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
Develop
Developer iterates in feature solution
Feature Branch
Sync, export, and unpack to Git
Dev-Test
Build, compile, and import as managed
Dev-Integration
Transport components to integration solution
Outer Loop - Release & Promotion
develop → main
PR triggers release build
Build
Compile, version, package all solutions
GitHub / Azure DevOps Release
Versioned artifacts with deployment settings
Test → UAT → Production
Approval-gated promotion to each environment
Release Promotion Path
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.
👤 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.
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.
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.
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.
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.
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.
“Replacing the Plugin Registration Tool with a scriptable CLI workflow was one of the highest-leverage changes in the inner loop. What was a manual, GUI-only step became a repeatable, auditable automation.”
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.
Get in Touch