Many teams have data, reports, documents, and systems, but still struggle to get timely answers or reliable visibility. Information may be spread across tools, reporting may depend on manual preparation, and repeated questions may slow down teams that need faster support.
We help structure operational data, reporting, and AI-enabled support around the workflow. That may include Power BI dashboards, analytics, Copilot Studio agents, knowledge-based assistants, or workflow-integrated support designed around the decision or process the team needs to improve.
We clarify what question, process, or decision the solution needs to support.
We identify who needs access, what each role should see, and where controls or boundaries are needed.
We review where the information lives, how reliable it is, and what needs to be connected or structured.
We define how the solution will be used, maintained, improved, and governed over time.
Before creating a dashboard, analytics model, AI assistant, or agent experience, we clarify what the team needs to understand or accomplish. We review the workflow, available data, knowledge sources, user groups, risks, permissions, and adoption needs so the solution supports real operational work.
The value is not only in presenting information. It is in helping teams use data, knowledge, and workflow context in a way that supports better decisions, less manual effort, and clearer operational control.
Design reports and dashboards that help teams track status, performance, risks, workload, and operational indicators with clearer visibility.
Structure data and metrics around the questions leaders and teams need to answer, from throughput and backlog to quality, exceptions, and process performance.
Build operational assistants that answer questions, guide users, retrieve knowledge, or support defined workflows through a conversational experience.
Use AI where it can support intake, classification, summarization, routing, knowledge retrieval, or decision support within a defined process.
Design data structures that support clean relationships, scalable workflows, reporting, automation, and governed access across the Power Platform.
Define usage boundaries, access considerations, documentation, monitoring, and improvement practices so the solution can be adopted responsibly.
Most of our AI and analytics work is designed within the Microsoft ecosystem because it supports governed workflows, reporting, automation, and adoption across tools many teams already use. The specific architecture depends on the workflow, data sources, permissions, and operating needs.
Power BI is the primary platform we use for business intelligence, reporting, and operational dashboards. It helps teams connect and visualize data, create reports, and share insights across Microsoft services and everyday workflows. Microsoft describes Power BI as a business analytics platform for turning data into actionable insights, and as a core Microsoft Fabric workload for analytics and visualization.
Copilot Studio is the primary platform we use for AI assistants and agent-style experiences when the goal is to support operational users through conversation, knowledge retrieval, or workflow actions. Microsoft documentation describes Power Platform connectors as tools that can be used in Copilot Studio agents to connect to services, retrieve data, and perform actions.
When a solution needs to move work, trigger approvals, send notifications, update records, or connect steps across systems, workflow automation may be part of the design. This often works alongside Power BI, Dataverse, Copilot Studio, or other operational tools.
When the workflow requires cleaner data relationships, governed access, role-based structure, or a foundation for apps, automations, reporting, and agents, Dataverse may be part of the solution. Your internal service materials already identify Dataverse architecture and data modeling as part of AI Enablement & Analytics, including scalable automation, clean data relationships, and Power Platform integration.
We do not lead with a model decision. We first clarify the use case, data, permissions, cost, security, and adoption requirements. If model selection matters, it is addressed during architecture and scoping rather than used as the public-facing value proposition. Your internal conversational analytics report evaluates multiple patterns, including Copilot Studio, Power BI/Fabric-backed approaches, and Claude integration options, which supports keeping model selection as an architecture decision rather than a marketing headline.
The platform should serve the workflow. We choose the architecture based on the decision, data, risk, governance, and adoption requirements, not because a tool is available.
AI and analytics only create value when they are connected to a real workflow, a clear decision, and reliable information. A dashboard that no one trusts, or an assistant that is not grounded in the right knowledge, can add more noise than clarity.
Lab Cortex starts by understanding the process, data, risk, users, and decision context. From there, we design dashboards, analytics, assistants, or AI-enabled workflows that are practical, governed, and easier for teams to adopt.
This work can be useful when teams have information but still struggle to find answers, monitor performance, understand workload, or respond to repeated operational questions.
The goal is not to add another dashboard or assistant. The goal is to make information easier to use, decisions easier to support, and workflows easier to manage.
When leaders or teams cannot easily see what is happening, where work is blocked, or what needs attention, analytics may need stronger structure.
When teams repeatedly ask the same questions, search across documents, or rely on key people for answers, an operational assistant may help reduce friction.
Clarify, redesign, and stabilize critical workflows so teams can reduce friction, improve execution, and operate with better visibility.
Build practical apps, automations, integrations, and workflows that reduce manual effort and make operational work easier to manage.
Design workflows and digital solutions with traceability, controls, audit readiness, and accountability built in from the start.
