For Enterprise & Mid-Market Teams
Ship it. Scale it.
Your agentic AI pilot works in the demo. Most don't survive the second team that tries to build on them — Gartner predicts over 40% will be cancelled by the end of 2027. Closer to home, SAP and Oxford Economics found 66% of Canadian organizations are already piloting agentic AI, and 97% say they're not fully ready to deploy and govern it.
We get Azure AI platforms into production with proper landing-zone governance, and GitHub-native agentic tooling — Copilot, the coding agent, Actions — as the delivery backbone.
Based in Calgary · Serving enterprise and mid-market teams across Canada and beyond
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What We Do
01
Governed Azure landing zones and AI landing zones as the foundation — identity, network segmentation, and Infrastructure as Code built in from day one, so environments are reproducible, not drift-prone portal deployments.
02
Building and deploying custom agents and agentic applications from inception to production on Microsoft Foundry and Foundry Agent Service — or Databricks, where that's already the data platform of record. Multi-agent architecture, tool and MCP integration, and the judgment to know what's actually worth building as an agent.
03
Evaluation gates, model routing, and cost discipline built into the deployment pipeline — not a review meeting bolted on after the fact. Fluency across Microsoft's agent frameworks, and the judgment to know when an agent is the wrong tool for the job.
04
GitHub Copilot's coding agent and agent mode, GitHub Actions, and MCP as the delivery backbone — agents handling the drudgery of software delivery so your engineers spend their time on the decisions that need a person. Developer experience isn't a side effect here; it's the design target.
05
Choosing the right target — App Service, Container Apps, or AKS — and using AI-driven modernization tooling to upgrade legacy Java and .NET applications, instead of a manual rewrite that eats a quarter of engineering time.
Need the Azure foundation first, such as landing zones, migration, cost and security? See Azure Cloud Advisory.
Guardrails
Counting tokens rewards activity, not results. Every agent we put into production ships with a control, an owner, and an outcome you can point to — so the work gets lighter, faster, and cheaper to run, not just busier.
An agent reaches production only after it passes a test you can read and rerun. A demo that works once is not a release, so fewer pilots stall or get cancelled after launch.
Calls go through one gateway with zero trust identity, so each one is attributable to a team and a purpose. You can see who used what, and revoke it without touching the agent.
Model routing and cost controls sit in the deployment pipeline, so spend is read against outcomes delivered rather than tokens consumed. Cheaper models handle routine work; the expensive ones are reserved for where they pay off.
Agents take on the routine work, and a human approves the steps that carry risk. You decide where that line sits, and it is written into the pipeline rather than left to habit.
The First Step
Two weeks
A fixed-length look at where your platform stands before you commit to a build. We scope it on a discovery call.
Use Cases
Illustrative problem patterns from the industries we work in — not write-ups of specific client engagements.
A pattern founder Kevin Evans saw repeatedly across oil & gas customers during his years at Microsoft: a critical field or asset-management application, a decade or more old, needed modernizing without the operational risk of a full rewrite. The approach: GitHub Copilot's app-modernization tooling instead of a ground-up rebuild, hardened with GitHub Advanced Security's code and secret scanning before anything shipped, running on Azure Red Hat OpenShift — jointly engineered, operated, and supported by Microsoft and Red Hat, deployed through the ARO Landing Zone Accelerator's secure-baseline pattern — with Microsoft Entra ID handling single sign-on end to end. The result: a CNCF-certified Kubernetes platform and real platform engineering under an operation that can't tolerate an unplanned outage, not a bespoke stack held together by one engineer's tribal knowledge.
Read the full write-up →Multiple teams want to ship agents against sensitive customer data, but without a shared identity gateway and evaluation gate, every team is one ungated deployment away from a compliance incident nobody signed off on.
GitHub Copilot's coding agent works well for the team that adopted it first, then stalls the moment a second team wants in, because MCP configuration and agent secrets were never centralized at the org level.
Dozens of legacy applications built up over decades, modernized with AI-driven tooling instead of a manual rewrite one system at a time — the Government of Alberta's own published results with Anthropic show the order of magnitude this can reach.
Read the Alberta case →Dependabot alone doesn't scale — every PR still needs a human to sort the safe patch from the major version bump that needs real scrutiny. GitHub Copilot's cloud automations handle that triage on a schedule, but only work unattended because of what's gated: an admin must deliberately enable the org's cloud agent policy, and GitHub Actions still requires human approval before anything runs.
Read the full write-up →Before an oil & gas org's first agentic AI use case shipped, the platform it would run on needed governance built in — identity, networking, and policy — using Azure AI Landing Zones as the reference architecture, not a bespoke build from scratch.
Read the full write-up →Proof, Not Slides
Every pillar above has a technical write-up behind it — fact-checked against current Microsoft and GitHub documentation, not marketing copy.
Memberships & Affiliations
An active member of the programs and partnerships shaping cloud, security, and open source — from the Government of Alberta to the platforms enterprises run on.
FAQ
Founder Kevin Evans spent nearly five years at Microsoft — from Azure Customer Engineer through Senior Solutions Engineer — leading cloud and AI platform strategy and driving enterprise adoption of cloud-native and AI-powered solutions, and is CNCF Ambassador and Chapter Lead for Calgary's cloud-native community. Forward deployed engineering means he's the one embedded with your team on this work, not a name on a partner slide.
Kevin Evans leads every engagement and is the engineer embedded with your team. When an engagement needs more hands, he brings in engineers for that engagement. The work is built as infrastructure in your own tenant and repositories that your team owns and extends, so continuity doesn't depend on any one person's tribal knowledge.
No. We build the governance pipeline, landing zone, and agent tooling as reusable infrastructure your team owns and extends — embedded alongside your platform team, not instead of them. Forward deployed engineering means we ship the system, not a slide deck.
That's the point of building it this way. The evaluation gates, CI/CD pipeline, and identity/gateway policies are designed to be reused by the second, fifth, and tenth team, not rebuilt bespoke each time — see our write-up on turning a single pilot into a company-wide platform.
Both, and specifically the combination — Microsoft Foundry and Azure landing zones for the AI platform and infrastructure, GitHub Enterprise, Copilot, and the coding agent as the DevOps delivery backbone. That pairing is the core of what we do, not two separate offers stapled together.
Both — the governance and pipeline discipline scales down as well as up. A mid-market team gets the same evaluation-gate and identity-first architecture, sized to their environment, without the multi-quarter enterprise rollout timeline.
Pricing depends on the scope — a landing zone assessment, a full agent platform build, and a legacy application modernization are very different projects. Book a discovery call for a straight answer on cost and timeline once we know what you're actually trying to ship.
Book a discovery call, or take the readiness scorecard first if you want a quick read on where your team stands.
