AI strategy and readiness
Where AI creates value in your organisation, what data, licensing, security and skills are needed, and a roadmap that starts with the highest-value, lowest-risk use cases.
AI & Copilot engineering
AI engineering that turns use cases into governed, production capability
BD-S engineers Microsoft Copilot, Copilot Studio agents and AI-assisted business processes the way we engineer any enterprise system: with readiness, architecture, testing, governance and operations designed in from the start.
Governed · Tested · Reusable
What we do
Seven areas, one engineering discipline
From strategy through to operations, the same method applies whether the asset is a Copilot prompt, a Copilot Studio agent, a retrieval service or a custom AI application.
Copilots and digital workers
Microsoft 365 Copilot capability, Copilot Studio agents and Agentforce assistants engineered for specific roles and processes, with the prompts, knowledge and guardrails to make them dependable.
Agentic automation
Agents that carry out multi-step work across Microsoft 365, Dynamics 365, Power Platform and Salesforce, with defined scope, approvals and human checkpoints.
Knowledge and RAG
Retrieval-augmented generation over your documents, records and data, grounded in SharePoint, Dataverse, Azure AI Search and Data Cloud with permissions respected.
AI application development
Custom AI-enabled applications on Azure AI Foundry, Azure OpenAI and the Power Platform, integrated with your existing systems and identity.
Integration and orchestration
Connecting agents and models to the systems that hold the work: APIs, MuleSoft, Azure Integration Services, Power Automate and line-of-business applications.
Responsible AI and governance
Ownership, versioning, approval, limitations, review cycles, data protection and lifecycle controls so AI capability can be trusted and scaled.
Our method
Nine steps from use case to governed asset
- Step 1: Use case
- Step 2: Pattern
- Step 3: Design
- Step 4: Test
- Step 5: Score
- Step 6: Refine
- Step 7: Approve
- Step 8: Publish
- Step 9: Govern
We treat prompts, agents and AI assets as governed production assets, not one-off experiments.
Lifecycle
From idea to production operation
Each stage has defined inputs, outputs and owners, so an AI capability can move from a good idea to something your organisation runs and trusts.
Stage 01: Readiness
Assess data, licensing, security posture, skills and governance so the first use cases start on solid ground.
Stage 02: Use-case engineering
Identify valuable, repeatable business activities where AI creates measurable value, ranked by value, repeatability, risk and readiness.
Stage 03: Architecture
Choose the right pattern: Copilot prompts, a Copilot Studio agent, a RAG service or a custom Azure AI application, and how it connects to your data and identity.
Stage 04: Implementation
Engineer prompts, patterns, agents and integrations to defined standards, with reusable templates and authoring guidance.
Stage 05: Testing and evaluation
Repeated runs, scoring against a quality rubric, failure analysis and refinement before an asset is called production-ready.
Stage 06: Governance
Ownership, versioning, approval, documented limitations, review cycles and lifecycle controls agreed before release.
Stage 07: Adoption
Publish governed assets, enable teams and transfer capability so internal owners can continue the method.
Stage 08: Productionisation
Deploy into Microsoft 365, Power Platform, Azure or Salesforce with monitoring, access controls and support in place.
Stage 09: Ongoing optimisation
Monitor usage and quality, tune prompts and agents, retire what is not working and extend what is, under the managed service.
What you receive
Deliverables that outlast the engagement
Use-case backlog
Prioritised use cases ranked by value, repeatability, risk and readiness.
Prompt and AI asset library
Structured, searchable and governed reusable assets within Microsoft 365.
Engineering framework and standards
Reusable architecture, patterns, templates and authoring methods for prompts and agents.
Quality assurance rubric
A measurable definition of what production-ready means: accuracy, consistency, safety and business suitability.
Testing evidence and limitations
Evidence of what works, what varies and where human review is required.
Governance model
Ownership, versioning, review, approval and lifecycle controls.
Skills and agents readiness
A pathway from reusable prompts into broader Copilot and agentic capability.
Capability uplift
Templates, guidance and knowledge transfer so internal teams can scale the method.
Responsible AI
Responsible deployment is part of the engineering
AI capability in government and enterprise has to be explainable, reviewable and bounded. BD-S designs the controls with the solution rather than adding them at the end, aligned to Microsoft's secure by design principles and the data protection tooling already in your tenant.
Data protection with Purview- Named owners and approval for every published prompt, agent and asset
- Documented limitations and the cases that need human verification
- Data protection through Microsoft Purview, sensitivity labels and Dataverse security roles
- Least-privilege access for agents, connectors and integration accounts
- Versioning, review cycles and retirement as part of the lifecycle
- Test evidence retained so decisions can be audited
Technology
Platforms we engineer on
Typical engagements
Where we usually start
AI readiness assessment
Data, licensing, security, governance and skills reviewed against your priority use cases, with a roadmap.
Copilot Adoption Engineering sprint
A 6-8 week engagement that takes 3-5 priority use cases from discovery through engineered, tested and governed assets.
Agent build
A Copilot Studio or Agentforce agent for a defined process, integrated, tested and governed before release.
RAG and knowledge service
Grounded retrieval over your content with permissions respected, delivered as a reusable service.
AI application delivery
A custom AI-enabled application on Azure AI Foundry or the Power Platform, from architecture to production.
AI operations
Monitoring, tuning and governance of your AI assets under the BD-S managed service.
Evidence
Proven through public procurement
The method on this page is the one a federal government agency selected BD-S for through public procurement.
Federal government
Microsoft Copilot AI engineering for a federal government agency
A federal government agency awarded BD-S its Microsoft Copilot AI engineering work through a competitive public procurement, ranking BD-S first of five shortlisted Microsoft partners.
- Government
- Microsoft Copilot
- AI engineering
Related pages
- Microsoft Copilot Adoption offerThe structured 6-8 week engineering sprint.
- Copilot case studyCopilot AI engineering for a federal government agency, selected through public procurement.
- Microsoft 365 CopilotReadiness, rollout and governance for Copilot.
- Copilot StudioAgents on governed Microsoft foundations.
- Azure Data and AIAzure AI Foundry, Azure OpenAI and Fabric.
- Agentforce and AI on SalesforceAgents and Data Cloud in the Salesforce ecosystem.
Start with a priority business area
Begin with a focused engagement, prove the operating model, then scale the patterns across the organisation.