acting on it. That needs to be flipped.At startups without data teams (who we’re built for), engineers waste precious time writing SQL queries for others (e.g. Growth, Product, Sales), and then those teams spend hours manually analyzing and formatting the data to make decisions. All of this is time we can give back with AI.--Introducing NexaWith Nexa, connect your MySQL / PostgreSQL database or upload CSVs and extract insights in plain English, not code. These insights can be added to Notion-page like dashboards and shared with your stakeholders instantly.Behind the scenes, our AI SQL agent writes queries, generates summaries, creates visualizations, and suggests prompts to help you uncover insights you didn’t think of.You might also find that uploading a CSV and exploring your data in Nexa is much faster and more pleasant than writing IF statements in sheets and copy / pasting charts and tables from sheets to docs ;-).--Try it out and let us know what you think.And a HUGE thanks for the support and feedback!

Hi friends,We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more.The key feature of EVA is its AI-centric query optimizer. This optimizer is designed to speed up AI-powered applications using a collection of optimizations inspired by relational database systems. Two of the most important optimizations are:+ Caching: EVA automatically reuses previous query results (e.g., inference results), eliminating redundant computation and saving you money on inference.+ Predicate Reordering: EVA optimizes the order in which query predicates are evaluated (e.g., running faster, more selective deep learning models first), leading to faster queries.Besides saving money spent on inference, EVA also makes it easier to write SQL queries to set up multi-modal AI pipelines. With EVA, you can quickly integrate your AI models into the database system and seamlessly query structured and unstructured data.We are constantly working on improving EVA and would love to hear your feedback!

Hey HN, Michael and Alexey from Narrative BI here.BackgroundWe've been working on Natural Language Query tools for quite some time. Back in the pre-LLM era, we launched a natural language to SQL startup called FriendlyData. It was later acquired by Service now where we integrated it into the Now Intelligence platform. Then we started Narrative BI with the thesis that business users like marketers don't always know how to ask data questions, so our goal was to build a product where data insights are generated proactively, before users even ask questions.EvolutionThe general dynamics of Generative AI going mainstream created an interesting opportunity. We still believe that the future of BI lies in proactive analytics and easy data integrations. However, with increased AI awareness, we've seen a lot of interest from our users in natural language capabilities, so we decided to take it to the next level.Why don't just use ChatGPT?The problem with ChatGPT and other chat-based LLM tools that they hallucinate a lot. ChatGPT answers look plausible so most of users don't bother to verify if the answers match the actual data. However our research shows that ChatGPT hallucination rate is 47% for more or less complex data sets, such as Google Search Console data, for example. For business data analysis accuracy is the key as wrong answers can lead to misinformed decisions. Another limitation is data integration. AI data analyst comes with pre-built data integrations, so you can easily connect your data sources such as GA4, Google Ads, Google Search Console, FB Ads, Hubspot and Salesforce. More data sources coming soon and we're also doing custom integrations for enterprise.We're here to answer your questions and look forward to your feedback!

Hi! I'm a uni student and I find the ways of learning and teaching SQL to be archaic. So I built this tool that helps you visualize queries, query your excel sheet and generate graphs using AI(just ask)I'd love to hear about your experience. Any suggestions, bugs, criticism leave them in the comments.