Inspiration
During our respective tenures at leading companies such as Microsoft and Amazon, Harsh and I often encountered businesses grappling with the challenge of optimizing their pricing models. We both recognized the glaring need for a pricing model that dynamically adapts in real-time, mirroring consumer consumption patterns. This realization, combined with my specialized experience with pay-per-use models at Google Vertex, served as the bedrock for the inception of CostCurve.
What it does
CostCurve offers a pioneering dynamic pricing platform tailored to cater to the unique demands of both online and offline businesses. Through a meticulous analysis of real-time market dynamics, customer sentiment, and global economic indicators, CostCurve empowers businesses to strategically adjust their prices, ensuring maximized profitability and heightened customer satisfaction.
How we built it
Drawing from my comprehensive background in product development, AI, and machine learning, and Harsh's expertise in software engineering and market analysis, we collaborated to mold CostCurve into a platform that's both deeply integrative and adaptable. At its core, CostCurve operates on robust algorithms that swiftly process vast data amounts, delivering real-time pricing insights.In this project we use Square's Transaction API to do price prediction and revenue Machine learning model. Square's Terminal API helps gets the data of transactions.
Code for Custcurve Service:-https://github.com/newtonjain/SpendWise-TC-Disrupt-FE/tree/hackathonSquare Code for CostCurveML service:- https://github.com/newtonjain/CostCurve-models
Challenges we ran into
One of the most prominent challenges was devising a method for the seamless integration of CostCurve with diverse platforms, be it online platforms or offline Point of Sale (POS) systems. Striking a balance between the platform's adaptability and user-friendliness was another hurdle. Our goal was to ensure businesses of varying scales and expertise could effortlessly harness CostCurve's potential.
Accomplishments that we're proud of
The successful onboarding of five startups, all keen on refining their pricing strategies, stands out as a significant accomplishment in the initial phases of CostCurve. The endorsements and referrals from these early adopters have not only been a source of encouragement but have also validated our vision and efforts.
What we learned
Building CostCurve was a journey filled with learning. It emphasized the paramount importance of maintaining a continuous dialogue with our user base and integrating their feedback. Grasping the unique challenges and requirements of various businesses was instrumental in fine-tuning our platform.
What's next for CostCurve
As we chart the future course for CostCurve, our focus is on amplifying its AI capabilities, ensuring even sharper pricing predictions. We aim to broaden our user base, reaching out to a diverse range of businesses, from emerging startups to large-scale enterprises. Collaborations with other SaaS providers, e-commerce platforms, and retail entities are also in the pipeline, positioning CostCurve as a forerunner in the dynamic pricing domain.We want to generate a heatmap to visualize large scale data of orders on map. To learn and then recommend to restaurant/businesses what people around the area like use something similar to Turf.js to make maps and do analysis of order data collected from the API we have in Square. As our front-end and back-end are in Javascript, we can integrate for sure.
Feedbacks
Google Cloud:-
1.User-Friendly Front-End Deployment: While the power and flexibility of Google Cloud is appreciated, many developers find the process of deploying front-end applications to be intricate and cumbersome. I urge the team to streamline and simplify the deployment process, possibly by introducing a more intuitive GUI or a wizard-like setup, ensuring that even developers with minimal cloud experience can deploy their applications with ease and confidence.
- Simplified VertexAI REST API Calls: The current process to interact with VertexAI via REST API can be daunting for many users. To accelerate adoption and user satisfaction, it's imperative that the Google Cloud team provides a more straightforward and well-documented approach to make API calls to VertexAI.
Square:-
- Provide unique order numbers for every order. So that restaurants can determine sequence of order.
AI-Driven Order Status & Readiness Predictions: Utilize machine learning models trained on historical restaurant data to make real-time predictions on order status and readiness. By considering factors like current restaurant load, past order preparation times, and kitchen efficiency, you can provide customers with accurate wait-time estimates. And their order status complete based on different menu items the order will take different times.
Implement machine learning models that utilize historical order data to predict spikes in order volume. By analyzing patterns and current order influx, the model can provide real-time adjustments to wait times. This ensures optimal customer experience during peak hours. Integrating such AI-driven insights can significantly enhance order management efficiency on busy days.
Dynamic Display of "Hot" Items: Utilize restaurant and business screens to dynamically showcase "hot" or trending menu items, enhancing customer engagement and potentially driving up sales.
AI-Generated Images for New Items: In instances where a newly added menu item lacks a photograph, consider integrating with Google's Generate AI or platforms like Imagegen. This will allow for the automatic generation of realistic item images, ensuring the visual appeal of the menu remains consistent and captivating.
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