...and commodity prices as well as economic indicators (like unemployment rate, gdp growth , inflation etc.). The model for the forecasts is provided by us and the users would rely on a dashboard to set the parameters of the forecast with instant results, once they hit Run.
People love using similar products but resist paying. You’ll need to either find who will pay or create additional value that’s worth paying for.
Build but think about differentiation and monetization.
Your SaaS idea for forecasting energy, commodity prices, and economic indicators falls into the 'Freemium' category, where users appreciate the product but hesitate to pay for it. We found 7 similar products which indicates a high confidence in this assessment but also increased competition. The average engagement is medium, suggesting there's some interest, but not overwhelming enthusiasm. Because there are similar products, differentiation and monetization are critical for your success. Your challenge lies in identifying the specific value drivers that will entice users to upgrade from the free version to a paid subscription. It's important to really understand who will pay for this and how to create additional value.
Your SaaS idea for forecasting energy, commodity prices, and economic indicators falls into the 'Freemium' category, where users appreciate the product but hesitate to pay for it. We found 7 similar products which indicates a high confidence in this assessment but also increased competition. The average engagement is medium, suggesting there's some interest, but not overwhelming enthusiasm. Because there are similar products, differentiation and monetization are critical for your success. Your challenge lies in identifying the specific value drivers that will entice users to upgrade from the free version to a paid subscription. It's important to really understand who will pay for this and how to create additional value.
We are developing Helios, an AI-powered solution for real-time solar energy forecasting. It aims to help businesses optimize energy usage, reduce costs, and maximize output.
OpenOs is an AI-powered platform for data and financial analysis. It integrates databases, payment gateways, and ML models like GPT and Bert, with a natural language interface. You can create reports, write queries, and make forecasts using natural language.
The Product Hunt launch received overwhelmingly positive feedback, with many users congratulating Vivan on OpenOs. Users highlighted its impressive data integration capabilities, particularly for financial analysis and reporting. Several users emphasized OpenOs's ability to empower non-technical users and its potential to revolutionize data-driven decision-making. There is excitement around the platform's AI capabilities and its potential to consolidate data from various sources, including databases, payment gateways, and marketing platforms. Several users expressed interest in future integrations like NoSQL and Stripe.
The user is asking about the product's roadmap, specifically regarding support for MongoDB and SQL Server. This suggests a desire for broader database compatibility.
A web-based application delivering fast, precise time series forecasting with a best-of-breed machine learning engine: Geneva Forecasting from Roadmap Technologies. Instantly run forecasts from your spreadsheet and receive real-time AI insights.
LockedIn is praised for its user-friendly interface and ease of use, especially with spreadsheet integration for time series forecasting. Users find it comprehensive and convenient for data analysis and decision-making. The speed and precision of forecasts are highly valued. Several users expressed excitement to try the tool and see its performance, particularly on different datasets. Questions were raised regarding its performance with smaller data sets, ideal industries, outlier handling, and potential integrations. Its application for digital asset management and security was also noted.
Users question the product's functionality and optimization, particularly for smaller datasets. They also request more transparency regarding the machine learning techniques employed and data on the accuracy of the results.
After months of brewing the perfect recipe in our AI kitchen, we're beyond excited to introduce Sulie - a fully managed (Model as a Service) platform for time series forecasting that actually works!From day one, we've had one mission: make powerful time series forecasting as easy as ordering your morning coffee.Now, businesses can make accurate forecasts from their data without the hassle of building complex models from scratch.We kept hearing the same frustrations from data teams trying to work with foundation models for time series forecasting: 1. "The zero-shot performance is about as reliable as a chocolate teapot!" 2. "Fine-tuning these models? Easier to teach a cat to bark!" 3. "And don't get me started on covariate support..."What makes Sulie special? • Automated model fine-tuning using LoRA - no PhD required! • Full covariate support for more accurate predictions. • Go from zero to production-ready forecasts in minutes (not weeks). • Zero ML complexity - we handle all MLOps heavy-lifting (you focus on the insights).We're already working with amazing customers who are: • Optimizing their supply chains • Making precise financial forecasts • Building custom models using Sulie's powerful embeddingsAnd this is just the beginning! Stay tuned for deep dives into these use cases in the coming weeksCheck us out: Python SDK: https://github.com/wearesulie/sulie Website: https://sulie.co
The comments highlight that the product addresses key pain points in time series analysis for foundation models. However, there is a lack of available content to provide further details.
The product lacks clarity on how it handles concept drift and updates its models.
After months of brewing the perfect recipe in our AI kitchen, we're beyond excited to introduce Sulie - a fully managed (Model as a Service) platform for time series forecasting that actually works!From day one, we've had one mission: make powerful time series forecasting as easy as ordering your morning coffeeNow, businesses can make accurate forecasts from their data without the hassle of building complex models from scratch.We kept hearing the same frustrations from data teams trying to work with foundation models for time series forecasting:"The zero-shot performance is about as reliable as a chocolate teapot! " "Fine-tuning these models? Easier to teach a cat to bark! " "And don't get me started on covariate support..."What makes Sulie special?• Automated model fine-tuning using LoRA - no PhD required!• Full covariate support for more accurate predictions.• Go from zero to production-ready forecasts in minutes (not weeks).• Zero ML complexity - we handle all MLOps heavy-lifting (you focus on the insights).We're already working with amazing customers who are:• Optimizing their supply chains• Making precise financial forecasts• Building custom models using Sulie's powerful embeddingsAnd this is just the beginning! Stay tuned for deep dives into these use cases in the coming weeksReady to revolutionize your forecasting game? Check us out:Python SDK: https://github.com/wearesulie/sulieWebsite: https://sulie.coLet's make forecasting fun again!
Introducing Sulie, a managed platform for time series forecasting.
Zero-shot performance unreliable, fine-tuning difficult, poor covariate support.
I wanted to share the whitepaper-style pitch/demo video I made for my company, ScrAI, to get any early feedback from the community here.We just kicked off a seed round, and while reaching out to investors and networking are now a priority, our biggest priority is getting feedback from potential users. That includes data scientists and analysts doing demand forecasting, market forecasting, economic forecasting, weather forecasting, anomaly detection, and denoising in time series data.So, for those of you who do time series forecasting:-What are the biggest challenges you face?-Given the existence of a tool that has the mathematical machinery to handle complex time series data and make accurate predictions, how would you want to interact with that tool to fit into your workflow? (E.g., would you like it to work in a similar way to calling models in tensorflow?)-Assuming you watched the video, what challenges do you think we will face? I'd love to hear your opinions and answer any questions.
Hey, We just launched the MVP of OpenOS,a data & financial analysis tool! It tackles common challenges like needing technical skills for querying data, integrating open-source models, and scattered data. With OpenOS, you can:1. Query relational databases & financial info using natural language. 2. Generate reports like MIS & financial statements with a single command. 3. Forecast user growth, cashflow, etc. It's powered by GPT, Tapas, and Prophet. We built it as a project/tool & would love for any of you guys to try it out & provide feedback as we build the product for public launch.
