Data & AI
Turn data and AI into practical capabilities that improve decisions, workflows, software delivery, and customer experiences.
XCentium helps organizations build the data, technology, and operating foundations needed to put AI to work across the enterprise. We connect data and AI strategy, data platforms, AI-enabled software delivery, agentic workflows, applied AI, enterprise knowledge, and cloud around real business requirements and measurable outcomes.
Build the foundations that make data and AI useful
Organizations have more data, AI tools, and technology choices than ever, but access to technology does not automatically create value.
Data may be fragmented across systems. Existing platforms may be difficult to scale. AI pilots may not connect to real workflows. Employees may struggle to access trusted enterprise knowledge. Development teams may be experimenting with AI without a consistent operating model.
We help organizations connect these pieces so data can support better decisions, AI can operate with the right business context, and new capabilities can move beyond isolated experiments into everyday operations.
Our data & AI services
Data & AI strategy
Identify where data and AI can create meaningful business value and what needs to be in place to support it. We help assess current capabilities, prioritize use cases, evaluate readiness, establish governance, and build a practical roadmap.
Data platforms
Create a stronger foundation for analytics, reporting, automation, and AI. We help organizations design, integrate, migrate, and optimize data platforms across architecture, engineering, warehousing, modeling, governance, and analytics.
AI SDLC
Apply AI across the software development lifecycle (SDLC) to improve how teams plan, build, test, document, and deliver software through repeatable workflows with appropriate quality, security, governance, and human oversight.
Agentic workflows
Connect AI agents with enterprise data, applications, tools, and workflows so they can help complete meaningful work within defined objectives, permissions, approvals, escalation paths, and monitoring.
Applied AI
Design and implement AI-enabled applications, experiences, integrations, assistants, and other solutions around specific business requirements, existing systems, trusted data, and measurable outcomes.
Enterprise knowledge
Make trusted organizational knowledge easier for employees, customers, and AI systems to find and use by connecting content, data, retrieval, permissions, governance, and AI.
Cloud
Create scalable cloud environments that support applications, data, analytics, automation, and AI across architecture, migration, DevOps, FinOps, integration, performance, reliability, security, and governance.
When data and AI are difficult to put to work
We help determine what needs to change across data, architecture, technology, workflows, governance, and operating practices so data and AI initiatives can move forward with a stronger foundation. Organizations often come to us when they are dealing with:
Data spread across disconnected systems and platforms
Limited trust in data used for reporting or AI
Legacy data environments that are difficult to change or scale
AI experiments that have not moved into production
Unclear AI use cases or investment priorities
Manual and repetitive business or technology workflows
Employees struggling to find trusted enterprise information
Development teams using AI inconsistently
Governance, security, and operational concerns slowing adoption
AI needs more than a model
Selecting an AI model or deploying a new tool is only one part of putting AI to work.
The quality of the data matters. The systems it needs to access matter. The workflow around it matters. Permissions, security, governance, monitoring, and human oversight matter. Most importantly, the use case needs to solve a real problem.
We start with the work that needs to improve, then determine what combination of data, AI, automation, integration, and platform capabilities can support it. That keeps the focus on operational value rather than AI for its own sake.
CASE STUDY
Funko
We partnered with Funko to implement Salesforce Service Cloud and Agentforce for more connected customer support.
Contextual AI responses
Connected Agentforce with knowledge and commerce data.
Automated common requests
Supported order-status inquiries and eligible rewards enrollment.
Agent handoffs
Enabled authentication, case creation, and live-agent transfers.
Service visibility
Added service-level tracking and customer feedback measurement.
Related assessments & offers
AI use case workshop
Identify practical AI opportunities aligned to your business goals, workflows, and operational priorities.
Data readiness assessment
Evaluate the quality, structure, and accessibility of your data to support analytics and AI initiatives.
AI-powered SDLC acceleration workshop
Improve software delivery efficiency across development, QA, testing, and operational workflows using AI.
Cloud modernization assessment
Improve scalability, performance, and operational efficiency across your cloud environment and infrastructure.
Frequently asked questions
Data and AI services help organizations make enterprise data more usable and apply artificial intelligence to real business and technology needs. They can include data strategy, data architecture, data platforms, AI-enabled software delivery, agents, enterprise knowledge, cloud, governance, and implementation.
XCentium provides data and AI services across strategy, data platforms, AI-enabled software development, agentic workflows, applied AI, enterprise knowledge, and cloud. We help organizations move from strategy and readiness through implementation, integration, governance, and ongoing optimization.
Start with a business problem or workflow rather than a model or tool. XCentium helps identify potential use cases, assess business value and feasibility, understand data and integration requirements, evaluate risks, and prioritize opportunities with a realistic path to implementation.
AI systems depend on the information available to them. Poor-quality, fragmented, inaccessible, or poorly governed data can limit the usefulness of AI even when the underlying model is capable. Data readiness includes data sources, quality, structure, ownership, accessibility, permissions, and intended use.
Traditional automation generally follows predefined rules or workflows. AI agents can interpret a goal, use available context, select from permitted tools or actions, and complete parts of a multi-step task. Agentic systems still require defined boundaries, permissions, monitoring, escalation, and governance.
Yes. Data platform work can involve improving architecture, integrations, governance, performance, engineering, or selected workloads rather than replacing an entire environment. The right approach depends on current investments, business requirements, technical constraints, and future analytics and AI needs.
XCentium works with Microsoft technologies including Azure, Fabric, Power BI, Foundry, and Copilot; Databricks; Snowflake; Salesforce Data 360 and Agentforce; OpenAI; Anthropic; and AWS. These platforms support data engineering, analytics, AI applications, agents, and cloud infrastructure. We select the capabilities that fit the use case, existing systems, data, and operating requirements.
XCentium incorporates governance into AI strategy and implementation through areas such as access controls, approved data and tools, security requirements, human oversight, monitoring, evaluation, escalation, and operating standards. The level of governance should reflect what the AI system knows, what it can do, and the potential impact of its actions.
Put your data and AI to work
Whether you are strengthening the data foundation, evaluating AI opportunities, building agents, improving software delivery, or moving AI into production, we can help determine the right path and put it into practice.