Data is not only an asset but the cornerstone of modern business. At Craftware, we understand the challenges and opportunities of managing large volumes, velocities, and varieties of data, as well as leveraging AI to enhance data preparation, insights generation, and even actions based on them to improve business results. We recognize that with the rapid growth of AI capabilities, solid Data Engineering and savviness in leveraging ever-growing libraries of AI/ML components are the key enablers for companies to become data-driven and AI-enabled.
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We advise on and perform end-to-end migrations of traditional Data Warehouses and outdated big data systems to modern cloud data platforms. A typical scenario includes a migration of Oracle DW or on-premise Hadoop to the Databricks Data Ingelligence platform.
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We help clients deal with data silos by seamlessly integrating disjointed data from multiple data sources into a Corporate Data Lake. This allows the entire organization to access a unified and holistic view of the business and enables more powerful analytics and deeper insights, leading to improved business performance.
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With AI’s growing role, there is even more demand for reliable data engineering, as there is no space for an analyst or engineer to manually correct the data or code with each run. Keeping this in mind, we build robust batch and inference pipelines that empower Data Scientists and enhance AL/ML models’ performance.
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AI technologies, including algorithms and libraries, are evolving rapidly, unlocking new business use cases. We help clients tap into these new opportunities by integrating AI components into their data applications, such as LLM-powered chatbots and search engines, that allow employees to easily find and summarize knowledge hidden in the vast body of the company’s text documents, e.g., R&D or clinical research databases.
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We begin with a discovery effort to understand your objectives and needs. Often, a short workshop with key stakeholders is enough for us to propose a relevant solution.
Sometimes, a separate engagement, including a series of workshops with multiple stakeholders, run over 4-6 weeks, may be required to develop the strategy, target architecture, and implementation roadmap collaboratively.
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We believe in agile, iterative working methods. Thus, we would like to start small with a pilot implementation that helps validate and improve the initial solution as part of a full-scale implementation.
In cases where the proposed solution or approach requires validation, we are also happy to engage in Proof of Concept or Proof of Value work.
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Successful pilots are scaled and implemented as a production-quality service to ensure the business can consistently use and rely on them.
We are eager to manage the end-to-end implementations and are open to enhancing the client’s internal team or their other vendor, providing specific components of the larger solution or specific expertise.
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Successful implementations often uncover broader needs in the data, analytics, and AI space that call for a more systemic approach to addressing them.
In such cases, we help clients set up a team that will address those needs programmatically and proactively over the mid—to long-term rather than merely reacting to individual needs each time they emerge.
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Experience supporting Enterprise clients
Over 15 years of supporting enterprise clients across range of technology domains, including a portfolio of data, analytics and AI projects delivered for global leaders in various industries.
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Engineering focus & customer value
Robust engineering and craftsmanship is at the heart of Craftware’s culture and is reflected in the company name. Customer Value is at the center of everything we do.
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Big enough to deliver, small enough to care
With a team of over 500 engineers and consultants, we have sufficient scale to address even very demanding client needs while remaining small enough to ensure close touch with our clients.
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Best in class technologies
We partner with best-in-class technology providers like Databricks, whose Data Intelligence platform is the only one recognized by Gartner as a leader in both data warehousing and data management platforms categories.
1. Data silos and multiple versions of truth:
Many companies struggle with disjointed data scattered across multiple systems. They miss a unified view of the business that would enable effective decision making. Complex ETL and data management is required to integrate it and maintain quality.
2. Outdated data architecture:
legacy This can limit the value you deliver to your business in multiple ways. Typically it makes it difficult to access new libraries, native integrations, and efficient development tools, which increases development and maintenance costs. Additionally, it may impose scalability, performance, and AI/ML integration challenges, limiting the use-cases you can address.
3. Rapid growth of AI and genAI:
The business demand AI and genAI applications keeps growing, while talent experienced in AI integrations is scarce. AI applications also require high level of automation of data operations which additionally drives more focus on DevOps. Thus, many companies struggle to meet the demand for AI/ML and DevOps skills to drive the key AI initiatives.
4. Data & AI skills & capabilities gap:
Given the boom for AI and data capabilities, many business find it hard to meet the demand for data & AI talent coming from business. With data technology landscape evolving fast it is also difficult to develop the skills internally. And yet, business cannot wait – they need to progress on the strategic data and AI initiatives now.
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Legacy data platfrom migration to cloud
Challenge: Our client relied on an outdated Big Data platform built on Hadoop technology, hosted on their own on-premise infrastructure for data integration and analysis. This data architecture proved unsustainable in the long run.
Solution: Craftware developed and executed a detailed strategy to migrate the client’s data infrastructure to Microsoft Azure’s cloud platform, hosting Databricks as the core data platform.
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AI/ML Integrations
Challenge: A global healthcare industry leader required efficient AI/ML solutions to optimize R&D processes, including the analysis of unstructured data and validation within a stringent medical environment.
Solution: Leveraging a scalable Databricks-based data platform and the client’s existing cloud infrastructure, Craftware implemented integrated AI/ML solutions. The result is rapid knowledge extraction from vast databases, a foundation for further AI/GenAI innovations, and effective cost control.
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Data Integration into a Corporate Data Lake
Challenge: A global leader in consumer health products needed unified access to data from multiple sources to streamline business decision-making.
Solution: Craftware built a central data lake, powered by Databricks technology, which became the single authoritative source of truth for their data.
