Candidate dossier / Prepared for COOs, VPs of Engineering & Founders

Available 2026

Industry is the variable — Execution is the constant.

I'm Matt Bredehoft, a Senior Product Manager & Delivery Lead with 18+ years of shipping across four industries on purpose. I diagnose why teams bog down, rebuild the cadence, and drive measurable throughput — the playbook doesn't change when the vertical does.

Read the exhibitsGet in touch
years shipping
18+
products launched
15+
support tickets cut
60%
satisfaction, out of 5
3.2→4.3
hours saved via AI practice
10.9K+

The Exhibits

Five engagements, results first

Exhibit A

Cross-industry / AI

Human-led AI Enablement Practice

10,900+ hours saved across five industries.

The situation

Companies know AI is a force multiplier, but most fail at adoption — teams get pointed at ChatGPT with zero strategy, resulting in chaos, low utilization, and zero ROI.

Filed under

AI Agents · RAG / Knowledge Bases · RPA · Roadmap Tooling · Change Management

What I did

  • Built the operating model that moves teams from sporadic AI usage to measured adoption across client engagements.
  • Built the workshop framework and discovery process that maps workflows, surfaces bottlenecks, and prototypes ideas in days.
  • Designed the scoring and ranking engine and roadmap tool that prioritizes tasks by impact, risk, and ROI.
  • Shipped the non-technical dashboard, RAG knowledge bases, AI agents, and RPA pilots that let teams start delivering value immediately.
  • Led a forward-deployed engineering model so solutions go live in weeks, not months.
  • Owned the AI ROI calculator and the training and change-management experience.

Also on the record

  • Enterprise clients now have clear, actionable AI roadmaps and production agents delivering double-digit time savings.
  • Efficiency gains across healthcare, finance, tech, education, and retail.
  • Teams feel empowered instead of intimidated — adoption without hand-holding.

Noted:The hardest part of AI isn't the tech — it's the human factor. Get the right tasks, the right knowledge bases, and relentless execution in the first 30 days, and the rest compounds fast.

Exhibit B

Sports tech / consumer

PING Golf & Cool Clubs Fitting Apps

Fitting apps shipped on time for two of golf's most recognized brands.

The situation

Two recognized brands in golf equipment needed product thinking fast — a ball fitting app for PING Golf and a club fitting platform for Cool Clubs. The timeline was tight, and client teams needed to own the output.

Filed under

Product Validation · Roadmap Strategy · Client Enablement

What I did

  • Embedded as delivery lead and established a validation cadence in the first week.
  • Defined scope ruthlessly — shipped what mattered, cut what didn't.
  • Coordinated cross-functional execution and enabled client teams to carry forward independently.
  • Applied the same fast-validation model across both engagements.

Also on the record

  • Client teams left with roadmaps they could execute without ongoing dependency.
  • Validated the embedded delivery model as repeatable across short-cycle sports tech engagements.

Noted:The fastest way to earn trust with a new client is to show your work in the first week. Validate the roadmap in public, cut scope without apology, and ship something real before the honeymoon is over.

Exhibit C

Media / community platform

F-List Platform Rebuild

Moderation time and operational costs down; user trust rebuilt through transparency.

The situation

Platform moderation was eating up resources — manual processes, unclear guidelines, and a backlog of user complaints that kept growing.

Filed under

React · Node.js · Moderation Tools

What I did

  • Diagnosed moderation as a process problem, not a policy problem — then fixed the system.
  • Implemented a GitHub-style transparent moderation system.
  • Automated redundant moderation tasks to free up team bandwidth.
  • Redesigned UX flows to reduce friction and user confusion.

Also on the record

  • Reduced moderation time and operational costs.
  • Improved user trust through transparency.
  • Smoothed out the UX pain points that were driving support tickets.

Noted:Most moderation problems aren't about policy — they're about unclear processes and manual work that should be automated. Fix the system, not just the rules.

Exhibit D

Enterprise SaaS

The PostgreSQL Search Decision

Search shipped on deadline; weeks of engineering redirected to higher-priority work.

The situation

A client milestone was approaching with limited resources. They wanted robust search, and engineering was pushing Solr or Elasticsearch — powerful but complex, adding weeks to an already tight timeline.

Filed under

PostgreSQL · Performance Optimization · Strategic Tradeoffs

What I did

  • Facilitated the release-cadence tradeoff: PostgreSQL with performance tricks — lazy loading, sort by online status first — over Solr or Elasticsearch.
  • Made the business case: ship functional search now, gather user feedback, and upgrade later only if data proves it's needed.
  • Aligned stakeholders on validating before over-engineering, under a fixed milestone deadline.

Also on the record

  • Met the client's functional requirements without blowing the timeline.
  • Saved engineering weeks that went toward higher-priority features.
  • Maintained optionality to upgrade later if the data demanded it.

Noted:Strategic tradeoffs become easy when everything ties back to business objectives. Sometimes the right answer is: ship it, then validate.

Exhibit E

Entertainment / VR

MFR VR: Tech Demo to Shippable Game

A one-developer tech demo taken to a public soft launch in six weeks.

The situation

A VR client arrived with a tech demo — one developer, a cool concept, but no shippable product and no delivery cadence. Six weeks until a public soft launch.

Filed under

Unreal · VR Development · Community Management

What I did

  • Assembled a cross-functional team of three developers plus an artist, and established sprint planning, testing workflows, and a weekly demo cadence.
  • Coordinated a high-stakes soft launch at a major community event with only six weeks' notice.
  • Created the event strategy: 3D gameplay projection with branded glasses, plus QR codes driving traffic to Telegram and the site.
  • Prioritized features ruthlessly to hit the event deadline with a stable, demo-ready build.

Also on the record

  • Soft-launched successfully in front of hundreds of community members, generating significant buzz and early signups.
  • Took a one-person R&D project to a revenue-generating product; on track to ship Early Access in 2026.

Noted:Demoing an early VR game in public is terrifying. But when you prioritize the right features, test relentlessly, and keep everyone aligned on the experience you're delivering — it goes smooth as butter.

The Method

How I run delivery, in practice

Interviewers always ask: “Walk me through how you'd turn around a stalled engineering team.” This is the answer — real methods, real examples, real results. Expand any line for the working detail.

2.1Discovery

Pattern-recognition interviews

My rule: interview until patterns repeat three times — that's my signal I've heard enough. After 15 user playtests, I heard “onboarding too complex” four times, so I cut two features to simplify.

ResultThe soft launch went smooth — hundreds of users engaged without major confusion.

AI-assisted research synthesis

I feed interview transcripts into ChatGPT to extract top pain points and flag repeated themes — cutting synthesis from 4 hours to 30 minutes — then validate the patterns against behavioral data.

ResultThe time savings let me run more research rounds. More iterations, better product decisions.

Competitive intelligence

I track five key competitors manually for strategic moves and use Perplexity to scan 20+ more for broader patterns. Positioning calls stay with me — they need market context AI doesn't have.

ResultSpotted a market gap early and positioned the product before competitors caught up.

2.2Planning

Impact vs. effort, with strategic weight

A standard impact/effort matrix, but weighted by “does this build our moat?” I'd rather ship platform capabilities than flashy features — choosing PostgreSQL over Solr is the canonical example.

ResultShipped on time, met requirements, and kept the option to upgrade later.

One-page decision memos

Problem, three options with tradeoffs, a recommendation, and what could go wrong. I draft the structure with ChatGPT from meeting notes, then add the strategy and business context AI can't know.

ResultFast stakeholder alignment — decisions in days, not weeks, with a clear paper trail.

Ruthless scope management

I force myself to answer: what are we NOT solving, and what's the riskiest assumption? For one high-stakes launch, that meant cutting features to nail the core experience in six weeks.

ResultThe event deadline was hit with a stable demo. Clear priorities kept the team focused under pressure.

2.3Execution

Realistic timeline estimation

I track sprint velocity manually and use AI for complex estimates from historical sprints — then add a 20% buffer, because AI doesn't know about holidays, learning curves, or scope creep.

ResultConsistent on-time delivery. Stakeholders trust the timelines, and the team isn't burned out.

Blocker-focused standups

Daily standups focus on dependencies and blockers, not status theater. AI drafts the standup summaries from Slack threads — saving 15 minutes a day — but I run the standup and unblock the teams.

ResultTeams move faster with less friction. Everyone knows who's blocking whom.

QA as partner, not gatekeeper

QA joins from sprint planning, not just at the end. I generate initial test scenarios from user stories with AI, and QA refines them — catching edge cases earlier.

ResultFewer production bugs, faster releases, and no last-minute QA scramble.

2.4Measurement

Leading indicators before lagging

I track engagement and adoption before revenue and retention — faster feedback loops. I export PostHog and Mixpanel data and use AI to correlate features with retention; then I investigate the why.

ResultProblems get spotted and fixed before they hurt revenue. Data drives decisions, not gut feel.

A/B testing with business context

AI handles the statistical interpretation of experiment data. I add what it doesn't have: strategic fit, opportunity cost, and team bandwidth.

ResultBetter go/no-go calls — not just statistically significant, but strategically smart.

Multi-channel feedback synthesis

AI extracts the top themes from 50+ support tickets in 10 minutes instead of 3 hours. I decide which themes are worth solving, based on severity, frequency, and strategic alignment.

ResultSupport tickets become product features. Users feel heard, and the team works on real problems.

2.5AI, with boundaries

Where I use it

Research synthesis, documentation drafts (PRDs, specs), data analysis, competitive intel, and test-scenario generation. Tools of choice: ChatGPT, Claude, Cursor, Perplexity.

ResultRoughly 10 hours a week saved — reinvested in strategy, user conversations, and the decisions that actually matter.

Where I don't

Strategic decisions, user conversations, prioritization calls, team leadership, and stakeholder negotiation. Humans lead humans; relationships matter.

ResultA hard boundary: AI speeds up analysis, I make the call. No AI output ships without my strategic review.

2.6 Strategic tradeoffsdocumented in the field — see Exhibits C, D, and E above.

Appendix: the operating stack

Planning
Jira · Linear · Coda · Confluence
Design & research
Figma · Miro · Pageflows · Mobbin · Usability Hub · UserCrowd · Snagit · Tool Maze
Analytics
DataDog · PostHog
AI & development
ChatGPT · Claude · Gemini · Grok · v0 · Cursor · Copilot · GitHub
Communication
Slack · Teams · Google Workspace
Automation
Zapier · n8n · Cloudflare

The Ledger

18 years, entry by entry

  1. 2022 — Present

    Synapse Studios — Senior Delivery Lead / Product Manager (Consulting)

    Embedded delivery lead across concurrent client engagements — running the standups, weekly client demos, and biweekly roadmap planning of a multi-team engineering org. Own the reporting cadence client leadership and boards use to approve feature work. Built and lead the Human-led AI Enablement practice (10,900+ hours saved across 5 industries). Took an early product from prototype to public launch in six weeks with no direct org authority.

    Consulting · cross-industry

    Tucson, AZ

  2. 2021

    Blackthorn.io — Product Manager

    Owned product direction for enterprise checkout and commerce platform features in a fast-paced startup environment.

    Ecommerce / SaaS

    Remote

  3. 2017 — 2021

    Informa Exhibitions — Product Manager, MarkitMakr Platform

    Owned delivery for a global B2B SaaS platform across 8 events on multiple continents, coordinating 5–6 stakeholder groups with conflicting requirements. Shipped enterprise media tools that powered millions in revenue.

    B2B SaaS / events

    Fort Collins, CO

  4. 2014 — 2017

    F+W Media, Inc. — Online Product Manager

    Managed a $12M revenue segment of a $65M educational content business; drove a 25% engagement improvement through iterative delivery.

    Media & publishing

    Fort Collins, CO

  5. 2013 — 2014

    Bluetent — Product Manager

    Digital agency

    Basalt, CO

  6. 2011 — 2013

    Viacom — Scrum Master / Product Coordinator

    Media

    New York City

  7. 2011

    MTV Networks — Production

    Entertainment

    New York City

  8. 2008 — 2011

    Sling Media — Associate Producer

    Entertainment tech

    Remote

  9. 2006 — 2007

    DISH Network — Marketing & Advertising Intern

    Entertainment / TV

    Englewood, CO

Education

2004 — 2008

University of Denver — BA, Digital Media Studies, Business

NCAA Division I soccer — goalkeeper and team captain. Leadership without formal authority, the same dynamic central to coordinating engineering teams.

Division I athletics

Denver, CO

Sworn Testimony

Colleagues, on the record

“He's digitally fluent in virtually all categories, but in particular in video. He learns quickly and executes flawlessly. He's also a pleasure to work with.”
— Rocky Landsverk, Marketing & Content Director
In short, Matt is a problem solver. He was excellent at getting to the essence of what customers wanted and then identifying whether current technologies could be used to meet that need or custom development was required. If development was required, Matt provided concise direction that allowed the development team to progress quickly. It would be a pleasure to work with Matt again.
— Brian YelleSenior Developer, Informa Exhibitions
It's his ability to seize opportunities and produce results, despite how many hurdles may be in the way, that makes him a pleasure to have worked with. He and I formed a great team, and we brought current, modern, and on-trend strategies to the table. I was always impressed with Matt's knowledge of the industry, its technologies, and how to leverage today's tools in innovative ways.
— Garrett EvansBrand Strategist, OTT HydroMet
His work ethic and drive to make an impact motivates all of those around him. His extensive experience in the digital space made Matt a crucial asset on the Digital team. He was a leader for those product lines and spearheaded initiatives that positively impacted the bottom line.
— Winter ThielenFounder & Digital Learning Solutions Architect

Off the Field

Character references, informal

Before I ran product orgs, I stood in goal. Division I soccer at the University of Denver — goalkeeper, team captain. The goalkeeper is the one player who sees the entire field, and the captaincy taught me the discipline I still lean on daily: organizing ten people who don't report to you, communicating constantly, and owning the outcome when everything ends up in your box.

Leadership without formal authority isn't a management theory to me. It was every Saturday for four years, and it's been the operating model for eighteen more.

Also on permanent record: a genuine soft spot for animals. All of them.

GK

Fig. 1 — The only position that sees the whole field

Matt Bredehoft
Subject: Bredehoft, M.

The Next Role

Available 2026 · conversations open now

I'm interviewing for VP and Head-of-level Product & Engineering roles — or senior delivery and platform leadership with full ownership — where I can diagnose what's blocking the team and rebuild a cadence that sticks.

What I solve

  • Delivery dysfunction — teams bogged down by too many meetings, unclear priorities, or slow shipping; I diagnose root causes and re-establish a shippable cadence.
  • Distributed team coordination — engineering work across vendors and time zones, where my leverage is trust, clarity, and results, not direct authority.
  • Systematic AI adoption — moving from sporadic individual tool usage to a measured engineering practice.

The dream fit

  • Engineering teams where delivery predictability directly drives business outcomes.
  • Teams inheriting post-acquisition chaos, scaling pain, or a stalled release cadence.
  • Leaders who want someone who owns delivery outcomes — not just roadmaps.

Open a conversation

Due diligence complete? Good. Resume available on request — just ask in the message.

Location

Tucson, Arizona — remote-first.

Core expertise

Delivery Operations · Distributed Team Leadership · AI-Enabled Engineering · Executive Reporting · Release Cadence · 0→1 Launches · B2B Platforms · Strategic Tradeoffs