Six services. One capstone.
One stack.
Most consultants will sell you AI. We'll tell you whether your data is ready for it. These are the engagements that get you there. Built on Microsoft Fabric, Power BI, and the rest of the Microsoft cloud, scaled to growing businesses, not enterprise budgets.
Every engagement starts with the same question: what is this data actually trying to tell you, and what's getting in the way of hearing it?
Here's the honest answer most consultants won't give you: most companies think they're ready for AI. Most aren't. The real value (the part that pays back in months, not years) usually comes from automation and integration. Connecting the systems you already own. Killing the manual work. Building the foundation AI can actually run on top of.
When we say AI, we usually mean automation and integration first. That sequence isn't a hedge. It's the math. Get those right and the AI work that follows, including AI agents that actually take action on your behalf, costs less, ships faster, and holds up in production.
Data foundations
Animation of records flowing from ERP, CRM, operations, and spreadsheet systems into a medallion lakehouse: raw in bronze, cleaned in silver, modeled in gold, with future analytics and AI capabilities waiting below.
Most data problems aren't data problems. They're foundation problems. Reports disagree because nobody owns the definitions. Dashboards take ten minutes to load because the model wasn't built for scale. AI is "exploring options" because there's no clean source to point it at.
We build the foundation properly: a Microsoft Fabric lakehouse using medallion architecture (bronze, silver, gold), source connectors to the systems your business actually runs on, and a semantic model that anyone in the company can query without breaking it.
- Source system audit and connector mapping (ERP, CRM, ops platforms, custom apps)
- Microsoft Fabric lakehouse implementation with medallion structure
- Data definitions, naming conventions, and lineage documentation
- Semantic model in Power BI tuned for performance at scale
- Governance setup: who can see what, who can change what
- Knowledge transfer to your team so they can operate it
Executive analytics
Animation of executive KPIs: revenue with year-over-year bars, gross margin with a donut chart, DSO counting down with satellite metrics, utilization bars, and on-time delivery, resolving into the Motion AI Systems logo.
Most executive dashboards die in their first quarter. They're either built for the wrong audience (engineers, not operators) or they answer questions no one is actually asking. The good ones share a few traits: they load instantly, the numbers tie back to what hits the bank account, and a board member can navigate them without a tutorial.
We design and build executive-grade reporting in Power BI: board packs, KPI dashboards, M&A target screens, operational scorecards. The kind of work that makes a CFO look prepared and gets a board chair off your back.
- Working sessions with executives to define the questions worth measuring
- Power BI dashboards built on a governed semantic model
- Board pack templates that update themselves on a schedule
- Drill paths that go from headline numbers to source transactions
- Mobile and tablet layouts (executives don't carry monitors)
- Training for the team that owns the report going forward
Process automation
Every business has them: monthly close routines, invoice processing flows, onboarding checklists, weekly report assembly. They eat hours, depend on people remembering steps, and break when key staff leave.
We automate them using Power Automate, Azure Logic Apps, and (where appropriate) lightweight custom code. Not "rip and replace." Your tools stay where they are, but the work moves itself between them while you do something more valuable.
- Process audit: where the time is actually going, prioritized by ROI
- Automation builds on Power Automate, Azure Logic Apps, or Fabric pipelines
- Approval workflows with audit trails (matters for SOX and PE-backed cos.)
- Error handling and alerting (automations that don't fail silently)
- Documentation your team can maintain without us
System integration
You bought the ERP. You bought the CRM. You bought the warehouse system, the e-commerce platform, the field service app. They all work, independently. The cost of them not talking is everywhere: duplicate entry, lagging dashboards, missed handoffs, and a finance team building bridges in spreadsheets at month-end.
We integrate these systems properly: APIs, event-driven pipelines, data syncs, master data management. The goal isn't elegance; it's a business where information flows where it needs to without anyone copy-pasting.
- System landscape map: what's connected, what isn't, what should be
- API-based integrations or event-driven pipelines (depending on what your systems support)
- Master data management (one customer record, one product record, one source of truth)
- Monitoring and alerting for when integrations fail (they will)
- Vendor-neutral design (we don't care which platform you bought, we make it work)
AI readiness
Boards are asking. Investors are asking. Your team is asking. The pressure to "have an AI strategy" is real. So is the pressure to not waste $500K on a pilot that goes nowhere because the underlying data wasn't ready.
This is a focused engagement: two weeks of structured assessment that answers three questions. Where would AI actually create value here? What infrastructure work has to happen first? Which pilot is worth running, and which one will burn cash? You get a written roadmap you can take to your board.
- Executive interviews to find the real bottlenecks (not the buzzword-driven ones)
- Data readiness assessment against your top 3-5 candidate use cases
- Build vs. buy analysis for each use case (Copilot, custom, off-the-shelf)
- Phased roadmap with cost estimates, timelines, and risk flags
- Board-ready presentation deck you can use as-is
AI implementation
Once the foundation is right and the roadmap is set, we build. Microsoft Copilot Studio for internal copilots that know your business. Azure AI Foundry for custom models. Power BI Copilot for analytics. The work is less about choosing the model and more about the infrastructure around it: retrieval, evaluation, guardrails, change management.
The AI is the easy part. Everything that makes it actually work in production is the hard part. That's the part we build.
- Implementation of the pilot or rollout defined in your AI roadmap
- Copilot Studio, Azure AI Foundry, or Power BI Copilot builds (whichever fits)
- Retrieval pipelines that ground the AI in your governed data, so it answers from your facts, not its imagination
- Evaluation framework (how do you know it's working, and how do you catch when it isn't)
- Adoption support: training, internal champions, feedback loops
- Cost monitoring (AI bills can get away from you fast)
AI agents
Copilots answer questions. Agents take actions. An agent qualifies the lead, drafts the outreach, sends the follow-up, updates the CRM, and pings a human only when something needs a real decision. The work that used to take an analyst now runs 24/7 with audit trails.
This is where most consultants build flashy demos and stop. The reason ours work in production is the same reason everything we build works in production: they're plugged into the foundation we already built. Clean data. Integrated systems. Real governance. An agent without that is just a chatbot with delusions of grandeur.
We build on Microsoft Copilot Studio for governed, low-code agents that live inside Teams and Microsoft 365, and on custom orchestration (Azure AI Foundry, Semantic Kernel) when the use case needs more horsepower or deeper integration with your existing stack.
- Use case scoping (which agent gets built first, and what success looks like)
- Copilot Studio or custom orchestration build, integrated with your existing systems
- Tool registry and action permissions (what the agent can do, what it can't)
- Human-in-the-loop checkpoints for high-stakes actions (sending money, changing records)
- Evaluation harness (measure agent quality on real workflows, not vanity metrics)
- Audit logging and governance review (agents that take actions need accountability)
- Adoption support and a roadmap for agent #2, #3, and beyond
Not sure which one you need?
Most engagements start with a thirty minute call to find out what is actually slowing you down. No pitch deck, no proposal pressure. Just a conversation about your stack and where the friction is.