Application Lifecycle Management

Power Platform ALM. Done right. At scale.

Recognized by Microsoft as “the best there is” in Power Platform ALM. We design and implement production-grade pipelines for organizations that have outgrown spreadsheets, manual exports, and one-solution-at-a-time thinking.

Why It Matters

Agents are building apps now. Your processes need to keep up.

AI agents are writing code, generating components, and assembling solutions faster than ever. But speed without process is just faster chaos. ALM gives both humans and agents the guardrails, validation gates, and deployment discipline to ship with confidence.

Agents Need Workflows Too

AI-generated components still need to be validated, tested, and promoted through environments. Without ALM, agent output bypasses every quality gate you have.

More Output, More Risk

The faster you produce, the faster you can break things. Structured deployments with approval gates protect production from the velocity that agents create.

Auditability Is Non-Negotiable

When a human and three agents all contribute to a release, you need to know exactly what changed, who approved it, and how to roll it back.

The Reality

Power Platform doesn't play by standard DevOps rules.

Standard CI/CD assumptions break on Dataverse. Git can't merge solution XML. Plugin uploads silently overwrite each other. Configuration is environment-specific but embedded in the package. Most teams learn this the hard way. We already have.

Dataverse is the merge engine, not Git

Solution components can only be reliably merged inside Dataverse itself. Standard Git branching and merging strategies don’t apply to solution metadata.

Multi-developer work causes silent collisions

Two developers uploading plugins to the same environment? The second silently overwrites the first. Without isolation strategies, work is regularly lost.

Configuration varies per environment

Connection references and environment variables must resolve differently in dev, test, and production — but they live inside the solution package.

Deployment sequencing is unforgiving

Connection references fail during import if not configured. Reference data can only load after tables exist. A single deployment step is never enough.

Our Approach

A dual-loop deployment model built for how the platform actually works.

We separate development-time iteration from release-time promotion into two distinct mechanisms, each with its own tooling, validation, and purpose.

Inner Loop - Daily Development

1. Develop
   Developer iterates in feature solution
2. Feature Branch
   Sync, export, and unpack to Git
3. Dev-Test
   Build, compile, and import as managed
4. Dev-Integration
   Transport components to integration solution

Outer Loop - Release & Promotion

1. develop → main
   PR triggers release build
2. Build
   Compile, version, package all solutions
3. GitHub / Azure DevOps Release
   Versioned artifacts with deployment settings
4. Test → UAT → Production
   Approval-gated promotion to each environment

Release Promotion Path

DevIntegrationTestUATProduction
Each promotion requires approval gates. Deployments are sequential to prevent dependency conflicts.

Agents in the Loop

Your developers say what to do. The agent runs the pipeline.

We build agent-directed workflow skills: Markdown files checked into your repo that encode the full operational knowledge for every ALM task. Checklists, parameter gathering, decision trees, validation steps. All with guardrails.

A developer types a natural language prompt in VS Code Copilot Chat. The matching skill activates, gathers missing inputs, selects the right script, and executes. No docs lookup, no context switching, no mistakes.

Copilot Chat

👤 start feature AB12345 add approval workflow
🤖 Created branch + feature solution in development. Set as preferred solution.
👤 sync solution from development
🤖 Exported, unpacked to feature branch. 12 files changed.
👤 build and deploy to dev-test
🤖 Built plugins + controls. Deployed managed to dev-test. Settings applied.
👤 transport AB12345 to integration
🤖 Validation passed. 14 components transported. PR opened for code-first changes.

Capabilities

Enterprise ALM. Purpose-built for Power Platform.

We work with GitHub Actions, Azure DevOps, and GitLab CI. Every implementation is designed around how Dataverse actually works, not how generic CI/CD tools assume it works.

Zero-Secrets Auth (OIDC)

Federated credentials issue short-lived tokens at pipeline runtime. No stored client secrets, no manual rotation, no leaks.

Smart Change Detection

Monorepo builds detect exactly which solutions, plugins, and controls changed — including transitive dependencies — and build only what’s needed.

Feature Isolation

Developers work in isolated feature solutions. Transport workflows merge only relevant components into integration, preventing collisions.

Settings Pipeline

Connection references and environment variables are templated, merged at build time, and validated before deployment — not patched afterward.

Extensible Hook System

Convention-based pipeline hooks let you plug in post-deploy flows, data imports, and custom validation without modifying core scripts.

Agent-Directed ALM Skills

Markdown-based workflow skills let AI agents run the full ALM lifecycle — sync, build, deploy, transport — with guardrails, validation, and zero docs lookup.

Versioned Release Packages

Date-based versioning, multi-solution package bundles, and per-environment deployment settings — one command deploys to any target.

How We Work With You

From assessment to production.

01 - Planning

Assess & Architect

ALM is not one-size-fits-all. We evaluate your current state: solutions, environments, team structure, and release cadence. Then we design a branching, deployment, and environment strategy that matches your organization.

02 - Implementation

Build & Automate

We build the pipelines, write the scripts, configure OIDC auth, set up environment approval gates, and wire up the full inner and outer deployment loops. You get a working system, not a slide deck.

03 - Customization

Extend & Adapt

Need custom validation hooks, automated data imports, or plugin registration without the GUI tool? We extend the pipeline with solution-specific logic while keeping the core scripts untouched.

04 - Support

Operate & Evolve

We don't leave when the pipeline is green. As your platform grows with new solutions, new environments, and new team members, we help you evolve the system to match.

Case Studies

Real implementations. Production results.

Multi-Solution Platform

Production-Grade ALM for a Multi-Solution Dataverse Platform

Designed and built a fully automated CI/CD system for four interconnected Dataverse solutions across nine environments using GitHub Actions, OIDC federated auth, and a dual inner/outer deployment loop.

• 4 Solutions
• 9 Environments
• 13 Workflows

Enterprise Migration

Enterprise ALM Overhaul for a Large-Scale Dynamics 365 Platform

Migrated a six-year, 40 GB Azure DevOps repository to GitHub, replaced ad hoc pipelines with a purpose-built GitHub Actions architecture, and implemented zero-secrets OIDC auth across eight environments.

• 30+ Solutions
• 8 Environments
• 25 MB from 40 GB

Ready to get serious about ALM?

Whether you're starting from scratch or migrating a legacy pipeline, we'll design an ALM system that works with the platform, not against it.