Operating Model

Why this model exists

Peterson & Associates, Inc. compounds value by applying the same operating system to every asset we touch so reliability improves, automation reduces marginal cost, and controls reduce downside. This is not process theater. It is an execution model rooted in measurable reliability, operational risk discipline, and portfolio reuse.

We draw from practices proven in production-focused organizations: error-budget-based reliability governance (Google SRE), toil reduction through deliberate automation (SRE Toil), security/risk lifecycle controls (NIST SP 800-37), and management-system discipline (ISO 27001).

The Core Loop

This loop is the operating backbone used across every asset. Each stage creates reusable evidence, control, and leverage for the next cycle.

1. Source

   Underwrite opportunities where operational discipline can create durable competitive advantage.

2. Build / Acquire

   Create or acquire with clear ownership, explicit service boundaries, and measurable readiness criteria.

3. Harden

   Apply a non-negotiable baseline for controls, recoverability, and observability.

4. Operate

   Run with weekly rhythm, incident learning loops, and measurable reliability commitments.

5. Compound

   Turn operating wins into portfolio standards so each future asset stabilizes faster.

Source → Build/Acquire → Harden → Operate → Compound

Source

Objective: identify opportunities where disciplined operations create defensible leverage.

We do not collect projects; we underwrite assets. “Durable” means clear ownership, repeatable delivery, measurable reliability outcomes, and a credible path to production controls.

High-signal filters:

• Failure is expensive and reliability can be governed with explicit service objectives (SLOs & error budgets).
• Operational drag is visible and can be engineered down rather than normalized (toil reduction).
• Decision rights and operating ownership can be assigned and audited (governance clarity).

Artifacts: Asset Thesis (1 page), Risk & Control Preview, Measurement Plan.

Build / Acquire

Objective: create operable reality with clean ownership and a path to baseline controls.

This stage is speed with reversibility: small and explicit steps, clear interfaces, and early definition of what “good” looks like in production.

• Named operating owner and explicit service boundary.
• Initial SLO intent to establish measurable reliability expectations.
• Integration path into shared observability, backup, and access patterns.

Artifacts: Go-Live Checklist (v1), deployment/rollback/restore runbooks (v1), initial dependency map.

Harden

Objective: raise the asset to the durability bar: secure, observable, recoverable, supportable.

Hardening is a gate, not a vibe. We apply lifecycle risk management and continuous monitoring practices so the service can be governed over time (NIST RMF).

• ISMS discipline: establish and continuously improve security management practices (ISO 27001).
• Observability baseline: actionable metrics, logs, and alerting tied to operating outcomes.
• Recovery proof: backup completion is insufficient without restore validation.
• Incident readiness: runbooks, on-call path, and deterministic first-response procedures.

Artifacts: Durability Scorecard, Control Exceptions Register, SLO + Error Budget baseline.

Operate

Objective: deliver predictable service quality while reducing toil and downside risk.

Operations is where trust is earned. Most incidents are change-related, so release discipline is stability discipline (SRE Principles).

• SLO reporting and error-budget policy for predictable tradeoff decisions (Error Budget Policy).
• Incident management and postmortems that produce permanent operational improvements.
• Toil caps and automation-first backlog management to preserve engineering capacity.
• Continuous monitoring with explicit ownership and review cadence.

Artifacts: weekly service review, monthly durability review, quarterly operating plan.

Compound

Objective: ensure each asset makes the next one cheaper, safer, and faster to stabilize.

We compound through economies of scope: shared resources, controls, and playbooks across multiple services reduce total cost versus isolated operation (UNESCWA). We also compound through learning effects: repeat work improves speed and quality as experience accumulates (IfM Cambridge).

Artifacts: Portfolio Playbook Library, reference architectures, Compounding Backlog with measured ROI.

What success looks like

• Reliability improves without heroics; incidents get rarer and less severe.
• Toil trends down as the portfolio grows; engineering time remains available for real improvements.
• Recovery is routine and tested, not aspirational.
• Shared baselines and reusable playbooks reduce both risk and marginal operating cost.

Execution Signals

We judge this model by outcomes rather than narrative. The strongest signal is reducing incident repeat classes while delivery speed and recovery confidence both improve.

• Reliability trend: fewer severity events and tighter SLO variance quarter-over-quarter.
• Recovery confidence: restore drills pass at a predictable cadence with objective evidence.
• Toil compression: recurring manual steps are steadily retired into automation and shared playbooks.
• Capital efficiency: each new asset reaches durability faster because controls and templates are pre-built.

This is the brand promise in operational terms: steady execution, low drama, and compounding trust built from disciplined systems.