Consulting
Areas of my practice where I take on outside engagements — agency and brand, AI integration, analytics, content and SEO, email, performance, and security. Every area is grounded in work I have shipped and operated myself.
“I consult in the places where I have built and operated real systems. The goal of an engagement is to leave a team with something they can run off of — a diagnostic, a plan, or a working implementation — not a deck.”
What I look for
- •Revenue leaks in conversion funnels that compound the longer they stay unfixed
- •Technical debt in infrastructure, data pipelines, or build systems that silently degrades performance
- •Measurement gaps where teams are making decisions on incomplete or wrong numbers
- •Content or SEO operations that generate activity without earning authority
- •Security posture gaps in authentication, session handling, dependency management, or infrastructure hardening
What I leave behind
- •A written diagnostic with specific findings, severity, and suggested ordering of work
- •A roadmap that sequences fixes by impact, effort, and dependency — not by vendor preference
- •Reference implementations, schemas, or runbooks that show how the work should actually be done
- •Measurement and monitoring wired in so the fixes can be verified instead of assumed
- •Ongoing review cadence if the engagement warrants it — most do not
For clarity on where things stand
Consulting engagements are occasional and focused on work that overlaps directly with what I already build every day. If that sounds like the right fit, the contact form is the way in — context in the first message helps me reply usefully.
Method
How I work
A short, structured approach that keeps engagements honest.
- Diagnose
- I start by looking at what actually exists — code, infrastructure, tracking, content, or process — and writing up what I find. No recommendations until the picture is clear.
- Sequence
- Findings get ordered by impact, effort, and dependency. I do not write plans that ignore what else has to change for a fix to land.
- Hand off
- Deliverables are written so someone else can execute them — runbooks, reference implementations, schemas, or code — not narrative-heavy strategy decks.
Reading the system
Most engagements start by reading the system as it is, not as it is reported to be. That usually means pulling data from the databases and logs directly, reading the deployment and CI configuration, and looking at what the code does instead of what the documentation says.
Direct data access
I work from the source databases and logs where I can. Reports built on top of reports tend to hide the interesting failures.
Funnel and path tracing
I trace specific user or request paths end to end — front-end events through to database writes — to find where things actually break down.
Baseline measurement
I establish a written baseline of the current state before recommending anything. Without a baseline the engagement cannot be verified.
Impact sizing
Every finding gets a rough estimate of what fixing it is worth — usually as a range, not a single number — so the team can prioritize against other work.
Tools & Frameworks
Working inside the existing stack
I try to avoid engagements that end in a rewrite recommendation. The interesting work is usually finding the improvements that fit inside the existing stack — whether that is a database query, a component, a CI step, or a content template — and measuring the change against the baseline.
Stack alignment
I read the team’s actual tooling before making recommendations. I do not recommend tools the team cannot operate.
Incremental change
I prefer a sequence of small, verifiable changes over a single big refactor. Each change either lands or teaches the team something about why it did not.
Verification by measurement
Every change is verified against the baseline. If a fix does not show up in the numbers, I would rather revert it than claim a win.
Tools & Frameworks
Process
The five stages most engagements move through. Click a stage for details.
What this covers
Direct system read
I work from the source — databases, logs, CI configuration, deployment scripts — not reports built on top of reports.
Baseline measurement
I establish a written baseline of the current state before recommending anything. Without a baseline the engagement cannot be verified.
Findings document
A written summary of what the system is actually doing, with specific findings grouped by severity and surface area.
Impact sizing
Each finding gets a rough estimate — usually a range, not a single number — of what addressing it is worth.
What you get
Written findings document
A structured document describing the real state of the system, with specific findings and severity ratings.
Prioritized work list
A ranked list of findings scored by impact, effort, and dependency — the sequence for what should be addressed first.
Baseline metrics
The measurable starting point so every future change can be verified against a benchmark instead of assumed.
What this covers
Hypothesis and scope
Each prototype starts with a written hypothesis — what I am testing, why, and what success looks like.
Minimal-surface implementation
I work inside the existing stack wherever possible — the smallest change that can test the hypothesis gets shipped first.
Measurement
Each change is measured against the baseline. Results are written up with enough rigor to distinguish real signal from noise.
What you get
Test results document
A clear writeup of each experiment — what was tested, what happened, and what the next action is.
Validated assumptions
A shortlist of diagnostic findings that have been verified in production before committing to the larger work.
Refined work sequence
An updated ordering of remaining work based on what the prototypes taught the team.
What this covers
Funnel-level surface work
Funnel analysis followed by targeted redesigns at the specific steps where drop-off is happening.
Form and checkout tightening
Reducing fields, fixing error handling, improving mobile behavior, and optimizing for guest checkout where it applies.
Page-level design and copy
Specific page redesigns based on real user behavior — not applied best-practice templates.
Mobile-specific work
Dedicated attention to mobile-specific friction — touch targets, scroll behavior, and mobile-first checkout.
What you get
Measured conversion changes
Before-and-after numbers for each surface change, benchmarked against the phase-one baseline.
Reusable page patterns
Working page and component patterns that can be reused across the rest of the site or similar flows.
Observability on the fix
Instrumentation and alerting so the improvements are visible in monitoring, not assumed from a single measurement.
What this covers
Workflow audit
A structured audit of where language-model work will be useful and where it will not, with specific integration points identified.
Model selection and orchestration
Per-step model selection with cost projections and fallback behavior — not a single model for every task.
Cost and quality tracking
Per-token cost ledger and output quality metrics wired into the integration from day one.
What you get
Working AI integration
A functioning integration running against the real workflow, built inside existing platforms where possible.
Cost ledger
Per-token cost tracking and monthly reporting so the AI spend is measurable and controllable.
Governance and review process
Written guidelines for when the team trusts model output, when it requires review, and how to handle model drift and new releases.
Extensibility notes
Documentation of how the integration can be extended to adjacent workflows without rewriting the core.
What this covers
Review cadence
Structured monthly or quarterly reviews with written reports covering metrics, trends, and next-action recommendations.
Regression alerting
Monitoring that flags performance regressions, tracking failures, or drift before they cause downstream damage.
Next-action planning
Each review produces a concrete list of next actions — not an open-ended discussion document.
What you get
Compounding improvements
Month-over-month improvements in the metrics that matter, because each review cycle builds on verified learnings from the last.
Working cadence document
A living document that reflects current priorities, recent learnings, and upcoming work.
Regression safety net
Alerts and monitoring that catch problems before they show up in manual review cycles.
Why I start with diagnosis
Most of the time the problem a team describes is not the problem I find once I read the system. The described problem is usually a symptom. I have seen campaigns that report well and convert badly because the attribution is wrong, caches that report warm and serve cold because the invalidation is broken, editorial pipelines that report healthy and push duplicate content because the deduplication step was skipped in a rollout. Starting with diagnosis means I can tell the team which problem is actually worth solving — often the one underneath the one they asked about — and what it will take to solve it inside the existing stack without a rewrite.
“The cheapest improvements usually live inside the system a team already has. I start by reading that system carefully before recommending anything new.”
Questions
Common questions about how engagements work.
Areas of practice
Individual areas I take on.
Analytics Consulting
Measurement systems built to drive decisions, not reports.
AI Consulting
Practical AI integration, tested in production.
Content Consulting
Editorial systems, automated production, and programmatic SEO at scale.
Brand Consulting
Positioning, voice, and visual systems that outlast a redesign cycle.