Superpowered AI is an end-to-end knowledge retrieval solution that makes it easy to build production-ready LLM applications with access to external knowledge.
Hello Product Hunt!
We’re excited to announce the release of The SuperStack alongside our new Chat Endpoint. Now, you can easily deploy conversational LLM applications with knowledge retrieval built-in. Our SuperStack suite of technologies directly targets common RAG failure modes, like hallucinations caused by out-of-context search results. Dive in and test it using our Playground, Python SDK, or clone these examples.
Problem 🤔
Many uses for LLMs, including most customer support and employee productivity applications, require effectively connecting LLMs to external knowledge sources. Doing this well is very hard. Current retrieval augmented generation (RAG) methods just take existing information retrieval methods and stuff the results into an LLM prompt. This works for simple demos, but usually isn’t reliable enough for real-world production applications.
Solution 🧠
Superpowered AI offers a REST API that lets you connect external data sources (like product documentation, for example) to LLMs. We leverage proprietary RAG technology that we’ve developed (we call it the SuperStack) to dramatically improve performance and reliability for a wide variety of use cases.
The SuperStack 🦸
The SuperStack has three components that directly tackle the problems with standard RAG pipelines:
AutoQuery → Convert user inputs into well-formed search queries for better retrieval results.
Relevant Segment Extraction (RSE) → Dynamically group clusters of relevant results into longer sections of contiguous text to provide better context to the LLM. This is especially useful for more complex questions, where the answer isn’t contained in a single sentence or paragraph.
AutoContext→ Automatically inject descriptive context into text chunks and embeddings, to capture the full context of each chunk of text, reducing the likelihood of poor search results and hallucinations.
Chat Endpoint 💬
Given that LLM applications often involve conversational interactions, we recently launched our Chat Endpoint to make it easy to configure and deploy chat applications that utilize our knowledge retrieval pipeline. We currently support GPT-3.5-Turbo and GPT-4, with Claude-Instant-1 and Claude-2 as our fallback models. We’ll have more models coming soon.
Custom Solutions 🌐
For companies that don’t have the resources or expertise to build their own LLM-based solutions, we’re here to help. Whether you're looking to enhance internal productivity or launch innovative new products with LLMs, we will work with you to bring your vision to life. Our team is dedicated to helping businesses of all sizes leverage the potential of AI to drive efficiency and customer love.
We look forward to hearing your feedback!
Congrats Team Superpowered AI on the successful launch on Producthunt! Your innovative solution to retrieve knowledge is really a game-changer. Just a thought, perhaps a user-friendly tutorial or demo could be included, to help new users better understand how to leverage your LLM applications. All the best!
We really wanted to avoid all the hassle of dealing with pinecone,llm,abcdxyz.
All we wanted was something like chatbase but with an API. Superpowered fit the bill!
Using it in our SaaS and looking forward to seeing the platform grow more powerful and stronger.
Congratulations on the launch of Superpowered AI! This is an incredibly exciting development in the field of AI and knowledge retrieval. The idea of an end-to-end solution that simplifies the creation of large language model (LLM) applications is not only innovative but also highly valuable in today's tech landscape.
Been using Superpowered for some time now and love it to quickly plug in RAG into my apps. Currently working on a CRM for PE funds and Superpowered will again be my RAG stack of choice. Could luck further growing your product guys!
@timvanheugten Thank you, Tim! You've always been an impressive RAG specialist and we're pumped to have you with us. Can't wait to see the end result of the CRM for PE funds!
Hey @dillon24, terrific work on launching this product! You're helping to open up a number of amazing possibilities for so many people. With the AI retrieval augmented generation, it will really level up the game in a major way. Kudos to you for all of the hard work and dedication you've put into it!
@winged_piggy Thank you so much for your kind words and encouragement! While I'm grateful for the shout-out, this achievement is truly a team effort. I want to give a huge shout-out to my amazing cofounders – @zmccormick7, @justinmclark, and @nick_mccormick1 – whose brilliance and hard work have brought Superpowered AI to life!
Also, a special thanks to our incredible Superpowered AI user base. Your enthusiasm, creative applications, and honest feedback have not only impressed us but also played an important role in guiding the direction of our platform. It's builders like you who inspire us to keep innovating and pushing the limits of this retrieval technology!
Superpowered AI's end-to-end knowledge retrieval solution for building LLM applications seems to be a significant advancement. Wishing the team great success! How does the platform handle complex queries, and what sets it apart from other knowledge retrieval solutions?
@venky_venky4 Hey Venkatesh, Thanks for the comment! Handling complex queries relies on a feature we call AutoQuery, which uses an LLM to generate high-quality search queries for your knowledge bases. For a complex query, AutoQuery will turn the original query into 2 or 3 well-formed search queries. This leads to higher quality results rather than directly using the complex user input for the search. If you pop into Chat in the user interface, you can view the search queries created by AutoQuery by clicking into the "Sources" included with each response. You'll see the queries that were used at the top.
What sets us apart is that this isn't a standard RAG pipeline. This is a RAG pipeline with superpowers. We're extremely focused on retrieval techniques that allow LLMs to be reliably used in production, and the features in the SuperStack are the main drivers that we want to highlight. To us, context is everything, or else your responses are riddled with hallucinations. In the upload phase, AutoContext provides document-level and knowledge base-level context in every chunk prior to embedding, making every chunk useful to the LLM. In the post-processing phase, RSE combines relevant chunks into longer sections of text to provide better context to the LLM. This is super useful with dense unstructured documents! Ultimately, our end-to-end solution allows you to get this entire high-performing retrieval pipeline from one platform, plus you get the highest quality generative output possible. With citations ;)
Superpowered AI