Azure enterprises building governed AI applications
Use existing identity, networking, security, data, monitoring, and cloud operations around models and agents.
Enterprise AI applications, model catalogue, agents, tools, tracing, monitoring, evaluations, Azure identity, and governance
Microsoft Foundry development services
Rokad develops enterprise AI applications with Microsoft Foundry across models, agents, tools, evaluations, tracing, monitoring, security, data, and Azure integration.
Platform fit / 01
Designed for teams with a specific platform requirement.
Microsoft Foundry provides a unified Azure environment for enterprise AI application development, models, agents, tools, tracing, monitoring, and evaluations. Rokad designs the surrounding application, identity, network, data, retrieval, tool, governance, deployment, evaluation, and operating architecture.
Teams comparing and operating several models
Evaluate model catalogue options, route workloads, track versions, and control quality, latency, cost, and data requirements.
Organisations deploying enterprise agents
Connect agents to tools and data with identities, permissions, evaluation, tracing, approval, and audit.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
AI projects bypass established Azure governance
Projects, subscriptions, identity, network, data, regions, secrets, logging, cost, and support are created outside normal controls.
Model choice is not tied to task evidence
Teams select a model from reputation or demos without representative evaluation, routing, fallback, or lifecycle planning.
Agent tools cross enterprise trust boundaries
Business applications, databases, files, actions, and credentials are exposed without scoped identity and policy.
Platform capabilities / 03
What Rokad can implement and operate.
01
Microsoft Foundry architecture, project, model, agent, data, network, security, and operational assessment
02
Model catalogue evaluation, deployment, routing, versioning, quotas, latency, cost, and fallback
03
Foundry Agent Service, tools, knowledge, state, actions, approvals, and application integration
04
Prompt, flow, retrieval, search, data, files, Azure services, and enterprise knowledge integration
05
Azure identity, managed identities, network, private access, Key Vault, policy, logging, and compliance controls
06
Tracing, monitoring, evaluations, safety, red-teaming support, dashboards, and release gates
07
Application deployment, CI/CD, environments, observability, support, governance, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
Azure AI foundation
Subscriptions, projects, identity, network, regions, secrets, storage, data, APIs, policy, logging, and cost.
Model and agent layer
Catalogue, deployments, prompts, agents, tools, knowledge, state, routing, quotas, versions, and fallback.
Enterprise integration
Azure services, search, data, applications, APIs, files, identities, approvals, audit, and business workflows.
AI operations
Tracing, monitoring, evaluations, safety, incidents, latency, cost, quality, releases, governance, and support.
Use cases / 05
Where this platform creates practical leverage.
Enterprise knowledge agent
Connect governed organisational information, search, files, identity, citations, tools, and escalation on Azure.
AI application platform
Provide reusable model, prompt, agent, evaluation, tracing, deployment, security, and integration capabilities to teams.
Business workflow agent
Analyse requests, retrieve evidence, use authorised tools, request approval, update systems, and preserve audit records.
Model evaluation and routing
Compare models against task datasets and route workloads by quality, latency, cost, region, and control requirements.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Foundry projects align with ownership boundaries
Separate environments, products, data, identities, budgets, teams, and risk while maintaining shared standards and observability.
Managed identity replaces embedded secrets
Use scoped Azure identities and service connections for data, tools, storage, search, deployment, and monitoring where supported.
Evaluations govern model and agent release
Measure task quality, safety, groundedness, tool trajectories, latency, cost, and regressions before promotion.
Quality and governance / 07
Production controls are part of the implementation.
Evaluated behaviour
Representative datasets, task criteria, failure modes, model comparisons, and release thresholds are defined before production expansion.
Governed model access
Identity, data boundaries, tool permissions, moderation, approvals, audit, retention, and provider controls match the use case.
Provider-aware operation
Models, prompts, tools, latency, cost, quotas, versions, fallbacks, telemetry, and migration risk are monitored explicitly.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
Microsoft Foundry model, agent, data, Azure, security, governance, and risk assessment
Project, identity, network, model, agent, tool, evaluation, and operating architecture
Production Foundry applications, agent services, model integrations, and enterprise workflows
Search, retrieval, data, Azure service, tool, approval, identity, and audit integrations
Tracing, monitoring, evaluation, safety, cost, deployment, and release controls
Developer, cloud, security, operator, governance, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Amazon Bedrock
AWS-managed foundation models, agents, knowledge bases, guardrails, and cloud integration.
02
Google Gemini
Gemini multimodal applications and Vertex AI enterprise deployment.
03
OpenAI
Direct API integration for agents, tools, retrieval, structured outputs, and multimodal products.
01
AI development
AI applications, agents, retrieval, evaluation, model integration, and intelligent workflows.
02
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
03
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
FAQ
Microsoft Foundry development services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01What is Microsoft Foundry used for?
What is Microsoft Foundry used for?
It provides Azure services for developing and operating enterprise AI applications with models, agents, tools, tracing, monitoring, evaluations, and governance capabilities.
02Can Foundry connect to private enterprise data?
Can Foundry connect to private enterprise data?
Yes. We design identity, network, search, storage, database, file, retrieval, permission, logging, retention, and citation controls around the data source.
03Can Rokad build agents in Microsoft Foundry?
Can Rokad build agents in Microsoft Foundry?
Yes. We can define tools, knowledge, instructions, state, identity, approvals, evaluation, tracing, application integration, and production operation.
04Can Rokad integrate Foundry with existing Azure systems?
Can Rokad integrate Foundry with existing Azure systems?
Yes. We can integrate identity, search, storage, data platforms, APIs, functions, applications, monitoring, networking, security, and CI/CD.
Microsoft Foundry · AI integration services
Bring enterprise AI into the Azure operating model with evidence and control.
Rokad can architect Foundry projects, build agents and applications, integrate enterprise data and tools, and establish evaluation and operations.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.