Businesses face many challenges when attempting to squeeze out every last bit of potential from their artificial intelligence (AI) products and services.
Whether they're developing innovative concepts or scaling fully operational AI solutions, the journey is filled with complexity.
Today, most companies seek custom AI solutions that address their unique needs across the AI lifecycle, ensuring seamless integration, robust performance, and tangible business outcomes.
They look for an AI solutions partner who understands their industry-specific challenges and offers the expertise and agility to drive their AI projects to success.
Why custom AI solutions matter
Businesses across various sectors increasingly recognize AI's transformative potential. However, each industry presents its own set of challenges. For example:
- Agriculture companies must implement precision farming solutions to minimize input costs and reduce unnecessary chemical exposure to the crop.
- Engineering companies inspect infrastructure assets to prioritize and optimize maintenance decisions and actions for existing assets.
- Financial services firms review and capture relevant information from contracts and forms to process applications for various financial services.
- Logistics companies aim to automate planning and navigating delivery routes and final deliveries with unmanned vehicles.
When choosing AI solutions, businesses across industries need to look for proven expertise, the ability to customize solutions based on needs, and tangible business results that are positive and trustworthy.
- Customization and flexibility: The ability to tailor solutions to specific business needs and constraints.
- Production readiness: A focus on quick, efficient model deployment, avoiding prolonged R&D phases.
- Proven expertise: A partner with a track record of successful AI projects across different industries.
- Outcome-based approach: A commitment to aligning AI solutions with the intended business outcomes, ensuring a clear return on investment.
A reliable ML solutions partner delivers results
CloudFactory’s extensive experience across hundreds of projects has provided us with valuable insights and best practices. Here are a few examples of how we have helped clients overcome their AI challenges:
Agriculture
Multiple leading precision agriculture firms partner with us to improve their ability to detect specific weeds, pests, and other crop-relevant details. By bringing in industry experts to the core team, we trained our workforce to do this accurately in multiple geographies.
Engineering
Multiple infrastructure engineering companies worked with us to identify and classify cracks, rust, and other signs of wear and tear in distributed assets. As most foundational models are trained on objects instead of details in those objects like cracks, we worked with these companies to train AI to identify these significant details in the assets.
Financial
We partner with many finance companies that face the challenge of extracting information from receipts, invoices, contracts, web pages, and other documents. The challenge lies in capturing information from fields like client numbers, dates, signatures, legal parties, etc. Using modern breakthroughs in intelligent document processing, we were able to help these companies extract this information with high levels of accuracy and automation.
Logistics
We worked with an unmanned drone delivery company to help navigate flight paths, avoid obstacles such as trees, power lines, and towers, and identify relevant drop zones so that packages could be delivered safely, efficiently, and on time.
Proven methodology from initial concept to production
At CloudFactory, we've developed a proven methodology to help you speed up your AI development journey from innovative concepts to fully operational solutions.
CloudFactory's proven methodology takes your AI project from initial concept to a scalable production-ready solution.
Here’s how we can support you:
Problem discovery
We start by working closely with you to workshop the problem, outline the intended business outcomes, and define the process to get there.
- Align on problems: Collaborating with you to identify critical challenges and set clear goals.
- Identify constraints: Understanding data privacy, data security, and regulatory requirements to ensure data compliance.
- Define success: Setting success metrics that align with your business needs.
- Define stages: Outlining the stages for development and ensuring a smooth transition from pilot to full-scale deployment.
Proof of value
Next, we build the initial dataset and models to validate our assumptions and demonstrate the likelihood of success.
- Mine dataset: Extracting valuable data to build a robust AI model.
- Define V1 ontology: Creating the initial structure for categorizing data.
- Label initial data: Ensuring high-quality data inputs for training.
- Train initial models: Developing and testing models to demonstrate early promise.
- Test on sample data: Validating the model's potential with sample data.
MVP (Minimum Viable Product)
We then implement a lean version of the solution to market-test the problem and show problem-solution fit.
- Refine ontology: Enhancing the model’s structure for better performance.
- Build initial data asset: Creating a comprehensive data asset for training.
- Refine models: Improving models to meet specific business needs.
- Build ML pipeline: Establishing a seamless pipeline for AI deployment.
- Test on production data: Demonstrating the solution’s effectiveness with actual data.
- Establish a Center of Excellence (CoE): Driving continuous AI innovation across your business units.
Production
Finally, we scale out a production-ready solution with inference monitoring and active learning.
- Build out data assets: Developing a comprehensive data asset to support the solution.
- Set up ML infrastructure: Establishing the necessary infrastructure for AI deployment.
- Curate data asset: Ensuring the data asset is accurate and relevant.
- Monitor inferences: Continuously tracking model performance and making real-time adjustments.
- Build feedback loops: Implementing feedback loops to improve the model continuously.
- Scale solution: Expanding the solution to meet growing business needs.
Speed up your journey to fully operational AI solutions
To deliver the bespoke solutions companies need, we bring together expert AI solutions consultants, a dedicated project delivery team representing the human aspect of our platform, and a cutting-edge product team as the technical arm—all under one roof.
This unique combination is essential for providing trustworthy and reliable solutions. Our synergy enables seamless transitions from R&D to production, driven by our production-first approach and inference-centric philosophy. Ultimately, it’s about delivering the desired experience efficiently and effectively.
CloudFactory is committed to accelerating your journey from innovative concepts to fully operational AI solutions. By leveraging our proven methodology and extensive experience, we help you achieve your AI goals faster and more effectively. Trust us to be your reliable ML solutions partner and transform your AI vision into reality.
