Approach
Diagnosis before prescription. Always.
Most organizations don't have a technology problem—they have an evidence problem. Decisions get made on opinion, urgency, and whatever was true six months ago, and it becomes hard to say for certain what's actually working. So before I recommend anything, I go look.
The methodology below is how I do that: a repeatable sequence that separates symptoms from root causes, establishes what "good" means in measurable terms, and turns improvement into a series of testable hypotheses rather than a leap of faith. Every engagement is tailored to the team, but the underlying discipline stays the same—because the fundamentals hold, and that's why they work.
How I Lead
Leadership Operating Model
Team Shape & Sub-Team Structure
Define team shape and sub-team structure for sustainable, high-velocity engineering rhythms.
System & Process Invariants
Set system and process invariants so allocated effort compounds into reusable flywheels.
Short Feedback Loops & Automation
Short feedback loops and automation to improve decision quality, velocity, and morale.
Methodology
The 4-Phase Cadence
Each phase anchors the team to first principles—so decisions compound instead of colliding, and velocity becomes a property of the system rather than a product of heroics.
Phase 0Archaeology
Before prescribing solutions, examine reality. Look at the actual artifacts: Jira boards, Slack threads, daily standups, dev environments, deployment pipelines, and customer feedback. Ask the hard questions: Who is most frustrated? How long would a single-character code change take to hit production?
Phase 1Value Proposition
Define the actual customer problem software needs to solve. Isolate suspect processes and test assumptions against a tight cohort of real users before writing code.
Phase 2Product Mapping & Acceptance Criteria
Use event storming and journey mapping to define the true Minimum Viable Solution. Establish clean backlogs backed by rigorous acceptance criteria (Given / When / Then).
Phase 3Scorecard Rubric
Evaluate both software performance (latency, throughput, cost, observability) and operational delivery (deployment velocity, automated regression detection, product/engineering negotiation efficiency).
Phase 4Hypothesis Testing & Optimization
Formulate and run targeted technical or process experiments to improve the scorecard continuous-improvement style.
“Chris is the guy I go to for a sanity check on a business plan, architectural plans for a new application, or a difficult interpersonal issue between team members.”
Perspectives
First Principles & Philosophy
Every methodology above rests on a small set of beliefs held deliberately. They're not slogans for a slide deck—they're positions I've tested across enough teams, codebases, and crises to trust with confidence. When we work together, these principles quietly shape every recommendation I make.
Coherence Beats Optimization
Software written in a single, coherent architectural "key" outperforms fragmented, overly-optimized messes every time.
Complexity is Unfamiliarity in Disguise
Real abstraction deletes code; concretions bloat it.
Empiricism Over Confidence
Hypotheses must be tested, assumptions named, and evidence separated from opinion.
Early First-Principles Discipline
The cost of anchoring to first principles is lowest at the start—and the payoff compounds indefinitely.