AI and cloud solutions built for real business work.

Identify valuable AI opportunities and establish the technical, data, security, and operating foundation needed to deliver them responsibly.

What This Includes

• Use Cases & Prioritization. Evaluate business problems, users, expected value, implementation complexity, data dependencies, risk, and measurable success criteria.
• Data & Permission Readiness. Review knowledge sources, content quality, permissions, oversharing, ownership, retention, and grounding requirements.
• Governance & Operating Model. Define ownership, approved platforms, development standards, testing, human oversight, lifecycle management, support, and adoption.

A successful AI initiative begins with a defined problem, trusted information, accountable ownership, and a measurable result.

Build, orchestrate, evaluate, deploy, and operate custom AI applications and agents using Microsoft Foundry.

What This Includes

• Models & Agent Architecture. Model selection, prompt and instruction design, Foundry Agent Service, tool integration, conversation state, and multi-agent patterns where justified.
• Tools, Actions & Integration. Connect agents to APIs, applications, databases, search services, business processes, and approved enterprise tools.
• Evaluation & Production Operations. Automated evaluations, quality testing, safety controls, tracing, observability, cost monitoring, deployment architecture, and lifecycle management.

Production AI requires evaluation, observability, security, and operational ownership, not only a working demonstration.

Create agents that work inside Microsoft 365, Teams, SharePoint, business applications, and customer-facing experiences.

What This Includes

• Agent Experience & Channel Design. Determine when to use Microsoft 365 Copilot, Teams, SharePoint, a website experience, or another application channel.
• Knowledge, Topics & Actions. Configure instructions, generative answers, approved knowledge, tools, connectors, actions, escalation paths, and deterministic conversation logic when needed.
• Environment & Lifecycle Governance. Power Platform environments, solutions, deployment pipelines, DLP policies, connector controls, sharing, licensing, analytics, ownership, and support.

The right agent platform depends on the users, channel, knowledge, actions, security boundary, and support model. Nora, SkyNorth's own AI agent, shows how a governed agent can combine approved knowledge, clear instructions, useful tools, and a branded customer experience.

Create intelligent search and grounded AI experiences across documents, websites, databases, and enterprise knowledge.

What This Includes

• Search & Index Architecture. Azure AI Search indexes, document processing, chunking, metadata, filters, permissions, vectorization, and content refresh patterns.
• Retrieval & Grounding. Hybrid search, vector search, semantic ranking, agentic retrieval, classic RAG, citations, relevance tuning, and retrieval evaluation.
• Knowledge Governance. Source ownership, content lifecycle, permission trimming, stale-content handling, search analytics, and answer-quality monitoring.

Poor retrieval is often caused by weak content structure, permissions, metadata, or chunking rather than the language model.

Connect and prepare the information required by applications, analytics, search, agents, and business processes.

What This Includes

• Data Pipelines. Azure Data Factory pipelines, ETL and ELT, scheduling, orchestration, incremental loads, dependency handling, and hybrid connectivity.
• Transformation & Quality. Schema mapping, cleansing, enrichment, validation, error handling, reconciliation, lineage, and data-quality monitoring.
• Integration Architecture. Azure SQL, storage, APIs, event-driven integration, on-premises systems, Microsoft Fabric, and secure service-to-service access.

AI-ready data must be current, explainable, governed, and reliable enough to support the decisions built on top of it.

Modernize legacy applications and build secure, cloud-native services that can support data, automation, and AI.

What This Includes

• Application Modernization. .NET modernization, Azure App Service, Azure Functions, containers, APIs, background services, and cloud-native design patterns.
• Security & Connectivity. Managed identities, Azure Key Vault, private endpoints, network segmentation, RBAC, Azure Policy, Defender for Cloud, and removal of embedded credentials.
• Delivery & Operations. CI/CD, infrastructure as code, environment separation, monitoring, Application Insights, logging, resilience, performance, and cost management.

Modernization should reduce operational friction and technical debt, not simply relocate an old application to Azure.