Enabling Businesses to Execute on AI Goals
Whether you’re brand new to implementing AI or seeking acceleration of mature projects, we can help navigate a change in course or build momentum. As STEM scientists, we are motivated to identify complex objectives & build tools, deliver software, services, or new products to help meet them. SFL Scientific is a data science consulting firm that is closing the data science talent & deployment gap to help clients in their business, not just IT.
Leverage a wide range of technical resources on AI and accelerated computing to assess and prioritize the many applications of AI against your strategic objectives. As part of our mission to accelerate AI adoption, we offer clients, partners, and researchers custom workshops to focus on strategy, data, use cases, hardware, or deployment.
From hands-on online discussions to onsite executive briefings, our workshops are designed to engage technical and non-technical individuals across the business. Often sponsored by our enterprise partners, these sessions are a flexible way to gather ideas or kickoff initiatives.
Our hands-on approach to building AI systems spans industry, AI maturity, and organization size.
How We Can Help
Enabling a comprehensive, actionable roadmap that aligns data, business objectives, and other requirements. Our holistic process enhances the understanding of real-world requirements and identifying key-value.
Use Case Strategy
Identification and prioritization of AI use cases to support corporate strategy for operational improvement and development of new products, tools, or services.
Custom business or technical sessions to jump-start & deep-dive into short and long-term objectives. Designed to be broad or problem-specific, our workshops and labs serve as tools that help focus ideas and refine metrics.
Evaluating current constraints, platforms, IT, and data architecture, creating the technical requirements for integration and deployment of novel AI solutions.
Data & ML Validation
Examining the quality of data sets, structures, & metadata required to achieve business objectives and benchmarking existing analytics or AI solutions to recommend performance improvements.
From technical due diligence and competitive assessments to examining the ROI, governance, compliance, and team structures necessary for AI adoption.
Training machine learning models is a small part of the entire AI life cycle. SFL Scientific’s core services include leading cross-functional efforts across IT and operations to accelerate development by merging disparate technology solutions and managing the integration of open-source or licensed tools. We’re recognized by leaders in hardware, software, and cloud as an AI consulting partner that recommends the best solutions to partner, buy, or build for each initiative.
Leverage our Expert Team
Often overlooked is the interdisciplinary team needed to build machine learning solutions, with a lack of AI ownership and data science expertise cited as leading roadblocks for businesses worldwide. AI deployment success typically requires abilities in machine learning modeling, data pipeline development, back-end/front-end & MLOps, and technical project management, along with the leadership and IT ecosystem to support efforts.
We provide organizations the ability to execute by leveraging our expert team of technical strategists, data scientists, and data engineers to recognize and develop AI opportunities.
Data & AI Engineers
Bridge Business & Technical Reality
In working with hundreds of companies while building AI solutions, SFL Scientific has identified effective data strategies that enable organizations to connect the dots and execute on their AI objectives. Often we see a gap between expectations and the reality of implementation, whether technical or temporal. We establish AI development working groups to navigate the complexity of bringing together business understanding, data understanding, and what’s possible from an AI performance perspective to lower the barriers for entry and get answers quickly.
This allows us to:
- Develop a broad use case roadmap to define starting, middle, and end points for each solution
- Deep dive technically into objectives, data sets, structures, architecture, and best practices
- Evaluate the technical difficulty and change needed by the organization
- Determine how end users will adopt and trust the solution, whether patients, customers, professionals, or employees