Our approach

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?

Sometimes the answer is a data foundation rebuild. Sometimes it's an automation that returns 30 hours a week. Sometimes it's AI on top of a clean stack. The point isn't selling you a fixed package — it's mapping the work to the constraint that's actually slowing you down, then doing it right.

/01 — Data foundations

Data foundations

The infrastructure layer everything else depends on.

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
Who it's for
Companies whose reporting takes too long, disagrees with itself, or breaks under load. If you've outgrown Excel and your last BI consultant left you with dashboards no one trusts, start here.
/02 — Executive analytics

Executive analytics

Dashboards CFOs and boards actually use.

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
Who it's for
CFOs, COOs, and CEOs who want to stop assembling board packs by hand the week before every meeting. Especially valuable for PE-backed companies and pre-exit founders preparing for diligence.
/03 — Process automation

Process automation

The work your team does every week that a machine should be doing.

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
Who it's for
Operations leaders, finance teams, and CFOs watching headcount creep up alongside revenue. If "we need another analyst" comes up in every QBR, automation is usually the cheaper answer.
/04 — System integration

System integration

Making the tools you already own talk to each other.

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)
Who it's for
Mid-market companies running 5-15 core systems with manual handoffs between them. Especially common in companies that grew by acquisition or stitched together their stack over time.
/05 — AI readiness

AI readiness

An honest answer to "should we be doing AI yet?"

Boards are asking. Investors are asking. Your team is asking. The pressure to "have an AI strategy" is real — and 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: 4-6 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
Who it's for
CEOs and CIOs being pressured to "do AI" but who want a credible answer before they spend. Especially useful before a board meeting, an investor update, or a strategic planning cycle.
/06 — AI implementation

AI implementation

Production AI on data that's actually ready for it.

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)
  • RAG pipelines connected to your governed data foundation
  • 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)
Who it's for
Companies who've completed an AI readiness engagement (with us or otherwise) and have a clear, sized pilot ready to build. Or organizations with a clean data foundation and a defined use case in front of them.

Not sure which one you need?

Most engagements start with a 30-minute call to find out what's actually slowing you down. No pitch deck, no proposal pressure — just a conversation about your stack and where the friction is.