Data Architecture and Platform Modernization
We design the foundation your business actually runs on — scalable, well governed, and ready for production — and we move you off what is holding you back without pausing the business to do it.
A medallion architecture, layer by layer.
Platform assessment and modernization planning
We map what you have, what it costs, and what it constrains — then set out a migration path that does not require pausing the business while it happens.
Layered architecture
Raw ingestion, conformed modeling, and business-ready outputs kept separable, with explicit contracts between each layer so every stage can be validated on its own terms.
Cloud platform design
Lakehouse and warehouse structures, workspace organization, and the capacity decisions that determine what your platform costs to run once it is live.
Legacy modernization
Long-running estates carry years of accumulated business logic. We migrate them without discarding what that logic knows.
Security and governance by design
Row-level security, sensitivity classification, and retention handled as architecture rather than as a later compliance exercise.
Executive Reporting and Analytics
Reporting built for decisions rather than decoration. Focused analytics tied to the questions leadership is actually asking, backed by models that return the same answer however the numbers are sliced.
Semantic model design
The model is where most reporting implementations succeed or fail. We build models with defensible grain, explicit relationships, and measures that return the same answer regardless of how a user slices them.
Advanced calculation logic
Time intelligence, allocation rules, and complex business logic implemented so they perform at scale and remain readable by the next person who opens them.
Report design and rationalization
Most organizations have too many reports and too few answers. We consolidate around the decisions leadership is actually making, and retire what no one opens.
Forecast and analytical models
Volume forecasting, accrual modeling, cost allocation, and other calculation-heavy work, built to be inspected rather than trusted blindly.
Trust and adoption
Reconciliation against source systems, documented definitions, and refresh monitoring — because a number an executive does not believe is worse than no number at all.
AI Strategy and Integration
Agents, automation, forecasting, and predictive models, built on data you can trust. We identify where AI creates real value, sequence what to build first, and make governance part of the plan from day one.
Opportunity assessment
We evaluate your current estate against the outcomes your business is pursuing, and identify where AI is genuinely worth the investment — and, just as importantly, where it is not. Every assessment ends with a ranked list, not a list of possibilities.
Practical roadmaps
We translate opportunity into sequence: what to build first, what it depends on, what it will require from your team, and how you will know whether it worked. Roadmaps are written for the people who must execute them.
Agents, automation, and predictive models
The build itself — automation of the work that should not be done by hand, and models that forecast and predict on data solid enough to carry them.
Governance and readiness
Data quality, access control, lineage, and auditability are not obstacles to AI adoption — they are the conditions that make it survivable. We build governance into the plan at the start rather than retrofitting it after the first incident.
Pilot design and evaluation
We define narrow, measurable pilots with clear success criteria, so the decision to scale or stop is made on evidence.
Fractional Data and AI Leadership
Senior data and AI leadership embedded with your team, without the search or the headcount. We set direction, hold the standard, and stay accountable for the outcome rather than the hours.
Embedded senior leadership
Direction, architecture review, and technical judgement available to your team on a standing basis — without opening a search for a role you may not need at full time.
Standards and review
We set the bar for how work is built, documented, and released, then hold it. Your internal team and any vendors you use are reviewed against the same standard.
Roadmap ownership
Someone accountable for the sequence rather than the ticket queue, who can say what is being built next quarter and defend why.
Team capability
Mentoring your analysts and engineers so the capability stays in the building. The point of this engagement is that you eventually need less of it.
Vendor and platform decisions
Independent assessment of what to buy, what to build, and what to retire, from someone who does not sell the platform.
You do not need an enterprise budget to get your numbers straight
The four disciplines above describe work at enterprise scale. Smaller companies usually need something different: not a data platform, but a short, focused piece of work that removes a specific problem. We take that work too, and we scope it to what the business can actually carry.
Make the reporting you already have trustworthy
Most smaller companies do not have a reporting problem so much as a spreadsheet problem — several versions of the same numbers, rebuilt by hand every month, and nobody entirely sure which one is right. We consolidate what you have into one set of numbers that updates itself, and we document where every figure comes from.
Put AI to work inside the tools you already pay for
Most businesses are already paying for a meaningful amount of AI capability inside the software they run today. Before anyone buys another platform, we look at what those tools can already do for the work that is actually taking your team time.
Remove one recurring manual job
Every small business has a task somebody does every week that a machine should be doing — rekeying orders, reconciling two systems, assembling the same report. We pick the one that costs you the most hours, automate it properly, and hand it over documented.
Tell you honestly what is not worth doing
Plenty of AI and analytics spending does not pay for itself at your size. We will say so. A short engagement that ends with a clear recommendation to wait is a better outcome than a platform you cannot staff.
Engagements at this size are measured in weeks rather than quarters, and they are scoped and priced before they start — so you know what you are committing to.
How to Implement AI into Small Business sets out the same ninety-day plan in full, and it is useful whether or not you ever hire us.
Not sure where to start?
Most engagements begin with a conversation rather than a scope document. Schedule 30 minutes and we will help you work out which of these four you actually need first.
Schedule a consultation