Matthew Serafin Resume ↓ Contact ↗
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Solutions engineering · implementation · AI integration

I simplify complex systems for reliable execution.

Connecting business needs to technical execution across data systems, analytics, implementation, AI integration, and operations.

Business student · University of Mary Washington · Class of 2027

See the systems ↓ matthewserf@gmail.com
A tangled set of inputs becomes an orderly system and one verified result.

Complex inputs. One accountable path.

~120Kdestination errors classified
7,500+formulas reconciled
11,656employers structured
22variables standardized

Selected work

Systems are easier to trust when the evidence is visible.

Three projects. Three different operating problems. Each visual shows the logic before the technical detail.

01 / Delivery systems

Located where valid data stopped delivering.

Compared the same events across system layers, ruled out upstream collection, and routed destination-specific findings to platform owners.

~120Kdestination errors classified
0Controldata warehouse rejections
Where the delivery path broke
Delivery audit showing an intact collection layer, a clean data warehouse control path, and destination-specific payload, authorization, and other error categories.
02 / Reporting systems

Built a report that verifies its own output.

Replaced fixed spreadsheet ranges with input discovery, explicit attribution rules, and an independent reconciliation path.

7,500+formulas reconciled
0Release gatemismatches
Independent reconciliation
An export passes through attribution, then splits into a workbook and an independent baseline that must match before release.
03 / AI integration

Mapped AI capability into an operating system.

Applied native-platform verification as the completion standard for AI-assisted campaign changes.

Completion standardEvery operational change verified in the native platform.
From context to verified execution
Four AI integration stages: context grounding, capability boundaries, human validation, and native-platform verification.

Supporting records

More evidence, less exposition.

GTM variable standardization22 variablesOpen record +

Replaced recursive searches and guessed keys with named source variables, explicit cleaning, validation, and documented missing value behavior.

Retool master data workflowReliable stateOpen record +

Corrected race conditions, numeric ID handling, persistent step values, duplicate validation, and false success states.

Paid media pacingDaily deltasOpen record +

Rebuilt monthly trends from daily deltas, handled negative resets, and separated current snapshots from history.

Employer monitoring system11,656 employersOpen record +

Defined tier logic, monitoring cadence, acceptance tests, role filters, and QA controls for an AI-assisted implementation.

Approach

A repeatable way of working.

01TraceMap data, dependencies, and ownership to isolate the actual error domain.
02BuildTranslate requirements into controlled workflows with explicit states and edge-case handling.
03VerifyReconcile outputs through an independent path before release.
04TranslateTurn technical evidence into decisions teams can act on.

Experience

Business context meets technical execution.

Solutions Engineering / Media Operations InternDigital marketing and media agency

Mapped cross-platform data flows, audited tracking errors, implemented internal tools, validated reporting logic, and translated findings into technical handoffs.

Marketing & Networking InternUniversity athletics program

Built digital content and alumni engagement workflows that connected student athletes with professional networks and career opportunities.

Social Media Marketing & Event Management InternAutomotive events organization

Analyzed channel performance and coordinated vendor, hospitality, and on-site operations across five high-volume automotive events.

ToolsPython, pandas, SQL, Excel, Google Tag Manager, Retool

EnvironmentsData warehouses, ad platform APIs, AI-assisted campaign platforms

Start a conversation

Looking for systems work that rewards clear execution.
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01 / Multi-source delivery audit

Tracing the error domain.

The same conversion events were compared at collection, data warehouse, and destination layers. The clean data warehouse route became the control that isolated downstream defects.

Role
Audit design, evidence classification, cross-layer comparison, root-cause isolation, technical handoff
Evidence
Approximately 120K destination errors classified across payload, authorization, and other categories
Control
0 data warehouse rejections, ruling out upstream collection
Handoff
Destination-specific findings routed to platform owners
Error domain

The layer where an error is introduced, not simply where it appears.

Control path

A parallel route used to verify that shared upstream data remains intact.

Payload error

A destination rejects fields or values that violate its API contract.

Authorization error

Authorization stops the event before the destination evaluates it.

02 / Reporting automation

Independent verification by design.

The replacement reads each export, applies explicit attribution logic, builds the workbook, and independently reconciles every published formula before release.

Role
Workflow analysis, Python and pandas implementation, attribution logic, workbook generation, QA
Evidence
7,500+ formulas independently reconciled with zero mismatches
Control
Delivery stops when the workbook and independent baseline disagree.
Schema discovery

Reading the export itself instead of relying on fixed cell locations.

Last-touch attribution

Assigning credit to the closest qualifying interaction within a defined window.

Independent reconciliation

Deriving the same result through a separate implementation path.

Fail-closed output

Withholding the deliverable when the verification layer does not pass.

03 / AI integration

Capability before claims.

An AI-assisted campaign platform is evaluated as an operational system. The work maps context to output, reproduces defects, and confirms whether changes reach the native destination.

Role
Platform evaluation, workflow mapping, capability analysis, defect reproduction, QA design, operating guidance
Model
Connection, metrics ingestion, campaign orchestration, and optimization treated as separate capabilities
Standard
A change is not complete until the native platform reflects it correctly.
Context grounding

Constraining output with brand, location, and campaign requirements.

Native-platform verification

Confirming a change appears correctly in the native destination.

Capability boundary

Separating current system behavior from roadmap assumptions.

Human validation

Keeping review at the point where AI output affects live operations.