AI Implementation and Pilot Programs 

Successful pilots focus on proving value, not on testing technology. Our team will: 

• Define pilot objectives, success criteria, and quantitative thresholds — intent resolution, task adherence, tool-call accuracy, groundedness, and the underlying business KPIs — before a single line of code is written 

• Scope pilots deliberately to fit a four-to-twelve-week window, with a single well-defined user cohort and a clear production exit path 

• Select the right architecture for the problem: Microsoft 365 Copilot extension, Copilot Studio low-code agent, Foundry prompt agent, Foundry workflow agent, or a code-based hosted agent built on Microsoft Agent Framework or LangGraph. The wrong choice here sinks more pilots than any model selection. 

Pilots stall when they depend on hypothetical future capacity. We build them ourselves, end-to-end: 

• Stand up Azure AI Search indexes, Foundry IQ knowledge bases, and the RAG or agent patterns required for the pilot, grounded in the data foundation work from our AI Data Management service 

• Implement custom agents using Microsoft Agent Framework, LangGraph, or the no-code Foundry prompt agent, with Model Context Protocol (MCP) tool integrations for enterprise systems 

• Apply production-grade patterns from day one: Entra Agent IDs, Managed Identity, private networking, Azure Key Vault, and Azure AI Content Safety guardrails, so the pilot does not have to be re-architected before it ships 

The teams that escape pilot purgatory build their evaluation infrastructure before they scale, not after. Our experts will: 

• Build evaluation pipelines using the Azure AI Evaluation SDK and Foundry's built-in evaluators (coherence, relevance, groundedness, retrieval quality, safety, task adherence, tool-call accuracy) against held-out datasets that reflect real user behaviour 

• Instrument OpenTelemetry-based tracing, Application Insights dashboards, and Foundry Control Plane observability so every agent decision is traceable and every regression is detectable 

• Run AI Red Teaming Agent simulations and set up continuous production monitoring with quality, safety, and cost thresholds before the pilot is exposed to real users 

Pilot success creates momentum only if it is converted into production investment. We help: 

• Harden the pilot for production: VNet integration, Entra-based identity, DLP policies, content filters, FinOps cost controls, and SLA-aligned runtime targets 

• Document reusable patterns, prompt libraries, custom evaluators, and architectural decisions so the next pilot in your organization starts at 60% completion instead of zero 

• Build internal team capability through pair-programming, architecture reviews, and hands-on knowledge transfer, so your organization owns the result rather than rents it from us