Consulting

Make the systems you already own work together.

I work with small and mid-sized organizations to turn fragmented data, manual reporting, disconnected systems, and repetitive workflows into connected, AI-enabled business processes — with a CPA's view of what has to stay controlled.

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Advisory and hands-on delivery · Finance, operations, and data · Seattle, WA, and remote

Who I work with

Organizations: Small and mid-sized companies that have grown faster than the systems and processes supporting them.
Leaders: CFOs, controllers, CEOs, COOs, and the finance and operations leaders accountable for the numbers.
Teams: Companies without a large internal data, reporting, or automation function — and no appetite to build one.

01Where this usually starts

You have probably outgrown the way your information works.

Most organizations do not have a data problem. They have a problem with how information moves between the systems they already own.

01Reporting still runs on spreadsheets — and one person knows how they are built.
02Core information lives in systems that were never connected to each other.
03Someone downloads the same reports every cycle and combines them by hand.
04Finance spends more of the month preparing reports than analyzing them.
05Two departments answer the same question with two different numbers.
06Leadership cannot see performance without asking someone to build the view.
07Budgeting and forecasting take weeks, and are dated by the time they are approved.
08Repetitive administrative work quietly consumes people you cannot easily replace.
09The board is asking about AI, and no one can point to where the return would be.
10You already own capable platforms that have never been made to work together.

None of these are technology failures. They are information-design and process problems — and most of them are solvable without replacing what you already bought.

02Services

Five areas of work.

Most engagements touch more than one, because the reporting problem and the process problem are usually the same problem.

S-01

AI & Business Transformation

AI should solve a business problem, not be adopted because it is new.

I start where decisions are slow, work is repetitive, and information is hard to get — then identify the few AI use cases with a defensible return and the order to deliver them in. Processes are redesigned before they are automated, and governance is designed in rather than added afterward.

AI opportunity assessment
Implementation roadmap
High-value use case identification
AI-assisted workflows
Internal AI tools
AI-enabled analysis
Process redesign
AI governance & review

S-02

Reporting & Analytics Modernization

Leadership should not have to ask twice for the same number.

Executive, financial, and operational reporting rebuilt so that it produces itself: metrics defined once, an agreed source behind each one, refreshes that run on their own, and dashboards built around the decision rather than the data model. Where it fits, business users answer routine questions themselves.

Executive dashboards
Financial & operational reporting
KPI development
Automated reporting
Management reporting redesign
Business intelligence strategy
Self-service reporting

S-03

Data Integration & Business Portals

Many companies do not need another system. They need their existing systems to work together.

Finance, HR, payroll, POS, ticketing, marketing, CRM, operational systems, budgeting tools, and external market data brought into one environment, so a question can be answered in one place instead of four. I have built a centralized business portal spanning multiple departments and platforms inside a multi-department operating environment.

System & data integration
Centralized business portals
Finance, HR & payroll data
POS, ticketing & operations
CRM & marketing data
Budgeting & planning tools
External market data
Shared metric definitions

S-04

Workflow & Process Automation

Automate the preparation. Preserve human judgment for the decision.

The recurring work that consumes a team's week — collecting data, assembling reports, chasing approvals, reconciling between systems, checking for errors, distributing information — designed out where it should not exist, and handed to software where it should. Exceptions surface to a person instead of failing quietly.

Data collection
Report preparation
Approval workflows
Reconciliations
Recurring reporting
Data validation
Internal requests
Information distribution
Business alerts
Routine administration

S-05

Finance Transformation

Finance expertise combined with technology execution.

This is where the work differs from a technology consultant's. As a CPA with FP&A experience, I modernize how finance operates while understanding what accounting controls, reporting requirements, and management decision-making actually demand — so that faster does not quietly become weaker.

FP&A modernization
Budgeting & forecasting
Management reporting
Financial modeling
KPI architecture
Finance automation
Reporting process redesign
ERP & reporting optimization
Decision-support tools

03How I work

Four steps, in this order.

Understand the business before changing it, simplify before automating, then prove the change was worth making.

01
Understand
The business, the systems, the processes, the pain points — and the decisions management is actually trying to make. That means time with the people doing the work, not only with the people describing it.
02
Simplify
Before anything is connected or automated, decide what should stop. Steps, reports, and approvals that no longer earn their place are eliminated rather than rebuilt faster.
03
Connect & Automate
Connect the sources, automate the repetitive activity, and apply AI where it changes the result — using the platforms you already own wherever they will do the job.
04
Measure & Improve
Track adoption, time returned, accuracy, and business value, then keep improving. A process no one uses was never finished, whatever the demo showed.

The order is the point. Automating a process that should not exist only makes it permanent.

04The difference

Where finance meets technology.

A technology consultant starts with the software. I start with the business question.

The common failure in transformation work is a capable platform installed on top of a process no one examined. The tool works. The process does not, so people quietly go back to the spreadsheet.

Before I recommend a platform, an automation, or an AI use case, the conversation is about the decision it is meant to improve and the work it is meant to remove.

The questions I start with

01What business decision are we trying to improve?
02What information does management actually need to make it?
03Where does that information originate, and who owns it?
04Why is the process manual today?
05What controls have to remain in place?
06What should be automated — and what should simply stop?
07Where is human judgment still necessary?
08What financial or operational value will the change create?

Technology and AI are selected after those answers, not before. That sequence is the difference between a system leadership adopts and one they work around.

05What changes

From assembling the information to using it.

The same people, the same systems, a different design — and a week that looks different at every level of the organization.

Today

Information spread across systems that do not reconcile
Reports downloaded and rebuilt by hand every cycle
Copy-and-paste reporting, with copy-and-paste errors
Departments working from different versions of the numbers
Hours of preparation before any analysis starts
Limited visibility for leadership between close cycles

Most of the effort goes into producing the report.

After

Connected data with one agreed source behind each metric
Automated workflows that run without being chased
Centralized reporting leadership can open for themselves
Shared performance metrics across departments
AI-assisted analysis on top of reconciled data
Self-service answers to routine questions

Most of the effort goes into deciding what to do about it.

06Engagement options

Ways to work together.

Each begins with the problem rather than a proposal — an assessment, a defined project, or an ongoing advisory relationship.

E-01

AI & Automation Assessment

What it is: A structured review of your processes, systems, reporting, and AI opportunities — how information moves today, where it stalls, and what it costs the organization in time.
What you get: A prioritized transformation roadmap: what to fix first, what to connect, what to automate, and where AI has a return worth pursuing.
Best for: Leadership teams that know something should change but want an objective read before committing budget.

E-02

Focused Transformation Project

What it is: A defined engagement to solve one specific reporting, workflow, data, or automation challenge end to end — designed, built, tested, and put into use.
What you get: A working solution in production, documented, with your team able to run and extend it.
Best for: Organizations with a known problem: a reporting cycle, a manual process, or two systems that should have been one.

E-03

Fractional Transformation Advisor

What it is: Ongoing strategic guidance across finance modernization, analytics, automation, and AI — a senior perspective available to your leadership team on a recurring basis.
What you get: Judgment on roadmap, sequencing, build-versus-buy, and vendor decisions, plus a check that what was delivered is actually being used.
Best for: Companies without an internal data or automation leader, and no reason yet to hire one full time.

Scope, timeline, and fees are set after the first conversation, once the problem is clear. Every engagement starts with the problem, not a proposal.

Next step

Have a process you know should work better?

If your team spends too much time gathering information, preparing reports, reconciling systems, or repeating the same work every month, the issue is usually how the process was designed — not how hard anyone is working. A short conversation is normally enough to tell whether it is worth pursuing.

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