Answers to common questions about our services, process, and approach
We have expertise with clients across a wide range of industries, including financial services, banking, capital markets, asset management, wealth management, insurance, healthcare, retail, manufacturing, fintech, cloud, and technology providers. Our team has deep expertise in applying AI and Data solutions to industry-specific challenges.
We don't believe in one-size-fits-all solutions. Our process starts with a deep dive into your unique requirements, existing systems, and business objectives to discover and co-develop the appropriate AI and Data strategy. We then design and implement custom solutions, combining custom development with best-in-class platforms as needed, ensuring the outcome aligns perfectly with your goals.
The timeline for an AI and Data engagement varies based on the complexity of the project, your organization's readiness, and your specific requirements. A typical engagement can range from 2-6 months for initial value creation. During our initial consultation, we'll provide a more specific range.
Our initial consultation is a complimentary session where we discuss your business challenges, goals, and current AI and Data landscape. We aim to understand your needs and explore how AI and Data solutions could potentially provide value. It's an opportunity for mutual discovery with no obligation.
Pricing varies depending on the engagement type, scope, complexity, and duration. We typically work on an engagement-based fee structure, providing a clear cost estimate after the initial assessment phase, and tying fees to outcomes. We also offer retainer models for ongoing advisory or support needs. We focus on delivering clear ROI and value for your investment.
We support a wide range of AI/ML frameworks and tools including OpenAI, Anthropic, Google Vertex AI, Hugging Face, Amazon Bedrock, Azure ML, Amazon SageMaker, LangGraph, TensorFlow, PyTorch, and many more. We select the best technologies based on your specific requirements and existing infrastructure, ensuring optimal performance and integration.
We support all major cloud-native and on-premises Data platforms, including Databricks, Snowflake, Microsoft Fabric, Oracle, Amazon Redshift, Azure SQL, BigQuery, DuckDB, Azure Purview, PowerBI, Tableau, Looker, QuickSight, and many more. We select the best technologies based on your specific requirements and existing infrastructure, ensuring optimal performance and integration.
Our team has expertise across all major public cloud hyperscalers (AWS, Azure, GCP, OCI) and on-premises platforms. We help select the appropriate platform for your initiatives or work seamlessly with your existing infrastructure, based on your needs.
Yes, we provide comprehensive training programs to ensure your team can effectively use and maintain the AI and Data solutions we implement. We believe in knowledge transfer as a critical component of successfully working together.
We offer various post-engagement support options, ranging from standard warranty periods to ongoing maintenance and optimization retainers. Our goal is to ensure the long-term success and value of the solutions we deliver, and we can tailor a support plan to your specific needs.
Data security and confidentiality are fundamental to all our engagements. We employ robust administrative, technical, and physical security measures aligned with industry best practices (e.g., encryption, strict access controls, secure development lifecycle, data masking, etc.). We readily sign Non-Disclosure Agreements and can adapt our processes to comply with your specific security policies, regulatory requirements (e.g., GDPR, HIPAA, etc.), and data handling protocols, ensuring your sensitive information is protected throughout the engagement.
We are committed to developing and deploying AI solutions ethically and responsibly. Our approach integrates principles of fairness, transparency, accountability, privacy, and security throughout the AI lifecycle. We actively work with clients to identify and mitigate potential biases, ensure model traceability and explainability, and build systems that align with human values, regulatory guidelines, and societal norms.