Our capabilities
AI AI is required to build algorithms that can match or exceed human performance, and hence deliver better results. It is important to make algorithms explainable and fair, without human biases. There are six pillars on which we organize our AI capabilities–Algorithmic decision-making, machine vision, conversational AI, AI Engineering, ML Ops and GenAI. Algorithmic decision making / Core ML The process and analysis of large amounts of data produces an output such as a score, choice, or probability that can be leveraged for making AI-enabled business decisions. Machine vision The technology and methods for extraction of relevant information from visual data on an automated basis and using this information to make decisions. Our AI specialists have the capabilities to build systems to enable machines to “see” as well as for humans to understand visual data using a variety of data sources and learning algorithms. Conversational AI (NLP) Technologies that enable machines such as virtual assistants to process, understand, and respond to human languages using ML, and natural language processing. ML Ops A framework comprising of end-to-end model life-cycle management which aims to build reproducible, testable & evolvable ML models. Scaling AI solutions require model training, monitoring, deployment supported by continuously integrated pipelines, model lifecycle management and model governance. Engineering Engineering is key to not only identifying the availability, quality, type and readiness of data to build an effective AI solution, but to also build efficient data architecture that facilitates real-time automated AI solutions that are designed for scale. We leverage our engineering capabilities to operationalize the data-to-decisions process for our clients. AI Engineering 1. Data infrastructure to store, manage and harness large amounts of data effectively, 2. Compute infrastructure to develop and operationalize AI solutions, and
Fractal brings together AI, engineering, and design along with deep domain expertise to enable data-driven decision-making for enterprises.
LLM Ops The methods, strategies, and tools used to manage large language models in real-world settings. While these models are easy to use during initial testing, integrating them into commercial products poses challenges. The process involves several complex stages such as handling data, refining prompts, adjusting models, deploying them, and monitoring their performance. It also requires teamwork across different departments, from data management to machine learning engineering. GenAI The algorithms that create new content, such as images, text, or music, mimicking human creativity. Utilizing techniques like neural networks, it learns patterns from data and generates novel outputs. Foundation models act as the powerful backbone for many GenAI applications. These pre-trained models, with vast troves of knowledge, provide a strong base for tasks like text generation, image creation, and code production in GenAI. We help our clients by infusing GenAI in our existing AI solutions thereby adding new intelligence to the same, and by building GenAI led solutions from scratch. These AI solutions help in the areas of:
Approach We have experienced that problem-solving at scale to drive results requires integrating AI, engineering, and design, along with domain expertise, to deliver end-to-end AI solutions, which we call the “Fractal Approach.”
AI AI algorithms match or exceed human performance in various cognitive tasks, delivering better results.
1. Increasing productivity
2. Adding a foundational layer of intelligence, and
3. Improving user experience
To build these GenAI solutions, we leverage a variety of open source and closed source GenAI foundation models.
Engineering Seamlessly connect data pipelines to automate decisions in real-time, and at scale.
3. Technology infrastructure to support data and AI requirements in an efficient, scalable and cost-effective manner. The systems are automated with minimal requirement of human intervention. This capability allows our clients to use AI across the organization at scale. Cloud engineering and migration We design, build and deploy AI solutions at scale on public cloud platforms. Our cloud engineering capabilities include architecture consulting, data estate modernization, data governance, security and cloud cost optimization for public cloud platforms such as AWS, Microsoft Azure and Google Cloud. We believe that these cloud-driven solutions enable clients to have agile analytics, reduce down-time, scale applications, and provide out-of-box tools for driving data backed insights for decision-making.
Design Solve the right problem through deep understanding of human behavior.
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Fractal Analytics Limited | Annual Report 2023-24
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