Adviser is a patent-pending cloud computing platform that makes it easy for anyone to run computational, AI, & data science workloads in the cloud.
The rapid growth of AI, data science, and GPU-based workloads has resulted in a "data explosion," creating a widening gap between well-resourced organizations and the majority who lack GPU expertise. This disparity is particularly pronounced in the $16.5 billion simulation software market and the $600 billion cloud computing market, where prohibitive costs and limited expertise often hinder impactful R&D. Through NSF I-Corps and over 120 customer discovery interviews, we identified a critical need for an equitable solution that automates cloud resource selection, enabling organizations to leverage high-performance computing without technical hurdles.
Adviser addresses this challenge by providing a cloud platform that simplifies running computational tasks, eliminating the need for cloud expertise. Users can replace a typical command like “my_simulation” with “adviser run my_simulation” to seamlessly launch simulations in the cloud using large cluster resources. Our patent-pending UI, available through both CLI and GUI, ensures a frictionless experience with a learning curve close to zero. Adviser supports multi-node jobs & offers multi-cloud compatibility, integrating with AWS, Azure, and GCP. Powered by a proprietary AI model, Adviser automatically optimizes cloud resource configuration, enabling tasks to run up to 10x faster than competitors. By bridging the knowledge gap and reducing costs, Adviser empowers users to focus on core workloads, ensuring high-performance computing is accessible to all data scientists & developers.
Krish Wadhwani, CEO: Product experience at TikTok, Astranis Space, & Resilinc. Oxford full-ride scholarship. Deep understanding of cloud challenges across startups, SMBs, & enterprises.
Tim Ellis, CTO: Formerly built cloud infrastructure platforms for Confluent, Stability AI, & Apple. 20+ YOE in the valley.
David Hyde, Chief Scientist: Secured over $800K in grants for Adviser, led the initial technical development of the prototype, & previously built key cloud platforms for Confluent, QC Ware
Jiashu Huang, Founding Engineer: Vanderbilt BS in Computer Science & won Vanderbilt’s Immersion Showcase for cloud computing
Ravindra Duddu, Advisor: Generated $2M in extramural research funding at Vanderbilt and deeply connected with research groups in the US & India requiring efficient cloud computing solutions
With over a decade of experience building cloud infrastructure for industry giants, the team is uniquely positioned to deliver an accessible, automated cloud solution tailored to the needs of enterprises and research institutions alike.
Through NSF I-Corps and 120+ customer interviews, we identified key ecosystem players, including IT directors at enterprises and principal investigators at universities. Our target customers are (1) commercial R&D and consulting firms using containerized/commercial software (e.g., trading, energy, biotech, materials, aerospace) and (2) academic labs developing in-house scientific codes (e.g., C++, Python). Educators can also use Adviser to bring supercomputing into classrooms. A common pain point across these groups is the high cost and complexity of cloud resource management. For instance, we’re currently finalizing a paid pilot with a Chicago hedge fund’s volume statistical arbitrage team to reduce cloud resource allocation time and IT/devops burden.
Our go-to-market strategy focuses on compute-intensive enterprises & research groups via direct outreach and industry events, eventually supported by digital marketing. We’ve presented at AGU 2024, IMECE 2024, 3686 by LaunchTN, and soon AWS re:Invent. We’re also launching Adviser publicly, allowing data scientists to download for free and use Adviser immediately. By going direct to developers, we aim to build awareness organically—our ideal users work at reputable organizations and will share Adviser internally, leading to enterprise adoption & interest. We want the developer to be an internal champion of our tool.
Additionally, we’re exploring cloud provider partnerships to direct optimized traffic to AWS, Azure, and GCP, potentially earning a share of revenue from customer cloud usage. Listing on cloud marketplaces would further solidify Adviser’s role in improving cloud efficiency, benefiting both users and providers by reducing costs and increasing accessibility.
Adviser has a Developer tier and Enterprise tier. The revenue model for the Developer tier is a simple usage-based billing model where we charge customers a fixed margin on top of whatever underlying cloud computing infrastructure they use each month. Given the discounts we can negotiate with cloud providers, we believe we can maintain this margin without the customer noticing any increased costs compared to directly using a cloud provider - and ideally making it even cheaper for our end users (lowering their cloud TCO). Upon launch, we’re targeting a 20% margin for the Developer tier (to drive mass adoption), but as discussed above, we believe that without much effort (primarily relying on standard negotiations with cloud providers), we’ll be able to increase this to an 80% margin over the next several years. We note that given the cloud credits we’ve already received or are currently applying for, we expect that around our first $1M of ARR will be at 100% margin (all cloud computing expenses covered by credits).
For our enterprise tier, we are currently evaluating usage-based billing at a higher margin (to reflect the added features provided), or a commitment-based model that involves a flat fee per year for a maximum amount of cloud spend via Adviser. Enterprise customers can also purchase our professional service packages, e.g., 24/7 support with one-hour response times for $25k/yr. These prices will be calibrated as enterprises begin to adopt the platform.
We are seeking to raise ~$750K in pre-seed funding to accelerate development and scale operations.
This funding will enable hiring a full-stack engineer & a B2B salesperson to drive customer acquisition and secure paid enterprise pilots. Key hires will allow us to iterate more quickly based on user feedback, pursue additional pilots, and achieve product-market fit faster.
We anticipate investing additional funding to further optimize our proprietary AI model. Our ultimate goal is to scale rapidly through strategic hires, product enhancements, and customer acquisition, positioning Adviser for a successful seed & Series A round.
Adviser incorporates two key technical innovations. The first is a user-friendly, patent-pending interface that enables seamless execution of large-scale computational workloads in the cloud, mimicking the simplicity of running tasks on a local workstation. Users can execute commands like “adviser run python train.py” to launch large-scale computations in the cloud without needing to understand the complexities of different cloud providers (AWS, Azure, GCP), instance types, or infrastructure management. Additionally, users are not required to manually transfer code or data between their local systems and the cloud.
The second innovation is a proprietary AI model. This model automatically selects the optimal cloud provider and instance type for each computational job. By analyzing static and dynamic characteristics of the submitted code, combined with metadata from previous jobs, the AI model predicts the most efficient configuration—balancing CPU and GPU utilization for optimal performance while minimizing costs. While Adviser offers advanced users the flexibility to explicitly specify cloud options, our AI-driven automation is particularly beneficial for those without extensive cloud or high-performance computing (HPC) expertise, saving them both time and money. Together, these innovations position Adviser as a transformative tool for simplifying and optimizing cloud-based computational tasks.
Research
Vanderbilt University - Seeding Success Grant - “Advanced cloud-based Data- and Visualization-Integrated Simulation EnviRonment (ADVISER) for Ice Flow and Fracture Modeling” - $120,000
Vanderbilt University - Scaling Success Grant - “ADVISER-AI: Intelligent Optimization of Cloud Computing Resource Usage” - $60,000
National Science Foundation - Geosciences Open Science Ecosystem Track 1 Grant - “Collaborative Research: GEO OSE Track 1: Advanced cloud-based Data- and Visualization-Integrated Simulation EnviRonment (ADVISER) to Advance Computational Glaciology” - $400,000 (approx)
National Science Foundation - Innovation Corps Grant - “I-Corps: Translation Potential of a Cloud Platform for Scientific Software and Data” - $50,000
1/3rd of the following grant will go towards Adviser objectives: National Science Foundation - CAREER Award - “CAREER: Cyberinfrastructure for the Computation and Collaboration Needs of Emerging Technologies Research” - $550,000
Company
LeapYear Fund I - Pre-Seed Investment - $40,000
